ISSN 1880-8468
Technical Report ofInternational Development Engineering࿖㓙㐿⊒Ꮏቇႎ๔TRIDE-2011- 01

February 15, 2011

Abstracts of Master Theses
Presented in February 2011

Department of International Development Engineering,
Graduate School of Science and Engineering,Tokyo Institute of Technology
http://www.ide.titech.ac.jp/TR

Preface
Master theses of Department of International Development Engineering, Tokyo Institute ofTechnology were presented successfully on August 3, 2010 and February 15, 2011. This
technical report consists of the abstracts of those theses.

Technical Report of International Development Engineering
TRIDE-2011-03
Table of Contents
Energetical consideration of the effect of a sinusoidal surface roughness on the
pin-on-disk contact processes............................................................................................................... Takashi OTAKE

1

Numerical analysis of non-axisymmetric flow between concentric spheres
using a spectral finite difference scheme.......................................................................................................................Toru SANO

5Microwave-assisted synthesis of protoberberine and its characteristic.............................................................................................................. Makoto SUZUKI

9Modeling the domestic fresh product supply chain in Bangladesh........................................................................................................................Rubel DAS

13

Analysis of synthetic cylindrical array beam-forming in presence of theelements position-error for semi-anechoic chamber evaluation...................................................................................................... Karlisa PRIANDANA

17

Preparation of activated carbons with high surface area from bagasse fly ash.......................................................................................................... Wahyu PURNOMO

21

Performance evaluation of cooperative sensing in disaster affected area............................................................................................................Santosh KHADKA

25

Process of methanol production from by-product off-gas generated in cokingof Mongolian coal.............................................................................................Jargalsaikhan ZOLJARGAL

29

Characterization of groundwater development potential in Agusan Del Norte,
Philippines.................................................................................................... Pierre Anthony JOVEN

33

Financial feasibility study of a new cement plant in Mongolia........................................................................................................ Binderiya DONDOV

37

Reasonable concession period for build-operate-and-transfer (BOT) roadprojects in the Philippines.....................................................................................................Hazel Perez PALAPUS

41

Representation of the double-directional channel by using spherical wave
expansions....................................................................................................................Kenji NAKAI

45

Effects of power sector reform in China - a case study of Liaoning province..............................................................................................................Mingyue ZHENG

49

Experimental study on thermal boundary layer along a building wall............................................................................................................Shiho ONOMURA

53

Analysis of international freight transportation characteristics of landlocked
countries.............................................................................................................. Hironori KAWAI

57

Operation and management of wind power plants under risk of failure............................................................................................................... Yoshihiro SATO

61

Adhesion between elastic beam and rigid body............................................................................................................... Yu SEKIGUCHI

65

System to make teaching materials based on collective intelligence................................................................................................................Yuhei NEGISHI

69

The characteristics of rainfall in Tokyo with detailed urban geometric data.......................................................................................................... Eriko FUKUMOTO

73

A study on the effects of seawater as mixing/curing water on the hydration
characteristics of blast-furnace slag cement............................................................................................................ Daisuke FURUYA

77

Identification of main compound in Lao medicinal plant-Kiderm.........................................................................................................Nariaki MAESHIBU

81

Wastewater treatment by adsorption with activated carbon from wood
residues in rubberwood sawmilling process...................................................................................................................Saori MITANI85

Quadratically constrained criterion based on maximum a posteriori
estimation for binary classifier............................................................................................................ Tatsuya YOKOTA

89

Selective catalytic reduction of NO over TiO2-ZrO2 supported metal catalysts.................................................................................................................... Yang WANG

93

Applicable condition of linear approximation for boost switching regulator~For charging tiny electric energy to capacitor~.....................................................................................................................Shu HUANG

97

Decomposition of nitrous oxide over rutile structure titania supported metal
catalysts.........................................................................................................................Peng GAO

101

Heavy metal adsorption by zeolite from lake sludge as raw material........................................................................................................................Yan SHAO

105

Sulfate resistance of cementitious materials with blast furnace slag, lime stone
powder and anhydrite............................................................................................................Jeong-Gook JANG

109

Evaluating infrastructure funds for transportation infrastructure projects...................................................................................................................... Lei ZHANG

113

Preparation of solid acid catalysts from bagasse fly ash.................................................................................................................... Jiaxing BIAN

117

Estimation of CO2 emission with development of rail-based intermodal freighttransportation in inland China...................................................................................................................Xiaolin FANG

121

Pattern recognition by kernel Wiener filter................................................................................................................................Rui LI

125

Assessment of operational efficiency and space allocation of low-cost terminal
in airports........................................................................................................ Batari SARASWATI

129

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Energetical consideration of the eﬀect of a sinusoidal surface roughness on the
pin-on-disk contact processes
Student Number: 08M18039

Name: Takashi OTAKESupervisor: Kunio TAKAHASHI

表面粗さのある物体の接触過程に対する系のエネルギーに基づく検討
大竹貴士
表面粗さの凝着現象に対する影響を理解することは重要である. 本研究では正弦波状表面粗さを持つ
剛体球と平面弾性体の軸対称接触モデルを考え, 系のトータルエネルギーを計算した. 外力の載荷過程と除荷過程に於いて接触半径が異なることをエネルギーカーブを用いて説明し, ファミが行った実験との比較を行った.

1

Introduction

Rigid bodyy

A surface roughness plays a signiﬁcant role in thecontrol of the adhesion force. Theoretically, Johnson has clariﬁed the adhesion process between elastic spheres and obtained the force curve , which is
the relation between the penetration distance andthe external force[1]. In experiments, Fami observed hysteresis between the penetration distanceand the contact radius [2]. Lei discuss about theeﬀect of the surface roughness on hysteresis loops
experimentally[3]. Guduru has proposed a theory
that a contact between a ﬂat elastic half-space anda rigid sphere with a sinusoidal surface roughness[4].
Guduru has obtained the force curve of the model,
and the force curve suggested there is more than one
solution of the penetration distance when the force
is applied, but the contact process isn’t clariﬁed.
In this study, total energy of an axisymmetric contact model between a rigid sphere with a sinusoidal
surface roughness and a ﬂat elastic half space is calculated , and a discussion about contact processes
from the view point of local minimums of energy
curves is carried out in order to clarify the eﬀect of
the surface roughness on the hysteresis.

2Or
Elastic body

External force
P

Contact radiusaPenetration distancehFig. 1: axisymmetric contact model with a sinusoidal
surface roughness
unit area is γ. In this study, we have nomalized eachfactors as follows.
Ū =

3

U
P
h
a
A
, P̄ =, h̄ = , ā = , Ā = ,2E ∗ λ32E ∗ λ2
λ
λ
λ
λ ′
2πγ
E
∗
λ̄ = , γ = ∗ , E =R
E R
1 − ν2

Total energy of the model

The total energy of the model is deﬁned as eq.(2).Ūe
is the elastic energy,Ūa is the work of the adhesion,Ūm is the mechanical potential energy.

Model of an axisymmetriccontact with a sinusoidal surface roughness

Ūtotal = Ūe + Ūa + Ūmwhere,

Figure. 1 shows an axisymmetric contact model between a rigid sphere with sinusoidal surface roughness and a ﬂat elastic half space. The rigid surface
proﬁle is expressed as eq.(1)[4].
(
(
))
r2
2πr
y=
+ A 1 − cos
(1)
2R
λ

∫ ā
Ūe =

P̄1
0

dh̄
P̄ 2 − P̄ 2dā − 1dā
2āŪa = −(
Ūm = −P̄

It is assumed that surface roughness is the sinusoidal which amplitude is A, and wavelength is λ.
The sphere curvature is R, Young’s modulus of theelastic half-space is E, Poisson ratio is ν, an external force is P , a contact radius is a, a penetration
distance is h, energy is U , work of the adhesion perγ ′ ā24λ̄)P¯1 − P̄
h̄ −
ā

(2)

(3)

(4)

(5)

where,
P̄1 = (2λ̄+4π 2 Ā)

1ā3 π Āā+H1 (2πā)−π 2 Āā2 H2 (2πā)32(6)Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Normalized external force −P−A=0.05−
λ =0.05
γ'=0.025

10

5

0

-5
-1

0
1
2
Normalized penetration distance −h3Fig. 3: energy curves in loading process

Fig. 2: force curve of the axisymmetric contact
model with a sinusoidal surface roughnessh̄ = λ̄ā2 + π 2 ĀāH0 (2πā)

(7)

( )n
∫ 1
2 z2
1
(
)
Hn (z) = √(1 − t2 )n− 2 sin(zt)dt (8)πΓ n + 12 0

4Energetical consideration

Guduru has obtained the relation between the external force and the penetration distance(Fig.2), but
contact processes hasn’t been clariﬁed[4]. In this section, we discuss about contact processes on the force
curve considering the local minimum of the energy
curve.(energetical consideration)

4.1

Fig. 4: force curve in loading process

Loading process

Fig.3-Fig.6 shows the loading process. The intitial
contact is S0 (Fig.3). Based on energetical consideration, it is clariﬁed that the contact radius changes
to S1 . Next, the external force changes to 2.0 from
0.0, the contact radius shift is S1 → S2 . Fig.4 shows
corresponding points(S0 → S1 → S2 ) of this loading
process on the force curve.
Next, the external force changes to 2.6 from 2.0,
the contact radius shift is S2 → S3 → S4 → S5 In
Fig.5, Fig.6. It is shown that the energy curve at S3
is not the local minimum when the external force is
2.38... . Consequently, the contact radius shift is S4
from S3 when the external force isn’t varied.

4.2

Fig. 5: dissipation of the local minimum in loading
process

is 0.39... . Consequently, the contact radius shift is
S10 from S9 when the external force isn’t varied.

Unloading process

Fig.7-Fig.10 shows the unloading process. The external force changes to 1.0 from 10.0, the contact
state shift is S6 → S8 . Fig.8 shows corresponding
points(S6 → S7 → S8 ) of this unloading process on
the force curve.
Next, the external force changes to 0.0 from 1.0,
the contact state shift is S8 → S9 → S10 → S11 In
Fig.9, Fig.10. It is shown that the energy curve at
S9 is not the local minimum when the external force

4.3

Detaching process

Fig.11,Fig.12 shows the detaching process. Whenthe external pressure changes to -5.54... , the local
minimum dissipate in Fig.11.(S13 ) Consequently, thecontact radius changes to 0.0, and the detachment
occurs. Fig.12 shows the detaching process on theforce curve.
2

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Fig. 9: dissipation of the local minimum in unloading
processFig. 6: jump of penetration distance in loading process

Fig. 10: jump of the penetration distance in unloading process
Fig. 7: energy curves in unloading process

Fig. 11: energy curves in detaching process
Fig. 8: force curve in unloading process

5
4.4

Hysteresis based on the energetical consideration

Comparison to the experimental results

Fig.14 is Fami’s experimental results about JKR
contact model , and it shows a tendency that theadhesion hysteresis diminish as the contact radiusincreased[2]. Fig.15 is the relation between the penetration distance and the contact radius based on
the energetical consideration. The curve drawn as ashort dash line is expressed as eq.(9.)√γ ′ ā22(9)hG (a) = λ̄ā + π ĀāH0 (2πā) −λ̄Based on the energetical consideration, the hysteresis of contact process between loading process
and unloading process is obtained(Fig.13). In JKRtheory(no surface roughness model), the hysteresisof contact processes doesn’t exist. So, these results
suggest a possibility that the surface roughness effects on contact processes.
3Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Penetration distance h [mm]

0.6

0.4

R=12.5mm
E=45500Pa
ν=0.5
γ'=0.0462J/m2

Loading
Unloading

0.2

1.5
2.0
2.5
Contact radius a [mm]3.0

Fig. 14: Fami’s experimental results

Fig. 12: force curve in detaching process

Loading process

Snap to contact

Unloading process

Detachment

Detachment

Fig. 15: hysteresis of the penetration distance between loading and unloading process

Fig. 13: hysteresis based on energetical consideration

6

To clarify the eﬀect of the surface roughness on
the hysteresis, we deﬁne eq.(10) as the measurementof the hysteresis. hJ is the penetration distance of
JKR contact model.
hG − hJ
π 2 ĀāH0 (2πā)√=′hJ
λ̄ā2 − γ āWe calculate total energy of the model an axisymmetric contact with a sinusoidal surface roughness,and obtain local minimums of energy curves. Basedon the energetical consideration, we clarify the contact process in the loading, unloading, and detachment process. We get the hysteresis based on theenergetical consideration, and compared with the experimental results of JKR contact model.

(10)

λ̄

,where√hJ (a) = λ̄ā −2

γ ′ ā
λ̄(11)

References

In eq.(10), H0 is the struve function, and the value
is decreased as the contact radius increased. Consequently,hG − hJ1∝hJāConclusion[1] Johnson,K.L.,Kendall,K. and Roberts,A.D.R.,”Surface energy and the contact of elastic
solids”, Proc.Roy.Soc.London 1971, A, 324,
301-313.

(12)[2] Fami,M., ”Time dependence of the adhesion
contact between elastic plane and rigid sphere”,
Bachelor Thesis(Tokyo Institute of Technology), 2008.

eq.(12) means that there is a similar tendency between experimental results and the energetic consideration.
In the experiment, There is the surface roughness
distribution exists in the direcion of rotation, but
the sinusoidal surface roughness of the axisymmetricmodel don’t achieve. So, the results doesn’t assent,
but there is a possibility that the energetic consideration of the axisymmetric model expresses the tendency of the eﬀect of the surface roughness on theadhesion contact.

[3] Lei,L., ”Experimental study on adhesion behavior between rough surfaces of solid bodies”,
Master Thesis(Tokyo Institute of Technology),
2008.
[4] Guduru,P.R., ”Detachment of a rigid sorid
from an elastic wavy surface:Theory”,
J.Mecs.Phys.Solids, 2007, Vol.55, 445-472
4

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Numerical Analysis of Non-axisymmetric Flow between Concentric Spheres
Using a Spectral Finite Difference Scheme
Student Number: 08M18080

Name: Toru SANOSupervisor: Yoshihiro MOCHIMARU

スペクトル差分法による同心二球間非軸対称自然対流の数値解析
佐野 徹
スペクトル差分法の三次元非軸対称流れへの適用性について検討を行う．三次元流れとして同心二球間自然対流を想定した．球間の流体は実質的に非圧縮性ニュートン流体で満たされ，球の中心を通る軸に対し下向きに重力が作用しているものとし，三次元定常流れを求積する．低グラスホフ数に対するある温度分布に対応する境界値問題を解く範囲では，適用は可能であることが判明した．

1.

In solving the basic equations, three-dimensionalvorticity, ω , and three-dimensional stream function, ψ ,are introduced so thatω =∇×υ,(4)

Introduction

A spectral finite difference scheme was developed by
MOCHIMARU [1]. This is a method for improving
numerical computation, where one or two independent
variables in space are separated even for the nonlinear
convective terms. Thus the number of independent
variables is reduced by one or two respectively, and thenumerical computation time is greatly reduced.
The scheme has been used mainly for
two-dimensional numerical analyses [2]. For this reason,in this study, to investigate its adaptability tothree-dimensional numerical analysis, analyzed is a
non-axisymmetric flow between concentric spheres using
the spectral finite difference scheme.

υ = ∇×ψ.

(5)

The vorticity transport equation, Eq. (6), is derived from
Eq. (2), and relationship between three-dimensionalvorticity and three-dimensional stream function, Eq. (7),is given by Eqs. (4) and (5).

µDω= − β ∇ T × g + (ω ⋅ ∇ ) υ + 0 ∇ 2 ω ,Dtρ0ω = ∇ × (∇ × ψ )= ∇ (∇ ⋅ ψ ) − ∇ 2 ψ .

2.

(6)

Numerical analysis(7)

Let Gr denote a Grashof number, which is defined as

2.1
Physical models and assumptions
Physical models are as follows: Natural convection is
generated between the concentric spheres. An analysis
domain is three-dimensional space. A spherical polar
coordinates, (r, θ , φ ), is used. Assumptions are as
follows: Fluid between the concentric spheres is assumed
to be incompressible and Newtonian. Flow is steady.

Gr =ρ 0 2 g β a 3 (Th − Tc )
µ02

,

(8)

where a is an outer sphere radius, (Th − Tc ) is the
maximum temperature difference in the fluid. Then Eqs.
(6) and (7) are non-dimensionalized using the following
reference values:• reference length: a .
2.2
Mathematical models
Under these physical models and assumptions, the basic
equations derived from the equation of continuity, theequation of motion with buoyancy under Boussinesq
approximation, and the equation of energy are expressedas∇⋅υ = 0,
(1)

ρ0

Dυ
= −∇ p + ρ 0 [ 1 − β (T − T0 )] g + µ 0 ∇ 2 υ ,Dt

ρ0 c pDT= κ ∇2 T ,Dt• reference velocity: υ 0 = µ 0 Gr ( ρ 0 a) .
• reference temperature: T0 = Th − Tc .
Thus, the following non-dimensionalized vorticity
transport equation and the relationship between
three-dimensional vorticity and three-dimensional stream
function are obtained:υφ ∂ωr∂ωr∂ωr υθ ∂ωr
+ υr
+
+
∂t
∂r
r ∂θ
r sinθ ∂φυθ ωθ + υφ ωφ∂υω ∂υ r−− ωr r − θ
r
∂r
r ∂θ
ωφ ∂υ r−r sinθ ∂φ

(2)

(3)

respectively, where υ , ρ 0 , t, p, β , T, T0, g, µ 0 , cp
and κ are velocity vector, density of fluid, time,pressure, coefficient of thermal expansion, temperature,reference temperature, gravitational acceleration vector,
viscosity of fluid, specific heat at constant pressure andthermal conductivity, respectively.

1 ∂T
1  2
2ω
2 ∂ωθ
+
 ∇ ω r − 2r − 2
r ∂φ
r
r ∂θ
Gr 
2ω cotθ2 ∂ωφ 
,
− θ2
− 2
r
r sinθ ∂φ 

=

5

(9)

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

υφ ∂ωθ∂ωθ∂ωθ υθ ∂ωθ
+ υr
+
+
∂t
∂r
r ∂θ
r sinθ ∂φυθ ω r − υφ ωφ cotθ
ω ∂υθ
∂υ
+
− ωr θ − θ
r
∂r
r ∂θ
ωφ ∂υθ
−r sinθ ∂φ=cotθ ∂ T
1  22 ∂ω r+ ∇ ωθ + 2
r ∂φ
r ∂θ
Gr 
ω
2cosθ ∂ωφ ,− 2 θ2 − 2r sin θ r sin 2θ ∂φ 

∂ωφ

2.3
Analytical solution of the energy equation
For low Gr, Eq. (3) can be approximated by a heat
conduction equation. That is, a boundary value problem
corresponding to Eq. (15) is solved.
T = r sinθ sinφ .
(15)
2.4
Fourier series expansion
In order to express the physical quantities and todecompose the basic equations into each spectral
component, the following Fourier series expansion is
used:

(10)

ω r 
 ω rcn  ω rsn 
ω 
ω 
ω 
 θ
 θ cn  θ sn ∞ ωωφ  ∞ ωφ cn φ sn   = ∑ cos n φ + ∑  sin n φ .
ψψ r  n =0  ψ rcn n =1  rsn ψθ ψθ cn ψθ sn 




 
ψ φ ψφ cn ψφ sn υφ ∂ωφ
υ ∂ωφ
+ υr
+ θ
+
∂t
∂r
r ∂θ
r sinθ ∂φ
υφ ω r + υφ ωθ cotθ∂υφ ωθ ∂υφ
+
− ωr
−
r
∂r
r ∂θ
ωφ ∂υφ−∂ωφr sinθ ∂φ=−−cosθ ∂ T∂T
1  2
 ∇ ωφ− sinθ
+
r ∂θ
∂r
Gr 

ωφ
r sin θ
2

2

+

∂ω r
2cosθ ∂ωθ ,+ 2r sinθ ∂φr sin 2θ ∂φ 22

ω rcn ,…, ψφ sn are the Fourier coefficients, which are
the function of θ , r, and t.
Substituting Eq. (16) into the basic equations (9)-(14)gives the following form:

(11)

ωθ =

∂ 2 ψφ1

r sinθ ∂θ ∂φ
2∂ 2 ψθ

1−+r 2 sin 2θ ∂φ 2
1 ∂ ψr+,r ∂ r ∂θ

ωφ =

cosθ

∂ ψφ

 ω f rcn  ω rcn  ω f rsn  ω rsn ω f θ cn ωθ cn ω f θ sn ωθ sn ω f φ cn ωφ cn ω f φ sn 
1 ωφ sn 
=

.
2 
 ψ f rcn  sin θ  ψ rcn  ψ f rsn  ψ rsn ψ f θ cn ψθ cn ψψ  f θ sn  θ sn ψ f φ cn ψφ cn ψψ 
 φ sn  f φ sn 222 ∂ ψθ ∂ ψθ
−
r ∂r
∂ r2

(13)

2
1 ∂ 2 ψ r 2 ∂ ψφ ∂ ψ φ−−r sinθ ∂φ ∂ r r ∂ r
∂ r2

−
+
+

2
1 ∂ ψφr 2 ∂θ 2
ψφr sin θ22−−cosθ ∂ ψ φ
r 2 sinθ ∂θ
cosθ

∂ ψθ
r sin θ ∂φ2

∂ 2 ψθ
.
r 2 sinθ ∂θ ∂φ
1

k =1

(17)

2.5
Introduction of new variables
In order to avoid 1 sinθ in the basic equations, thefollowing new variables except for ω rc 1 , ω rs 1 , ψ rc 1
and ψ rs 1 are introduced:

r sin θ ∂φ−

k =0Thus the spatial variable, φ , is separated.

(12)

2

∞

where Fck , Fsk are Fourier coefficients. Equation (17)gives
Fck = 0 , Fsk = 0 .
(18)

1 ∂ ψθ
cosθ ∂ ψ r
1 ∂ 2 ψr+
− 2
− 2
r ∂ r ∂θ r sinθ ∂θr ∂θ 2
∂ ψφ
∂2 ψr
1
1
− 2
+ 2
2
2
r sin θ ∂φr sinθ ∂φ2
1 ∂ ψφ+,r sinθ ∂φ ∂ r∞∑ Fck cos k φ + ∑ Fsk sin k φ = 0 ,1 ∂ ψ θ ψ θ cosθ
cosθ ∂ ψ θ+ 2+
r sinθ ∂ r
r 2 ∂θ
r sinθ

ωr =

(16)

2

(14)

6

(19)

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

For ω rc 1 , ω rs 1 , ψ rc 1 and ψ rs 1 , the following new
variables are used:ω f rc1 ω f rc1 ω f rs1  = 1  ω f rs 1  .
ψ f rc1  sinθ ψ f rc1 ψ f rs 1 ψ f rs1 2
1 ∂ ψ f θ c1c1
1 ∂ ψ f θ c1c1 ψ f θ c1c1
+
−
2
4
2r
∂r
r2
∂r
3 ψ f φ s 1c 0 1 ∂ ψ f rc1c 0 ω f θ c1c1
−
−
+
= 0,
r
4
∂r
r2

(20)

1 ∂ ω f φ s1c 0 1 ∂ω f φ s 1c 0 ω f φ s 1c 0
+
−
r
∂r
2
r2
∂r2
2 ω f rc1c 0 ω f θ c1c1−−
− Gr = 0 ,
r2
r2
2

2.6
Fourier series expansion
Furthermore, in order to express the physical quantities
and to decompose the basic equations into each spectral
component, the following Fourier series expansion isused:

1 ∂ ψ f φ s 1c 0 1 ∂ ψ f φ s 1c 0 1 ∂ ψ f rc1c 0
+
+
r
r
2
∂r
∂r
∂r2
+

ω f φ s1c 0
2Equation (28) can be decomposed into the Fourier series
expansion in φ and θ along the boundaries. That is,
No-slip conditions at an inner wall and outer wall are
given byψ f θ c1c 1+ ψ f φ s 1c 0 = 0 ,(29)

ψ f rc 1 c 0

ω f rcncm ,…, ψ f φ snsm are the Fourier coefficients, whichr

are the function of r and t.
Similarly, the spatial variable, θ , is separated.

ψ f rc 1c 0r

Governing equations

−

+

∂r
2 ω f φ s 1c 0
r2

2 ψ f rc1c 0
r2+

ψ f φ s1c 0
r2

+

(22)

3.

1 ∂ ψ f φ s 1c 0r∂r

− ω f rc1c 0 = 0 ,1 ∂ ω f θ c1c1
1 ∂ω f θ c1c1 ω f θ c1c 1
+
−
2
∂r
4
2r
∂r
r2
2 ω f rc1c 0 2 ω f φ s 1c 0+−
+ Gr = 0 ,
r2
r2

−

ψ f φ s 1c 0

2r

ψ f θ c 1c1
4r

1 ∂ ψ f φ s1c 0
=0,
2
∂r

(30)

1 ∂ ψ f θ c1c1
= 0.
∂r
4

(31)

+

−

2.9
Numerical integration
Runge-Kutta method of 4th order can be used for anumerical integration. The numerical integration iscomputed from the inner boundary toward the outerboundary. Accordingly, a steady solution is obtained by alinear combination of a special solution and an auxiliary
solution.

2 ∂ω f rc1c 0 4 ω f rc1c 0
−
r
∂r
r2
+ Gr = 0 ,+Especially, without loss of generality, at the inner wall,ψ f rc1c 0 = 0 .
(32)

In a numerical analysis, only linear terms are used in Eqs.
(9)-(14). In addition, Fourier coefficients only of cos 0φ ,cosφ , sinφ , cos0θ , cosθ and sinθ are used at theFourier series expansions. The following governing
equations (22)-(27) are used for a numerical analysis.

2

(27)

= 0.

2.8
Boundary conditions
No-slip conditions at the boundaries apply. That is, along
the boundariesυ = (υ r , υθ , υφ ) = (0, 0, 0) .
(28)

(21)

∂ 2ω f rc1c 0

(26)

2

 ω f rcn  ω f rcncm  ω f rcnsm 
ω

ω

ω

 f rsn  f rsncm  f rsnsm ω f θ cn ω f θ cncm ω f θ cnsm 






ω f θ snsm ω f θ sn ω f θ sncm ω f φ cnsm ω f φ cn ω f φ cncm 






∞
∞
ω f φ sncm ω f φ snsm ω f φ sn  cos m θ + ∑  sin m θ .
=∑
m =1  ψ f rcnsm  ψ f rcn  m=0  ψ f rcncm  ψ f rsncm  ψ f rsnsm  ψ f rsn 






ψ f θ cncm ψ f θ cnsm ψ f θ cn 
ψ

ψ

ψ

 f θ snsm  f θ sn 
 f θ sncm ψ f φ cnsm ψ f φ cn ψ f φ cncm 
ψ

ψ

ψ

 f φ sn 
 f φ sncm  f φ snsm 

2.7

(25)

Results and discussions

3.1
Numerical results
Figure 1 shows velocity vectors and temperature at y-z
plane, Gr = 1.0 . And Fig. 2 shows velocity vectors at z-x
plane, Gr = 1.0 . Likewise, Fig. 3 shows velocity vectors
and temperature at y-z plane, Gr = 0.01 . And Fig. 4
shows velocity vectors at z-x plane, Gr = 0.01 .

(23)

2

(24)

7

Technical Report of International Development Engineering

Fig. 1

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Velocity vectors and temperature
at y-z plane, Gr = 1.0 .

Fig. 4Velocity vectors at z-x plane, Gr = 0.01 .

3.2
Discussions
In Figs. 1 and 3, two circulations are shown at both sidesof y-axis. In addition, a flow is generated along the outerboundary. In Figs. 2 and 4, some larger velocity vectors
can be confirmed at both sides of x-axis.

4.

Conclusion

In order to investigate its adaptability of a spectral finite
difference scheme to a three-dimensional numericalanalysis, analyzed is a three-dimensional flow between
concentric spheres using the scheme. As a result of theanalyses, it has been found that one three-dimensionalmodel can be analyzed using the scheme. The accuracyof the simulation would be improved by taking into
account the non-liner terms and high-order Fourier
coefficients.
Fig. 2

Velocity vectors at z-x plane, Gr = 1.0 .

References
[1]

[2]

Fig. 3

Velocity vectors and temperature
at y-z plane, Gr = 0.01 .

8

Mochimaru, Y., New way for simulation oftransient natural convection heat transfer,
International Journal of Heat and Fluid Flow,
Vol.8, No.3(1987), pp.235-239.
Mochimaru, Y., A Spectral Finite Difference
Scheme and its Effectiveness, Proc. 26th Ann.
Iranian Mathematics Conference, Vol.2(1995),
pp.245-254.

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Microwave-assisted synthesis of protoberberine and its characteristic
Student Number: 08M51479

Name: Makoto Suzuki

Supervisor: Sachio Hirose,
Kiyohiko Nakasaki

マイクロ波によるベルベリン誘導体の合成とその特性に関する研究
鈴木 真
本研究では、ベルベリンの代謝物であるベルベリン誘導体を合成するため、マイクロ波を用いてベルベリンからベルベリン誘導体の合成方法を検討し、反応温度、初期 pH、反応時間の反応への影響を明らかにする。オートクレーブによる加熱方法とマイクロ波による加熱方法とを比較する。次に、様々な添加剤を添加し、添加剤による反応への影響を明らかにした。また、溶媒についても検討を行った。添加剤と溶媒を変えることにより異なるベルベリン誘導体を選択的に合成できることを明らかにした。
atmospheric pressure [8] and the characteristics of
microwave-assisted synthesis are not fully understood.
In addition, no method has been reported for thesynthesis of TLF.
The purpose of this research is to develop amethod for microwave-assisted synthesis ofprotoberberines from BER and compare to thesynthesis using autoclave. In addition, the effects
of additives and solvent are investigated.

1. Introduction
Protoberberine alkaloids have various activities suchas antimicrobial [1], anti-inflammatory [2], antimalarial[3], and anti-diabetic activities [4]. Among them,berberine (BER) has a long history of medicinal use in
traditional medicine and it has been widely
investigated. However, only few researches have beenstudied the activities of other protoberberines.
Recently, it was reported that BER was metabolized to
be protoberberines such as berberrubine (BBR),
columbamine, dehydro-discretamine, demethyleneberberine (DMB), jatrorrhizine (JAT), and thalifendine(TLF), which were observed in human urine (Fig. 1)[5]. This evidence suggested that the pharmacologicaleffect of BER could be derived from its metabolites.
However, they are too complex for cost-effective
chemical synthesis and the plants contain very small
amount of protoberberines. Therefore, it is difficult to
study the pharmacological effects of protoberberines.
Microwave energy has been widely used for heating
food materials, and recently, there has been an
increasing interest in using microwave as heat source
for chemical synthesis. Microwave-assisted synthesis
has been used for the synthesis of some chemicalcompounds, for example, for deacetylation [6], and forcross-coupling reaction [7]. In many cases, shorter
reaction time and higher selectivity were observed
comparing with conventional heating method.
However, only an experiment for producing of BBR
from BER was conducted using microwave at

O

MeO

HO

+

O

N

HO
OH

Berberrubine
322

Dehydrodiscretamine
324
OMe

HO
+

Demethyleneberberine
324

OMe

OMe

Columbamine
338

N

OMe

Jatrorrhizine
338

OH

O
+

MeO
OMe

N

MeO

OMe
HO
HO

+

+

N

+

N

O
OMe
OMe

N

OMe
Thalifendine
322

Fig.1 Metabolites of BER

OH

2. Materials and Methods
2.1 Materials
All chemicals were purchased from Wako Pure
Chemicals Co., Japan. Ultra pure water produced by
the Milli-Q Advantage vessel (Millipore, USA) wasused in this study.
2.2 Microwave-assisted synthesis
The microwave apparatus Mars-X (CEM, USA), as
shown in Fig. 2, was used for microwave-assisted
synthesis. It can be operated with an output power of9Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

up to 1600 W (2450 MHz). The temperature wasmeasured using the fiber optic temperature probe
RTP-300 Plus, and pressure was measured using the
ESP-1500 Plus.
The microwave apparatus automatically adjusted
the radiated power when the actual temperaturereached the set temperature. The experiments were
performed in a Teflon-made Green Chem vessel witha capacity of 100 mL, which could operate under atemperature of up to 180℃ and a pressure of up to
1.38 MPa (200 PSI). The initial output power was set
at 300 W. A volume of 20 mL of BER solution (2mmol / L) was put in the vessel and radiated by
microwave. The reaction vessel was cooled down for
30 min before filtration.

desorbed in methanol (20mL) for 24 h.

Fig. 3 Autoclave

2.6 Effect of solvents
A BER solution (2 mmol/L) was prepared ineach solvent. All the sample was filtered throughfilter paper No.40. The filtrate was diluted 5 times
in ultra pure water, and then filtered using a
0.45-μm membrane filter (Toyo Roshi Kaisha,Japan) for liquid chromatography mass
spectrometry (LC/MS) analysis.
2.7 LC/MS analysis
The quantification of BBR, BER, DMB, andTLF was performed using LC/MS-2010EV
apparatus (Shimadzu, Japan). The separation was
carried out in a ZORBAX Eclipse XDB column(3.5 μm, 2.1 × 150mm) (Agilent Technology,
USA). The mobile phase, which consisted of a
0.1% formic acid aqueous solution (A) andmethanol (B), was delivered at a flow rate of 0.2
mL/min using the following gradient program:
25% (B) for 0–15 min, 25–30% (B) for 15–32 min,30-70% (B) for 32–47 min 70% (B) for 47–52 min,and 25% (B) for 52–60 min. The sample injection
volume was 1 µL, and the column temperature wasmaintained at 40°C. The UV spectra were obtainedby scanning the samples in the range of 200–470
nm. The amounts of BBR, BER, DMB, and TLF
were calculated from the peak area at 345 nm.

Fig. 2 Microwave apparatus

2.3 Autoclave synthesis
The autoclave used for autoclave synthesis wasshown in Fig. 3. The temperature was measured
using thermocouple and pressure was measured bypressure gage. The autoclave automaticallyadjusted the output power when the actual
temperature reached the set temperature. The
output power was set at 100 V. A solution (20 mL)
of 2 mmol/L berberine was used for the reaction.
The reaction vessel was cooled down for 1 h
before filtration.
2.4 Effect of initial pH
The initial pH of the solution was adjusted to 3-11
using sodium hydroxide and hydrochloric acid. The
pH was measured by pH meter model F-55 (Horiba,
Japan).

3. Results and discussion
3.1 LC/MS analysis
Fig. 4 shows UV chromatogram at 345 nm of
the BER solution after radiated by microwave for
30 min. DMB , TLF , BBR , and BER wereobserved at retention time of 14.5 min, 21.4 min,29.3 min, and 29.9 min, respectively. The
compounds were determined by its retention time,

2.5 Effect of additives
Each additive (0.5g) was added into 20 mL of BER
solution. After the reaction, the solution was filteredby No. 40 (Whatman, UK). The filtered additive was10Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

mass to charge radio (m/z) and comparing withliterature.

reaction time by microwave and autoclave. The peakarea of protoberberines increased with an increase in
the reaction time. Microwave-assisted synthesis gave
faster reaction than that of autoclave. Within 90 min of
microwave radiation, BER was decreased by 75% of
initial concentration.

%

5

mAU
17.5

90
80

BER

12.5

70

10.0

60

7.5

50

DMB

BBR

-2.5

-1

Berberine concentration (mg.L )

8
911
10
13
1412

7

0.0

6

4

3

1

2.5

700

40

TLF

2

5.0

30
20
10

-5.0

0
0

10

20

30

time (min)

40

50

min

Fig. 4 UV chromatogram at 345 nm

DMB
TLF
BBR
BER

600
500

40
30

300
20

200

10

100

0
2.7

400

30

300

DMB
TLF
BBR
BER

200
100

20
10

0

0
100

120

140

150

160

170

180

9.01

11

Berberine concentration ( mg.L )

700

120

-1

600

100

500

BER
DMB
TLF
BBR

400
300

80
60
40

200

20

100
0

0
20

40

60
80
Radiation time (min)

100

120

140

Fig. 7 Effect of reaction time (MW)700
-1Berberine concentration ( mg.L )Berberine concentration (mg.L-1)

40

500

8

120

600

100

500

BER
DMB
TLF
BBR

400
300

80
60
40

200

20

100
0

0
0

Temperature (℃)

20

40
60
Radiation time (min)80

Peak area of product compounds (104 )

600

7Peak area of product compounds (104 )

50

6.02

Fig. 6 Effect of initial pH

Peak area of product compounds (10 4)

60

700

5

Initial pH

0

800

50

400

0

3.2 Effect of temperature
Fig. 5 shows the peak area of protoberberines by
microwave-assisted synthesis. DMB was notobserved at the temperature up to 120℃. The
DMB yield increased with an increase in thetemperature above 140 ℃ . The peak area of
DMB was highest at 180℃. The reaction was
performed at the temperature up to 180℃ becauseof the limit thermal-resistance of microwave vessel(200℃).

60

Peak area of product compounds (104)Intensity (mAU)

15.0

100

Fig. 5 Effect of temperature

Fig. 8 Effect of reaction time (Autoclave)

3.3 Effect of initial pH
Fig. 6 shows the effect of initial pH. DMB was
produced when the experiment conducted in theinitial pH from 2.7 to 8, but DMB peak area washighest around the initial pH 6-7. However,DMB peak area was decreased at pH 9 and was
not observed at pH 11. On the other hand, BBR
was generated at pH 11 as a major production
compound.

3.5 Effect of additives
The additive material such as cellulose and Iron
oxide could absorbed microwave and increased thesurface temperature. Thus, they may enhance thedegradation of BER and increase the amount of otherprotoberberines. Fig. 10 and Fig. 11 show the effect ofadditives by microwave and autoclave. In microwave
radiation, Carboflon and cellulose gave higher yield of
DMB compared to experiment without the additive.
Fe and Co3O4 produced BBR as main product. SiC,
Al, and Fe3O4 gave low yield of DMB. On the other
hand, DMB was not observed when using CuO, CuI,
Fe3O4, and Co. TLF was produced when using Cu2O
and CoO as the additives. From this result, only some

3.4 Effect of reaction time
Microwave heated BER solution to 180℃ in 5 min.
On the other hand, it took 30 min for the experimentby autoclave. Fig. 7 and Fig. 8 show the effect of

11

P
e
a
k
ar
e
a
of
pr
o
d
u
ct
c

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

20

u

oO

lc

lc

BER

Fig. 12 Effect of solvent (MW)

C

C

C
o
u2
O
C

C
u
Fe I
3O
4

Fe
o3
O
4
C
uO
C

C
Si

BBR

C

A
Fe l
2O
3

0

W
at
el er
lu
l
C ose
ar
bo
flo
n

0

0

TLF

Et
G

100

0

DMB

20
%

40

50

Et
G

60

200

100

pa
no
l

80

100

H

300

DMB
TLF
BBR
BER

Et
O

100

150

200

2pr
o

400

200

H

120

250

300

Et
O

140

300

400

20
%

500

350
500

at
er

160

400

W

180
600

450

600

60
%

Berberine concentration (mg.L-1)

700

200

Peak area of product compounds (10 4)

Berberine concentration (mg.L-1)

700

and 20-100% ethylene glycol gave lower yield of
protoberberines than water. When using pure
1-propanol as a solvent, the main compound was
BBR. However, for the 1-propanol aqueous
solution the peak area of BBR decreased. The
more water contents, the more DMB was
generated. Mixed solvent inhibited the reaction.
Peak area of product compounds (104)materials can increase protoberberines. The othercharacteristic of the additives rather than microwave
absorbing ability could cause the difference in theamount and molecular structure of protoberberines.
In most experiment using the additives,
microwave-assisted synthesis gave higher yields ofprotoberberines compared to those of autoclave
synthesis. However, SiC, Al, and CuI gave lower
yields of DMB.

180

600

160

500

140
120

400

100
300

80
60

200

40

100

20

TLF
BBR
BER

400

400
300
300
200

200

100

100
0

0
0

20

60

80

DMB
TLF
BBR
BER

100

Concentration (%)

u

Fig. 13 Effect of 1-propanol (MW)

C

C
Fe uI
3O
4
C
o
C
u2
O
C
oO

Fe
o3
O
4
C
uO
C

DMB

500

500

C

Fe Al
2O
3

0

el W
lu at
lo e
se r
0.
5g
Si
C

0

Berberine concentration (mg.L-1)-1

Berberine concentration ( mg.L )200

Peak area of product compounds (104)

700

600

600

Peak area of product compounds (104)Fig. 9 Effect of additive (MW)700

Fig. 10 Effect of additive (Autoclave)

4. Conclusion
This study shows that it is possible to use
microwave as a heat source for synthesis ofprotoberberines. Using additives such as Cu2O and
CoO, TLF was synthesized from BER withmicrowave radiation. BBR was produced
selectively when using Co3O4 as the additive or
1-propanol as solvent. Microwave-assisted
synthesis gave faster reaction for production of
protoberberines than that of autoclave.

700

120

600

100

500

80

400
60
300
40

200

20

100
0

Peak area of product compounds (104)Berberine concentration (mg.L-1)

Fig. 11 shows the effect of amount of cellulose on
the degradation of BER and peak area of
protoberberines. As the amount of cellulose increased,peak area of DMB was increased, while there was no
big different for the peak area of TLF and BBR. In
addition, the peak area of DMB by using 5 g cellulose
was decreased compared to 2 g.

Reference[1] Iwasa, et al..,J. Nat. Prod., 61, 1150-1153 (1998)[2] Kuo, et al.,Cancer Lett., 203, 127-137 (2004)[3] Iwasa, et al., Eur. J. Med. Chem., 34, 1077-1083(1999)[4] Ko, et al., Biol.Pharm. Bull., 28, 1431-1437(2005)[5] Feng, et al.: DMD., 36, 2159-2165 (2008)[6] Varma, R. S. et al., J. Chem. Soc. Perkin. Trans.
1, 999 (1993)[7] Kabalka. G. W, et al., Tetrahedron Lett., 41,
5151 (2000)[8] Das, B. et al., Synth. Commun., 32, 19,3027-3029 (2002)

DMB
TLF
BBR
BER

0
Water

0.5g

1g

2g

5g

Fig. 11 Effect of cellulose weight (MW)
3.6 Effect of solvents
Fig. 12 and Fig. 13 shows the effect of solvent
and 1-propanol solution on the yield of
protoberberines. the solution of 20-60% ethanol

12Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

MODELING THE DOMESTIC FRESH PRODUCTSUPPLY CHAIN IN BANGLADESH
Student number: 08M51485

Name: Rubel DAS

Supervisor: Shinya HANAOKA

This study focuses on the fresh product (such as vegetable, flower etc.) distribution system in developing country,
focusing on Bangladesh, a South Asian country. Domestic fresh-product distribution in developing country faces
difficulties from endogenous distribution system. Reasons for it can be enumerated as immature infrastructure, socioeconomic aspect, shortcoming of farming system, low information flow etc. Involvement of intermediary, who acts aslink between farmer and consumer, makes the deficiency severe. This total system creates postharvest wastage, lowbargain power of farmer, higher distribution cost, and also exposure to health risk. To overcome the endogenous
problems, supply chain redesign strategy has been adopted. Bargain power can be increased by making cooperative.
Horizontal cooperation accumulates their product and gets higher scale of transport efficiency. Poor distribution systemof vegetable in city level is one of the reasons for high price. Mathematical formulation was proposed to solve thisproblem. Service level and fixed charge facility location model were combined to make a integrated model. This model
dictates the location of distribution center and allocation of demand point. Distribution in city level was assumed to be
conducted as Cooperative Distribution System (CDS), which is cheaper than the uncoordinated distribution. Sensitivity
analysis was done to show the trade-off between service level and transportation cost.
distance from farm location) sell and another is distancemarket (usually Dhaka, Chittagong etc.).
Differences between two different option are thenumber of intermediary (is defined as the vegetableowner between farmer and retailer) and transport mode.
Unlike local distribution, distance market distribution
needs a number of intermediaries (as shown in Figure1). Those intermediaries are known as different namedepending on position oneself in vegetable distribution
chain: Faria, Bepary, Arathdar, Paikar etc (equivalentEnglish term can be found in the box beside Figure 1).

1. Introduction
Unlike developed country, developing country economy
is dependent on agriculture. Bangladesh (study area), aSouth Asian country, is an example of agriculture
dependent country, where agriculture is contributing
23% of the Gross Domestic Product (GDP) in 2006 andabsorbs more than 62% work force. Total land area
under vegetable cultivation has increasing trend.
Instead of good production, farmer still cannot getbenefit it. It is said that if transport services are
infrequent or poor quality or expensive, then farmer will
be at a disadvantage when they attempt to sell theirproduct. Addition to transport service, vegetable grower
are facing problem from perishability and seasonality
attribute of product, requirement of conditioned storage,
product variation (due to weather and biological factors)
etc.
Besides farmer’s deficiency, developing country isfacing problem from high distribution cost andconsequence is higher food price. To minimize theburden of poor consumer, developing country focus on
subsidy, price fixing, food aid program etc. But little
attention is paid in lowering the cost for food
distribution. By considering this situation, it is foundthat there is a gap where supply chain management,
which deals with the efficient product distribution withminimum cost, can be applied to fill up the gap.
Therefore, this research focused on identification oflimitation of current vegetable distribution and on
formulation of new proposed supply chain. Morespecifically, this research covers the following issues:
 Identify current vegetable distribution system andlimitation.
 Redesign the role of actor in fresh product flow.
 Formulate a mathematical model for facility location
for fresh product distribution by using integer
programming.

FarmerFaria

BeparyArathdar

Retailer

PaikarDistance Consumer

Equivalent english term:
Faria= Collector (invillage).
Bepary= Trader andtransporter.
Arathdar= Commission
agent.
Paikar= Distributor (in citylevel)

Figure 1: Relationship among intermediary
Different types of modes are being used to complete afull chain. Farmer and Faria use van. Truck is used tocarry vegetable to distance market by Bepary and CNG(a three wheel motorized vehicle), pickup, Rickshaw (athree wheel non-motorized vehicle) etc. are used forvegetable distribution in city level by Paikar andRetailer. Each transport mode has different capacity andspeed; and so different cost structure.

3. Limitation of current product flow
Small scale farmer stay always in disadvantage position
during selling vegetable. Situations compel them toproduce staple crop which is regarded as less profitable.
Besides this, there are a number of limitations in current
product flow, described below.

2. Existing distribution system
This research focuses on domestic distribution. Farmer
has two options for selling vegetable, one is local (short13

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

 Bargain power: Farmer does not have bargain power
in selling vegetable; that is required for Pareto
efficient marketing system.
 Transaction based business: Intermediary does nothave fixed investment in vegetable business; whichmakes them more flexible to leave the business. Thissituation makes the business environment with nopurposeful intention to deliver the best products.
 Quality declination: Vegetable quality deteriorates
for poor marketing system and transportation system.
Other reason for deterioration can be identified aspoor packaging and handling methods.
 Long marketing time: Marketing processing ofvegetable takes long time in existing system.
 Price difference: Price difference of same productcan be found in short distance outlet. Table 1 candescribe this situation.

chain. Proposed supply chain consists of lower numberof actor compare to existing product flow chain.
Table 2. Difference between current and proposed
supply chain
Criteria
Existing product flow New supply chain
Farmer power Limited bargain power. Has cooperative.
Transport
On spot transport
Have contract withserviceservice.
transport provider.
Flexibility
Anyone can leave
Strong commitment.
(intermediary) business any moment.
Value addition Little scope for value Increase the scope for
addition.
value addition.
City transport
Non-integrated.
Integrated.

New supply chain makes an environment of strong
commitment for providing good service to customer.
City level distribution of existing chain is done by nonintegrated way, which loses scale of economies;
Distribution in proposed model is done by cooperative
distribution system.

Table 1. Price of bean within short distance
Kawran bazar Hatirpul bazar Polashi bazar
Bean price55 tk/kg

65 tk/kg

70 tk/kg

Distance fromkawran bazar

0

1.14 km

2.7 kmTable 3. Role change of actors
Role
playerbased on survey data, 2009

4. Proposed new supply chain

Legend:FP= Food processor
DC = Distribution center
Expo= Exporter

FP

Paikar(Distributor)

Expo

Tp DC

Rt

Difficulties

Faria• Buy product• No transaction • Intermediate(Collector) from farmers
in village.
means ofand sell to• Farmer brings in transport is
Bepary.
cooperative.
required.
Bepary• Buy product • No transaction • Contract(Trader)from farmers
in production
withor Faria.
zone.
dedicated• Transport• Role change to carriers for
product todistribution
transporting
distant market. center owner.
product.

Deficiency of existing supply chain (involvement ofintermediary, inefficient transport system etc.) occurs
due to scaling problem of farmer. This scaling problem
also increases transaction cost (information cost,
negotiation cost, monitoring cost (Hobbs, 1997)).
Horizontal cooperation has potential to reduce thetransaction cost. The negotiation cost depends on market
structure; cooperation can increase the bargain power.
Monitoring cost can be decreased by aggregatingproduct in same place which is possible in cooperation.

Cooperative
Farmer Farmer

Current product Proposed supply
flow
chain

• Buy productfrom Bepary.

• Transaction• Needs city
between retailer based
and distribution transport
center.
operator.

5. Mathematical formulation
The choice of location for DC is among the most criticalstrategic decisions. Both the cost of a distribution
system and the level of service provided by the system
are significantly affected by the number, size andlocation of DC, as well as by the decision on whichcustomers to serve from each center.
Distribution system can be represented as directed
nodes and arcs. Examples of infrastructure as nodes
include retailer location, distribution center. Roads thatconnect nodes can be represented as arc.
This research solved the facility location model byintegrating two models; one is fixed charge facility
location model and another is minimized uncovered
demand.

C

Tp=Transporter
Rt=Retailer
C= Consumer

Figure 2: Proposed new supply chain
With the fulfillment of scale, cooperative get apotentiality to contract with transporter, a third party.
Transporter, supposed to optimize transport cost, isassigned for long haul starting from village. Transporter
delivers vegetable to DC. DC is designed as a store
point, from where vegetable is distributed to Rt bycooperative delivery system (CDS). CDS is successful
in the delivery environment where the sizes of deliveries
are small (Qureshi et al, 2005). Inclusion of foodprocessor and export agent, increase the scope of value
addition in vegetable.
Table (2) and Table (3) illustrate expected change ofsupply chain after implementing proposed new supply

5.1. Fixed charge facility location model
Fixed charge facility location model is formulated by
the minimization form. It makes trade-off between fixed
cost and distribution cost.

14Technical Report of International Development Engineering

min z1 =ai xi +
ii fij = 1

such that(1)

αdj cij fij

(2)
(3)
(4)
(5)

In equation (12), ‘w’ is the weight given to the term of
uncovered demand. All demand will be served but theuncovered demand will be served at a lower level ofservice. If ‘w’ is very large, the integrated model isequivalent to minimizing uncovered demand. On theother hand, for small value of ‘w’, the model turns to
fixed cost facility location model.

In this formula, fixed cost consists of rent, utilities
and labor cost. Distribution cost represents transportcost. In general transport cost can be reduced by
opening many facilities.
The objective function (equation 1) shows the tradeoff between fixed cost and distribution cost. ‘𝛼’represents transport cost per distance per unit of demand andai indicates fixed cost for opening DC at site ‘i’. ‘d j’ and‘cij’ represent demand at ‘j’ and distance of ‘j’ from site
‘i’ respectively. Equation (2) represent all demand mustbe satisfied. Another interpretation of this equation isthat one customer can accept vegetable from only one
facility.
Equation (3) indicates one facility cannot serve
customer if it is decided to shutdown. Only open facility
can serve product to customer. Again if one facility
serves at least one customer, this facility cannot be
closed. Equation (4) and equation (5) are binary
decision variables for site location (xi) and demand point
allocation (fij).

6. Data and parameter
The author had made a survey in study area, required
information were collected during this time.
Demand point location was decided after dividing
Dhaka city in 27 zones according to thana boundary(lowest administrative unit). Each zone demand wascomputed following the function of zone population anddaily consumption.

c5.2. Minimize uncovered demand
The minimal uncovered demand problem identifies ‘N’locations that minimize the amount of uncovered
demand within a distance S (threshold distance forcoverage) of at least.
min z2 =b(6)

dj q ij fij
ij

i fij =1i x i =Nfij ≤xixi 𝜖{0,1}
fij 𝜖{0,1}

Such that

K

(7)
(8)
(9)
(10)
(11)

Source: Google earth, 2010.

Figure 3: Location of four potential sites
Potential facility location (shown in figure 3) wereoutside of truck restriction area (Big truck cannot enterin Dhaka city in day time). Transport cost data, based on
survey, has been shown in table 4. Coverage factor (qij)was calculated by following equation 17(e. g. y=10 km).

5.3. Integrated model for facility location
Whereas fixed charge facility location model focuses on
cost and minimize uncovered demand focuses onservice quality; those two models have been combined
to select DC site for vegetable distribution in Dhaka.
Integrated model is as follows.

iai x i +ij

αdj cij fij +ij

wdj q ij fij

Truckboundarya

The objective function (equation 6) illustrates theminimized of cost for uncovered demand. qij is a binary
variable, it takes the value equal 1 if a facility at
candidate site ‘i’ cannot cover demand at ‘j’; otherwise
its value is zero (0). dj , fij and xi explanation are similar
to section 5.1. Equation (8) indicates that total numberof open facility is a given constraint for minimize
uncovered demand.

min z =

(13)
(14)
(15)
(16)

fij ≤ xixi 𝜖{0,1}
fij 𝜖{0,1}ij

i fij = 1
fij ≤ xixi 𝜖{0,1}
fij 𝜖{0,1}

Such that

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

𝑖𝑓 𝑐𝑖𝑗 > 𝑦 𝑡𝑕𝑒𝑛 𝑞𝑖𝑗 = 1𝑜𝑡𝑕𝑒𝑟𝑤𝑖𝑠𝑒, 𝑞𝑖𝑗 = 0(17)Road distance was collected from Google earth. To
convert to actual distance ‘circuity factor’ has beenused. It was done by following equation 18.

(12)

15

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

millions

𝑎𝑐𝑡𝑢𝑎𝑙 −𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 1.1 ∗ 𝐺𝑜𝑜𝑔𝑙𝑒 𝑒𝑎𝑟𝑡𝑕− 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 (18)

Table 4. Transport cost
Mini Truck Rickshaw /
truck
van
Distance(km) 60
200
5

Taxi

Capacity (kg) 3,200 12,00040

40

Cost (taka)

5,000 15,000

200

500

Total cost
(taka per kgkm)

0.03

1.00

0.83

Number of open DC Vs total cost

42

b

41
40

Total cost

39

15

38
37

b,a,c

a, b

36
35

0.01

0

1
2 number
3
open
DC

4

Open facility

Figure 5: Relationship of cost and open DC

7. Results and analysis
Different scenarios were considered for DC site locationand demand point allocation. In the first scenario, traffic
congestion and Dhaka transport system was ignored (allpotential location were considered). Second scenario
was made considering transportation system, (potential
site, k was omitted). It is because k is in center of Dhaka
city, which create congestion in Dhaka.

w sensitivity when tc =0.03

3
2

K, a

1

k

0

0

0.1 0.2 0.3 0.4 0.5 0.6
w value

Table 5. Model comparison

Figure 6: Sensitivity of w value, in selecting
facility location and allocation (single
sensitivity is shown).

Model 1Model 2
Objective value
30.4 million 35.9 million[taka][taka]
434,000
513,000[USD][USD]
Open facility
12Demand point allocated at ‘a’0
11Demand point allocated at ‘b’
0
16
Demand point allocated at ‘c’
00Demand point allocated to
27
0
Kawran bazar(closing K)

To reduce the congestion and for improving thetransportation system in Dhaka city, scenario two were
made. Figure 5 (mode 2 output) illustrates therelationship between cost and number of open facility. It
can be seen that in model 1 ‘b’ was not suggested toopen. It can be said that model 2 output can be applied
for improving the transport system in dhaka city, withminimizing vegetable distribution cost. Table 5 compare
the model 1 and model 2 output. It also shows the
allocation of demand point. Figure 6 shows sensitivityof ‘w’ values.

In scenario one, it can found that for small 𝛼 value,model suggest to open k. with the increase of 𝛼 value
model suggest to open new facility, it can be found in
figure 4.

8. Conclusion
Intermediaries are looting the profit share of farmer. It
is required to lower the number of intermediary forcompleting chain. Number of intermediaries can be
minimized by proposed new supply chain model.
Vegetable price in city level increase due to highdistribution cost. Cost can be reduced by CDS. To dothis DC is required. DC has been selected by following
mathematical formulation.

No of open Open facility vs Transport cost4
facility
K, a, c3

K, a2

References Hobbs, J.E. (1996). A Transaction Cost Approach toSupplyChain
Management, Supply
ChainManagement, 1(2: 15–27)
 Qureshi, A. G., Hanaoka, S. (2005). Analysis of the effects
of a cooperative deliver system in Bankok. Proceeding of
the 4th International Conference on city logistic,
Langkawi, Malaysia, 2005.

k1

𝛼 value
0
0

0.1

0.2

0.3

0.4

0.5

0.6

Figure 4: Model 1 output

16

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

ANALYSIS OF SYNTHETIC CYLINDRICAL ARRAY BEAM-FORMING IN PRESENCE OF
THE ELEMENTS POSITION-ERROR FOR SEMI-ANECHOIC CHAMBER EVALUATION
Student Number: 08M51491

Name: Karlisa Priandana

Supervisor: Jun-ichi TAKADA

電波半無影響室評価用合成円筒アレーアンテナの素子位置誤差に対するビームフォーミング
特性解析
カルリサ

プリアンダナ

本論文では、電波半無影響室の評価に供する円筒アレーアンテナの配列寸法と誤差を考慮し、低サイドローブビー
ムフォーミング設計に対する実現可能なサイドローブ特性の評価を行った。ビーム形成には Dolph-Chebyshev ア
ルゴリズムを用い、サイドローブレベルと素子位置誤差との関係を調べるためにモンテカルロシミュレーションを用いた。

Scattering Source

1. Introduction
Scattering Signal

The rapid emergence of electromagnetic (EM) equipments has forced us to carefully conduct the electromagnetic compatibility (EMC) testing, to avoid the possibilityof interference. Ideally, EMC testing is conducted in an
open-area test site (OATS), an obstacle-free environment.
However, it is inconvenient due to the high dependency on
the changing factors of the environment. As a result, EMC
test is conducted inside a semi-anechoic chamber (SAC):a room having walls and a ceiling equipped with EMabsorbers, and a metallic ground. To simulate an OATS,
SAC should be free from scattering waves other than the
ground-reflected wave. However, in reality, the imperfectabsorbers, corners, junctions, etc., may cause the SAC
discrepancy from an ideal OATS. Therefore, evaluationof SAC is required.
This thesis proposes the utilization of beam-forming
for SAC evaluation. The final goal of this research is
to design the applicable beam-forming methodology for
SAC evaluation. In this study, a synthetic cylindrical array
antenna will be used. The low side-lobe requirement for
SAC evaluation is realized by using Dolph-Chebyshev
algorithm. Monte-Carlo simulations were conducted tofind the optimal array parameters that can produce minimum side-lobe level at reasonable beam-width, under theantenna elements position-error.

Direct Signal
Receive Antenna Locations

Transmit Antenna

40 cm

Figure 1.

SVSWR Measurement Points Along 40 cm LineThe disadvantage of this method is the inability toidentify the reasons behind the SAC discrepancy froman ideal OATS. This is because the obtained output isonly the SVSWR with no other information about thesource(s) of the scattering waves. Thus, the performanceof SAC cannot be improved. Another limitation is thatthe frequency selective scattering may not be captured bythis method.

3. Beam-Forming for SAC Evaluation
This thesis proposes a new method for SAC evaluation
by beam-forming. The method offers an ability to identify
the sources of scattering signals, including the frequency
selective scattering. These advantages are superior to thestandardized Site VSWR method. This method requirea similar measurement system with that of Site VSWR
method. However, different measurement positions will
be used as a synthetic array antenna. In this method, someassumptions are taken:
1) The chamber discrepancy occurs due to some scattering waves that are caused by discrete points
inside the SAC. Each of these points is modeled
as an independent signal source, to be identified bybeam-forming.
2) The distance between receiving array antenna centerand the scattering point(s) are far enough, so thatthe plane wave approximation can be applied. The
near-field analysis will be incorporated by using thespherical-wave radiation in the future.
3) The estimated Angle-of-Arrival (AoA) informationof each detected signal path will be enough todetermine the origins of each scattering wave.
A common non-parametric approach to find the AoA of
the detected signal is beam-forming, which combines the

2. Conventional Method of SAC Evaluation
Standardized validation methods to evaluate the SAC
have been developed by CISPR/A [1]. The validation isestablished by finding the Site VSWR (SVSWR ) of the
SAC. Standing waves voltages are measured at discrete
locations of receiving antenna (Fig. 1). By moving theantenna spatially, the phase between the direct and scattering (unwanted) waves will vary. When the waves add
constructively, there is a peak response (Emax ) between
the two antennas and when the waves add destructively,
there is a minimum response (Emin ). SVSWR is the ratioof the maximum peak to the minimum peak. For SAC
of 1-18 GHz, the SVSWR at each frequency of at least
50 MHz steps should be less than 2:1 (in linear scale) or
6 dB (in logarithmic scale). When there is no reflection(ideal case), the VSWR value will be 1:1 or 0 dB.
17

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)−→ Magnitude (dB)Therefore, to avoid false alarm, the detection thresholdshould be set to a value higher than the SLL.

Figure 2.

0
−3

HPBW

SLL

Geometry of Cylindrical Array Antennasteering1

Figure 4.

Rx
Turn table

Tx

synthetic
cylindricalapertureport 1VNA

GPIB

port 2

measurement of S21

Figure 3.

steering3−→ Angle

Example of 2D Antenna Beam-Pattern

To find the AoA of the detected signals, the steering
angle should be continuously changed (beam-steering) tocover all the possible combinations of elevation and azimuth angle. The AoAs are the steering angles where thesignal(s) is detected. However, since two signals separated
by the half-power beam-width (HPBW) of the beam may
not be distinguishable due to the high possibility of beampattern error/imperfection, the steering angle steps should
be taken as the expected HPBW. In Fig. 4, HPBW is theangular width measured between the points on the mainlobe that are 3 dB below the magnitude peak. Therefore,
the AoA resolution depends on the HPBW.
After the scattering signal detection is accomplished,other related information of the scattering signal such as
the signal power, delay time, etc., can also be obtained.
Therefore, the expected results of this evaluation are the
AoA and delay time of each scattering signal (showing
the sources of SAC discrepancy), and the scattering signal
power. Note that the scattering signal relative power ishighly related to the reflection coefficient of the chamber,so that this value can be used as one of the criteria for
SAC compliance testing.

step
z-positioner

GPIB

steering2

PC

Measurement Set-Up for SAC Evaluation

received signal in such a way (by giving the appropriate
weighting and delay), so that the beam can be directed tothe desired direction. For SAC evaluation, the utilizationof a synthetic cylindrical array antenna (Fig. 2) is proposed, considering its flexibility to get the desired beampattern in elevation and azimuth plane.

3.1. Measurement and Data Analysis
The SAC evaluation consists of a data measurement
system and a data analysis (beam-forming). The measurement apparatus shown in Fig. 3 is the same with thatused in the Site VSWR method. Only a single antenna
element will be installed at each link of the vector network
analyzer (VNA). A synthetic cylindrical aperture will be
realized by moving the antenna at the receiver side, using
a turn table and a step z-positioner. At each position,
all the frequency transfer functions of interests (1 to 6
GHz with 10 MHz frequency steps) will be obtained
from VNA. However, in the data analysis, each of thefrequency transfer function will be separately analyzed.
Note that the positioning error of the turn table and step zpositioner will cause some errors in the resultant synthetic
cylindrical array due to the incorrect position of eachantenna element.
The frequency transfer functions obtained from the
VNA will be kept in a database, to be further analyzed
by beam-forming. In the analysis, each frequency will
be separately treated by using a frequency dependentinterelement spacing (decimation) and weighting. The
beam can be directed to the desired direction by choosing
the desired steering angle, which will affect the weighting
factor, as shown in Fig. 4. The received signals in the
direction of side-lobes will be attenuated by the value ofside-lobe level, |SLL|, of the beam. Our target is to beable to detect the signal(s) in the main-lobe direction only.

3.2. Requirement and Limitations
The target specification of beam-forming for this purpose is the maximum side-lobe level (MSLL) of -40
dB, to detect the weak scattering wave at the main-lobe
direction apart from the strong waves at the side-lobes
directions. To get the uniform low side-lobe beam-pattern,
Dolph-Chebyshev weighting [2] is applied. Considering
the smallest dimensions of SAC, the maximum antenna
array physical dimensions in this study are considered as
2 m in height and 1 m in width.

4. Beam-Forming Simulations
Monte Carlo simulations, each of 10000 trials, were
performed to find the mean MSLL under the influence ofGaussian antenna elements position-error. The obtained
mean MSLL at different array parameters (under thelimitation of maximum array dimensions) were used todesign the optimal parameters of synthetic cylindricalarray with minimum MSLL at reasonable half-power
beam-width (HPBW).
Table 1 shows the variables utilized in the simulation.
The investigated frequencies range from 1 to 6 GHz.
18

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

12

HPBW
µMSLL

-31

11.5

-33

11

-35

10.5

-37
0

HPBW (degree)-29µMSLL (dB)The antenna elements position-errors were assumed as
Gaussian-distributed random errors with standard deviation of 1 mm in linear, and 0.1o in angular. To get thesufficient samplings of continuous antenna aperture, themaximum interelement spacings dL and dC , were limited
to 0.5λ. Since the target specification of MSLL is -40
dB, the defined side-lobe level (DSLL) in Chebyshev
polynomials was set to be -40 dB.
Fig. 2 shows the geometry of a cylindrical array. In
the figure, θ express the co-elevation angle, the angulardeviation from the z-axis, whereas ϕ express the azimuth
angle, the angular deviation from the x-axis at xy-plane.
As can be seen from the figure, the array is composed of a
uniform circular array (UCA) of N elements and uniform
linear array (ULA) of M elements. Using the principle ofpattern multiplication, the total pattern of the cylindricalarray antenna is expressed as:A(θ, ϕ) = AUCA (θ, ϕ)AULA (θ)F (θ, ϕ),(1)where:N∑
AUCA (θ, ϕ) =wn exp(−jkrC sin θ cos(ϕ − ϕn )), (2)between elevation MSLL and dL under the influence ofantenna elements position error and their corresponding
HPBW for f = 1 GHz and 6 GHz can be seen on Fig.
5 and Fig. 6. It is shown that the minimum achievable
MSLL in f = 1 GHz is -36.8 dB (at dL = 0.5λ = 15cm, l = 1.8 m, and M = 13). In f = 6 GHz, -24.7 dBof minimum MSLL is obtained at dL = 0.5λ = 2.5 cm,
l = 0.3 m, and M = 13. The different trends of MSLL
with respect to dL in Fig. 6 occur due to the different
aperture size of the array.

100.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55

dL (λ)

Figure 5.

Elevation Performance vs. dL (f = 1 GHz, l ≃ 2 m)

-17
-18

µMSLL (dB)

Table 1. Simulation Variables
VariableValue
signal frequencyf[1, 6] GHz
std of linear error
δdL
1 mm
std of angular error
δϕ
0.1o
array heightl(M − 1)dL
array radiusrC(N dC )/(2π)
interelement spacingdL , dC≤ 0.5λdefined side-lobe level
DSLL-40 dB

-19

l=2 m

-20

l=1 m

-21

l = 0.67 m-22

l = 0.5 m-23

l = 0.33 m

-24
-25
0.1

0.2

0.3

0.4

0.5

dL (λ)

n=1′

AULA (θ) =M∑

wm exp(jkmdL cos θ),

Figure 6.

(3)m=−M ′

Elevation MSLL vs. dL (f = 6 GHz, Various l)

Based on these results, the optimal measurement sampling for the linear array at each frequency is: 13 points,
with dL ≃ 0.5λ. The sampling steps is determined by thehighest frequency of 6 GHz, dL = 2.5 cm. The total arrayheight is determined by the lowest frequency of 1 GHz,l = (M − 1)dL = 1.8 m. Thus, the linear aperture shouldbe measured by 73 points, each 2.5 cm apart.
For each frequency of f = [1, 6] GHz with 10 MHz
steps, 13 samples are taken for the data analysis (beamforming) with the corresponding linear spacings, dL ,shown in Fig. 7. Note that by implementing this scheme,
the linear aperture l is electronically varied with respect
to frequency. The expected performance in elevation forseveral frequencies of f = [1, 6] GHz are shown in
Fig. 8. It is shown that the range of elevation MSLL is[−36.8, −24.7] dB, where higher MSLL is obtained at
higher frequency, due to the linear error that are electronically larger. The elevation HPBW varied in the range of
11◦ , 20.4◦ ] due to the different electronic aperture size,l (λ). HPBW is inversely proportional to l (λ).

and F (ϕ, θ) is the antenna element pattern. In the equations, k is the wave-number of the incident wave, rC is
the UCA radius, N is the number of UCA elements,
M = 2M ′ + 1 is the number of ULA elements, dC isthe circular interelement spacing, and dL is the linear
interelement spacing. The terms wn and wm express
the weighting for the nth element of UCA and that forthe mth element of ULA respectively. Dolph-Chebyshev
algorithm was used for the weighting design, to obtain auniform low side-lobes beam-pattern with optimal beamwidth [2]. Since this algorithm is only applicable to ULA,
Davies transformation [3] was applied as the preprocessing technique to transform the UCA to a virtual ULA.
In the simulation, the investigation for elevation andazimuth beam patterns was separately conducted. The
beam pattern in elevation mainly depends on the arrayfactor of ULA, AULA (θ) (3) and antenna element pattern(assumed as ideal vertical dipole, F (θ) = sin θ), whereasthat in azimuth plane, i.e. sin θ = 1, merely depends on
the array factor of UCA, AUCA (ϕ) (2).

4.2. Uniform Circular Array
4.1. Uniform Linear Array
These simulations were conducted to observe the azimuth mean MSLL, µMSLL , in various circular interelement spacings, dC , and number of elements, N , within the
maximum physical array dimension of rC = 0.5 m. Related to the Davies transformation of UCA of N elements,

The first simulations were conducted to observe the elevation mean MSLL, µMSLL , in various linear interelement
spacings, dL , and number of elements, M , within the
maximum physical array height of l = 2 m. The relation
19

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

because one same measurement scheme is required forall measurement frequencies of f = [1, 6] GHz, and at
f = 1 GHz, maximum aperture is preferred to obtain areasonable HPBW. Thus, the azimuth MSLL in 6 GHz
can only be decreased by further decreasing NV , which
is not preferable due to the significant widening of HPBW
(proven by simulation).

Spacing (cm)

17.5
15

0.5λ

12.5

dL

10
7.5
5
2.5
0
0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

f (GHz)Figure 7.

The SAC evaluation by a synthetic cylindrical arraybeam-forming is proposed. This method offers an ability
to identify the sources of SAC discrepancy by estimating
the AoA of the detected signals. The frequency dependent
scattering signals due to the frequency dependent reflections can also be detected. These advantages are superior
to the current standardized (Site VSWR) method.
The influence of elements position-error on the performance of the array antenna was investigated by beamforming simulations. It was found that the optimal geometry of synthetic cylindrical array antenna measurement
for SAC evaluation is 1.8 m height and 1 mϕ radius, with
2.5 cm (linear) and 1 cm (circular) interelement spacings.
In the data analysis, each frequency will be separately
analyzed by using 13 linear samples with a correspondingdL (f ), and all circular samples with a corresponding
NV (f ). Utilizing this scheme, the expected array performance under the influence of Gaussian elements positionerror with standard deviation of 1 mm (linear) and 0.1o(circular) are:• Elevation MSLL = [−36.8, −24.7] dB at HPBW =[11◦ , 20.4◦ ].
• Azimuth MSLL = [−38.6, −10.3] dB at HPBW =[3.3◦ , 19.4◦ ].
The overall simulation results reveal the trade-off between
MSLL (sensitivity) and HPBW (resolution).
In the future, the fluctuation in azimuth MSLL may becompensated by taking additional samplings at a smaller
radius, to be used at the higher frequency. However, thiswill results in a longer measurement time and wideningof HPBW. The far-field assumption taken in this study
should also be justified by incorporating the sphericalwave propagation, since the relative size of the semianechoic chamber and that of the array may not satisfy
this assumption.

23

HPBW
µMSLL

-26
-28

21
19

-30

17

-32

15

-34

13

-36

11

-38
0.8

1.4

2

2.6

3.2

3.8

4.4

5

5.6

HPBW (degree)-24

mean MSLL (dB)

5. Conclusions and Future Works

Frequency vs. Linear Interelement Spacings

9
6.2

f (GHz)Figure 8.

Elevation MSLL and HPBW at f = [1, 6] GHz

care should be taken in choosing the number of virtual
linear array, NV , to avoid the effect of super-directivity:
inappropriate implementation of weighting resulting anerror-sensitive beam-pattern.
It was obtained that the minimum azimuth MSLL in
f = 1 and 6 GHz are obtained by using a UCA ofrC = 0.5 m with N = 314 and N = 215 respectively.
These two best measurement schemes are then comparedin Fig. 9. The figure shows that the azimuth MSLL
fluctuates in the range of [−38.43, −0.9] dB for N = 251,
and [−38.62, −10.26] dB for N = 314. The occurrence of
these fluctuations is related to the Davies transformation.
The range of azimuth HPBW for both parameters is[3.26◦ , 19.4◦ ]. Narrower HPBW is observed in the higher
frequency due to larger electronic aperture.
From these results, the circular aperture of rC = 0.5 m
should be measured in 314 points, each 1 cm apart. All
these samples will be used for the data analysis (beamforming) by choosing different values of virtual array NV
at each measurement frequency.
In the case of ULA, the physical height, l, can be
decreased by taking fewer samples of the total measurements. However, different from ULA, the physicalradius of UCA, rC , cannot be decreased. The reason is

HPBW

0

References

20µMSLL at N = 251 and dC = 1.25 cm
-4

18
16

-12

14

-16

12

-20

10

-24

8

-28

6

-32

4

-36

2

-40
0.8

1.4

2

2.6

3.2

3.8

4.4

5

5.6

[1]CISPR 16-1-4, “Specification for Radio Disturbance and
Immunity Measuring Apparatus and Methods – Part 1-4:
Radio Disturbance and Immunity Measuring Apparatus –Antennas and Test Sites for Radiated Disturbance Measurements”, Edition 3.0, April 2010.

HPBW (degree)

mean MSLL (dB)µMSLL at N = 314 and dC = 1 cm
-8[2]C. L. Dolph, “A Current Distribution for Broadside Arrays
which Optimizes the Relationship between Beam Widthand Sidelobe Level,” Proc. of the I.R.E. and Waves andElectrons, pp. 335–348, June 1946.

0
6.2[3]D. E. N. Davies, “A transformation between the phasing
techniques required for linear and circular aerial arrays,”
Proc. IEE, vol. 112, no. 11, pp. 2041–2045, November 1965.

f (GHz)Figure 9.

Azimuth MSLL and HPBW at N = 251 and N = 314

20

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

PREPARATION OF ACTIVATED CARBONS WITH HIGH SURFACE AREA FROMBAGASSE FLY ASH
Student Number: 08M51500

Name: Chandra Wahyu PURNOMO Supervisor: Hirofumi HINODE

  !" #$%&'()*+,-./012  (BFA)
345 6$78 9:;KOH <= ZnCl > 42?@ABCDE <= CO >42/FAB345?@AB /FABGHIJK > 4:LMNOPQRST345
UV WX2Y0Z;[ \]^_$`!Y N 78CabcdC_ef
ghSi345 FT-IR > 4j9:;6$kl78mn= 303.03 mg/g 01:;2

2

2

Before any treatment, BFA was separated into
three particle sizes by standard sieves. Aftersieving, the medium size BFA (0.7-1.4 mm)
underwent washing and floating method forseveral time using distilled water. After thistreatment, BFA was dried overnight and thenstored in a dry sealed container.

1. Introduction
Activated carbons (AC) have always become aresearch interest due to their continuous usages inmany areas. These materials have beensynthesized from many types of carbonaceous
precursors. One of the potential precursors isbiomass waste due to low price and abundantly
available. Many type of biomass such as woods[1], plant’s shells and stones [2], have beenstudied for preparing activated carbons.
Meanwhile, a sugar industry emits
approximately 5.5 kg of fly ash per metric ton ofcane processed in the flue gases from the bagasse
combustion [3]. In the case of Indonesia witharound 30 million tons of annual production ofcane, 16,500 tons of BFA per year can beproduced nationwide. This large amount ofbiomass waste should be considered to be utilized
properly. Furthermore, due to low efficiency oftypical bagasse combustors, BFA contain asignificant unburned carbon that promising to be
used as AC precursors.
In this research, BFA was used for activatedcarbon precursor. Considering its initial porosity,
it is expected that this material can be activated ina low temperature treatment and low chemical
usages.

2.2. Activation method
In this study, three activation methods have been
employed. Each of detail procedure will be
discussed in the next section. Meanwhile, sample
ID classification is provided in Table 1.
2.2.1. Physical activation
The dry pretreated BFA was weighed before
activation. Then, it was placed inside a ceramic
pipe. The internal diameter of the pipe was 3 cm
with 30 cm in length. A horizontal automatic
tubular furnace was used for heat treatment. Afterseveral minutes of nitrogen purge, the heating
was started. In this procedure 15 oC/min ofheating rate was chosen and the flow of nitrogenwas set at 150 ml/min.
For CO2 activation, after reaching the final
temperature, the nitrogen flow was replaced with
CO2 with the same flow rate. Meanwhile forsteam activation, the nitrogen flow was reduced
to 100 ml/min while the micro syringe was turned
on to introduce water into the heater for steam
generation. The flow of water was set at 0.3
ml/min.

2. Experimental
2.1 Material
BFA was obtained from PT Madukismo, a sugarcompany in Yogyakarta province, Indonesia.

21Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Table 1: AC samples nomenclature
Activation
CodeX
Physical(X)T(y)t(z)Q
C for CO2 or S
for steamChemical(X)(y)T(z)C
K for KOH or
Z for ZnCl2
Physiochemical (X)(y)T(z)CQ K for KOH or
Z for ZnCl2yz
Max. temp. in hundred holding time duration in
o
C
hours
chemical agent/BFA
Max. temp. in hundredoweight ratio (R)C
Max. temp. in hundred
chemical agent/BFAoweight ratio (R)C
2.3. Carbon characterization

The treatment during final temperature wasmaintained for 1 to 2 hours of holding time. After
that, the mixture was cooled down by natural coolingmechanism to room temperature under nitrogen flowof 150 ml/min. After reaching room temperature, thegas was closed and activated BFA was removed andweighted. The final weight was used for determining
the yield of activated carbon product. The last step
was labeling and storing the sample inside a
desiccated storage box.

The samples textural properties have beencharacterized using nitrogen adsorption at 77 K by
Autosorb 1 equipment from Quantachrome. The
chemical property analyses were done by measuring
the zero point charge (pHZPC), FT-IR (not shown inhere) and also titration method.
2.4. Liquid adsorption
Liquid adsorption equilibrium of several selected
samples has been carried out in a batch system. Eachof adsorption point was repeated in duplicate andfitted by Langmuir equation:

2.2.2. Chemical activation
The pre-dried medium size BFA before being
subjected to heat treatment, it underwent chemical
impregnation using zinc chloride or potassiumhydroxide with certain weight ratios of chemical
agent over BFA from 2 to 4. The impregnation was
done by manual mixing BFA with the chemical
concentrated solution at room temperature and thendrying in an oven at 100 oC overnight. The
impregnated BFA after weighing then placed insidea ceramic pipe and subjected to the same heat
treatment as in physical activation.
After reaching the final temperature, the heating
was hold for one hour before cooling to room
temperature. After reaching room temperature, theactivated BFA was removed from the pipe andwashed using 0.5 N HCl solution several times
followed by hot distilled water until constant pH of
distillate was reached. After washing procedure thesample was dried overnight and weighed for
calculating the product yield.

K.C

Q=Qm 1+K.C(1)where Q is the amount of phenol adsorbed (mmol/g
adsorbent), Qm is the monolayer adsorption capacity(mg adsorbate/g adsorbent), K is the affinity constant(L/mg adsorbate), and C is the equilibrium solution
concentration of adsorbate (mg/L).

3. Results and discussion
This section is grouped based on the activation
method. The sample’s physical-chemical properties
and phenol adsorption results are provided.
3.1. Physically activated carbons
3.1.1. Textural properties
Generally, CO2 activation produced activatedcarbons with dominant microporosity. Meanwhile
for steam activation, a significant development of
mesopores can be observed by large values ofmesopore volume (Table 2). Larger microporosity
for steam activation in a similar activation condition
with CO2 activation can be observed as well. The
larger pore development in steam activation has been
compensated by lower yield.

2.2.3. Physiochemical activation
This activation procedure was done for impregnated
BFA simply by switching nitrogen flow with CO2 in
the heat treatment during holding time with the same
flow rate. After activation, all samples were
subjected to similar washing and drying procedure as
in chemical activation method.

22

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Table 2: Porosity profile of physically activatedcarbonsID
Yield
porosity
(%)
Vmes
SBET
Vmic
(cc/g)(cc/g) (m2/g)
CT7t1Q
92
0.210
0.056
485
CT8t1Q
73
0.272
0.047
629
CT8t2Q
66
0.412
0.128
944
ST7t1Q
71
0.345
0.051
656
ST8t1Q
53
0.575
0.191
1,311
ST8t2Q
36
0.881
0.560
1,970

distribution pattern with ZnCl2 only activation but
with further development of narrow microporosity.
Meanwhile, in higher chemical ratio (R=3), theeffect of CO2 reduced significantly the development
in mesopore range. Furthermore, KOH-CO2
activation using R=3 also behaved in a similar way.
Table 4: Porosity profile of chemically andphysiochemically activated samples
ID
Yield
porosity
(%)
Vmic
Vmes
SBET
(cc/g)(cc/g) (m2/g)
K2T6C
87
0.569
0.055
1,306
K3T6C
56
0.644
0.102
1,441
K3T7C
51
0.960
0.093
2,180
K4T6C
57
0.942
0.057
2,158
K4T7C
48
1.135
0.189
2,571
Z2T6C
83
0.308
0.080
709
Z3T6C
81
0.417
0.157
946
Z3T7C
77
0.525
0.138
1,168
Z4T6C
82
0.378
0.125
853
Z4T7C
78
0.485
0.132
1,136
Z2T6CQ
77
0.528
0.140
1,200
Z3T6CQ
80
0.266
0.041
602
K2T6CQ
73
0.643
0.026
1,472
K3T6CQ
52
0.779
0.028
1,711

3.1.2. Surface Chemical Properties
The basic and acidic moieties have been investigatedby Boehm titration method. Then, pHZPC was also
determined and provided in Table 3. In general, thephysical activation produced basic activated carbon
with only phenolic group can be quantified by thetitration method. The steam activated samples
exhibited higher basicity than CO2 samples that wereconfirmed by lower acidity by titration method withthe highest pHZPC obtained by ST8t2Q sample at
10.76.
3.2. Chemical activated carbons

3.2.1. Textural properties

3.4. Phenol Adsorption

KOH activation produced activated carbon withsignificant development of microporosity with quite
low mesoporosity as indicated by porosity data in
Table 4. But for the highest R and temperature asignificant mesopores was also developed. By thismethod, the highest porosity among all samples canbe achieved by K4T7C sample with surface area of
2,571 m2/g.
Meanwhile, ZnCl2 samples exhibited significant
development of mesopores. Microporosity was lessdeveloped by this method compared with KOH
activation but higher yield can be obtained.

Selected activated carbon samples from above
preparations have been tested to remove phenol fromwater. Then the performances have been comparedwith a commercial activated carbon (Norit SX Plus)and non-treated BFA.
3.4.1. Physically activated sample isotherms
The isotherm adsorptions data have been provided in
Fig.1 along with the Langmuir plots. In general, theprepared activated carbon samples have better
phenol adsorption capacity especially in low phenol
concentration compare with the commercial one andnon-activated BFA.
From the Figure, it shows that steam activatedcarbon have larger capacity in adsorbing phenol
compare with CO2 activated samples. The main
factor for the capacity is the porosity profile of eachsample. As described in previous section, steam
activation provided higher porosity development
than CO2 activation. The highest monolayer phenol
uptake (Qm) for this group was obtained by ST8t1Q
sample at 270 mg/g.

3.2.2. Surface Chemical Properties
The chemical activation produced acidic activatedcarbon with carboxylic, lactonic and phenolic groupcan be quantified by the titration method aspresented in Table 3. KOH samples are more acidic
than ZnCl2 ones.
3.3. Physiochemical activated carbons

Combining the two activation methods will create
activated carbon with unique porosity characteristic(Table 4). ZnCl2 impregnation with R=2 coupledwith CO2 treatment will create similar pore23

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Table 3: Chemical properties of AC samples
Sample ID
Functional groups
Carboxylic
Phenolic Lactonic(meq/g)(meq/g)(meq/g)CT8t1Q
0.050
0.0
0.0
CT8t2Q
0.160
0.0
0.0
ST8t1Q
0.020
0.0
0.0
ST8t2Q
0.010
0.0
0.0
K3T7C
0.770
0.120
0.140
K4T6C
1.190
0.060
0.200
Z3T7C
0.230
0.040
0.140
Z4T6C
0.330
0.040
0.180
300

CT8t1Q eq
ST8t1Q eq
BFA eqAcidic(meq/g)Basic(meq/g)

pHzpc

0.050
0.160
0.020
0.010
1.030
1.450
0.410
0.550

0.570
0.770
0.940
1.110
0.240
0.160
0.150
0.220

10.17
10.26
10.31
10.76
4.69
4.24
5.71
4.74

3.4.2. Chemically activated sample isotherms

CT8t2Q eq
ST8t2Q eq
SXPlus eq

The isotherms of several chemically andphysiochemical activated carbon samples havebeen shown by Fig. 2. Similar to the previous
adsorption results, the prepared samples exhibited
higher phenol uptake than the commercial one.
The highest capacity of 303 mg/g was obtainedby K3T6C sample.

qe (mg/g)

200

100

4. Conclusions
BFA has been confirmed as a promisingprecursor for preparing ACs with tailor-able
physical and chemical properties. The preparedsample also has a superior performance for
remediating phenol in aqueous solution.

0
0

200
Ce (ppm)

400

Figure 1: Phenol isotherms of selected physically
activated carbons

References

300[1] F.C. Wu and R.L. Tseng, “Preparation ofhighly porous carbon from fir wood by
KOH etching and CO2 gasification for
adsorption of dyes and phenols fromwater”, Journal Colloid Interface Science, vol.

qe (mg/g)

200294, pp. 21–30, 2006.
[2] A. Aygun, S. Yenisoy-Karakas, and I. Duman,“Production of granular activated carbon

K3T6C eq
K4T6C eq
K3T6CQ eq
Z2T6CQ eq
SXPlus eq

100from fruit stones and nutshells andevaluation of their physical, chemical andadsorption properties” Microporous andMesoporous Materials, vol. 66, pp. 189–195,
2003.
[3] The World Bank Group, Pollution prevention
and abatement handbook 1998: toward cleaner
production, World Bank Inc., Washington
D.C., 1999.

0
0

100

200
300
Ce (ppm)

400

500

Figure 2: Phenol isotherms of selected chemically
activated carbons
24Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Performance Evaluation of Cooperative Sensing in Disaster Affected Area
Student Number: 08M51516

Name: Santosh Khadka Supervisor: Jun-ichi TAKADA

被災地における協調センシングシステムの特性評価
サントス

カドカ

大規模災害発生時には、世界中からの援助隊が狭い地域に展開してそれぞれの通信システムを使用することが予想され、同じ周波数帯を使用することによる干渉問題が発生することが過去の事例から明らかになっている。本
研究では、センサネットワークを用いて使用中の周波数帯を検出・データベース化することにより干渉問題を回避する方法を提案する。また、複数の周波数帯に対して災害発生前後におけるセンサネットワークの検出性能を比較することにより、提案手法の有効性を明らかにする。

1

Introduction

Central Database
ServerMulti-hopAd-hoc link

Spectrum sensing using energy detection have beenstudied extensively in past. Among them, the performance under fading channel has been studied by
Kostylev [1] recently. He derived the expression ofprobability of detection Pd for Rayleigh fading, Nakagami and Rician channels. Similarly Digham in [2]presented the alternative analytical approach for thesame fading channels. This work assumes the situation of a natural disaster that occurred in a major
city and many emergency rescue teams come to thescene to carry out rescue operations. But due to lackof information about the uses of frequency band, they
begin to interfere with one another after setting up
their radio communications networks. So the task at
hand is to design a wireless sensor network (WSN)that is capable of sensing the used frequency bandin the area and keep record of them. This thesis
evaluates and compare the performance of individual
sensing and cooperative sensing using energy detection method in both pre- and post-disaster channels
which is different form general fading channel.

2

Emergency
RadioSpectrumSensorCluster 2

Cluster 1

Disaster struct area

Figure 1: Cluster based sensor network
・ The GNU Radio-USRP [4] software defined radio is the targeted implementation platform forspectrum sensing.
・ Emergency rescue teams will use both analog
and digital systems and transmit between 100 MHz
and 1 GHz [5].
・ The emergency radio signals are narrowband≤ 25 KHz and prior information about theirwaveforms is not available.

Following these assumptions the objectives of thisstudy are set as follows:・ To study the effect of antenna height on received signal strength and angle of arrival (AOA)
to determine the suitable height of sensor forboth sensing and geolocation.
・ To evaluate and compare the performance ofindividual and cooperative sensing in pre andpost-disaster scenario.
・ To determine the number of sensors needed forspectrum sensing in the area considered.

Problem Statement and Objectives

After big disaster rescue teams from different states/
countries come to the scene to join rescue operation.
Due to lack of information about the uses of frequency band, two or more teams might use same
frequency band for their communication and mightcause severe interference with each other which hinders the rescue operation. Considering this, the cluster based sensor network [3] in the area destroyed byearthquake as shown in Figure 1 that can sense allchannels in the allocated emergency frequency bandand update the information in the database was realized and its performance was analyzed in this study.
In order to come up with more realistic solutions,following assumptions were made:3

Spectrum Sensing Technique

Detection of unknown deterministic signal using energy detector is a problem of binary hypothesis test.
The probabilities of detection (Pd ) and false alarm(Pfa ) in case of non-fading environment where channel amplitude gain h is assumed to be constant overmeasurement period, are given by Eq. (1) and (2)[1, 2, 6].
√p(1)
Pd = P {T > λ|H1 } = Qm ( 2γ, λ)・ The earthquake occurs in a residential area,
Ookayama, Tokyo in Japan.
・ Communication between the sensor nodes andthe emergency radio are not possible.
25

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)
Table 1: Simulation Parameters
Pulse Shape
Carrier Frequency
Bandwidth
Transmission power
Antenna Type
Polarization
Antenna gain( dBi)
VSWR
Transmiting antenna height( m)Sensing time (T)Sinusoidal
200, 400, 800 MHz
25 kHz
30 dBmOmnidirectional
Vertical
3
1
1.5
1 ms

Figure 2: 3D model of destroyed area
Pfa = P {T > λ|H0 } =Γ(m, λ/2)
Γ(m)

(2)

where Qm (., .)is the generalized Marcum Q-functionof mth order, m is equal to T W , where T is the sensing time and W is the bandwidth of the signal, γ issignal to noise ratio (SNR) and Γ(.) and Γ(., .) arecomplete and incomplete gamma functions respectively.
When h is varying due to shadowing / fading, theaverage Pd can be derived by averaging Eq. (1) overfading statistics [7],
Z
√
p
Pd =
Qm ( 2γ, λ)fγ (x)dx(3)

Figure 3: Propagation paths form Tx 1 to sensors
Ookayama area is affected by earthquake measuring
upper shindo 7 and also assuming that most of thehouses in this area were built after year 1982, following [9], earthquake affected Ookayama area was modeled by destroying almost 55% of wooden house and
20% of concrete houses using the edit tool of Wireless Insite as shown in Figure 2. Material propertiesof residential house were changed to wood. Building of Tokyo Institute of Technology and other tallbuildings taller than 15m were assumed as concretebuildings and changed accordingly.

xwhere fγ (x) is the PDF of SNR under fading. Performance of energy-detector for in different channel condition can be characterized through ROC (receiver
operating characteristics) curve.
One issue in spectrum sensing is the hidden terminal problem, which occurs when sensors are shadowed or are in severe multipath fading. This problem can be solved by cooperative spectrum sensing
where multiple sensors sense the radio environment
and make the decision cooperatively. The cooperation between sensors can be achieved either by using
soft cooperation scheme or by using hard cooperation scheme. In this study hard cooperation method
using OR rule [7] as given below was used.
Qd = 1 −Qfa = 1 −nY

(1 − Pd,i ),

(4)

n
Y

(1 − Pfa,i ),(5)Simulation and results

5.1

Simulation Parameters

The simulation parameters for channel modeling were
set in Wireless Insite following assumptions made insection 2 as shown in Table 1. The height of sensors
was determined by observing path loss and angle ofarrival at different antenna height and also considering the average height of houses. In Figure 3 it can beseen that at lower antenna height, the propagation
path follows canyon propagation but as the height increase, they follows almost line of sight (LOS) pathand this is very helpful for geolocation application.
In Figure 4 it was found that at higher sensor height,
path loss is less. Considering all these, height of thesensor was set to 15m.
520 emergency radio transmitters and 48 sensors
were uniformly distributed in the disaster affected
area. After setting all required parameter in Wireless
Insite, simulation was run for 48 hours to get therequired channel model parameters.

i=1

i=1

where Qd and Qfa are collaborative probabilities ofdetection and false alarm and i = 1, .., n is the number of collaborating sensors.

4

5

Channel modeling of earthquake affected area

This study consider a scenario destroyed by earthquake whose model can be done by using site specificchannel model like ray tracing propagation model. A
commercial software, Wireless Insite, was used forray tracing [8]. 3D model of earthquake affectedarea was imported in Wireless Insite. Assuming that

5.2

Spectrum sensing performance

The received signal power which is degraded by channel under consideration was obtained from Wireless26

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)
Table 2: Pd at 10% Pfa before and after earthquake

130

Frequency
200 (MHz)400 (MHz)800 (MHz)Path loss (dB)

120
110
100

80
70
3

1
4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20
Sensor height (m)0.95

Figure 4: Path loss versus sensor height due to Tx 1
Qd

0.9

1
After EarthquakeD

2 sensor
3 sensor
4 sensor
5 sensor
6 sensor
7 sensor

0.75

0.8
P

0.85
0.8

0.9

0.7

0.7

0.65
0

0.6
Before Earthquake

0.5
0.1

0.2

0.3

0.4

0.5
P

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Qfa

0.6

0.7

0.8

0.9

1

1

(a) At 200 MHz

0.95
0.9

1
d

0.9

0.85

Q

After Earthquake
0.8

2 sensor
3 sensor
4 sensor
5 sensor
6 sensor
7 sensor

0.8

0.7

P

0.75

0.6

0.7

0.5

Before Earthquake

0.4
0.3
0

1

(a) Before EarthquakefadAfter Earthquake
65.5% to 72%
62.2% to 69%
58.66% to 65%

sen1
sen2
sen3

90

0.4
0

Before Earthquake
55% to 62%
52.6% to 58.4%
49% to 54.8%

0.65
0
0.1

0.2

0.3

0.4

0.5
P

0.6

0.7

0.8

0.9

1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Qfa

1

fa

(b) After Earthquake(b) At 400 MHz

Figure 6: Cooperative sensing at 200 MHz
earthquake for different carrier frequency was shownin Table 2. Performance of sensors after disaster
was found to be improved by 10% for all carrier
frequency. It can be also seen from the table thatat higher frequency the performance degrades. Thisis because path loss increases with increase in frequency.

1
0.9

Pd

0.8

After Earthquake

0.7
0.6
0.5
Before Earthquake
0.4
0.3
0

0.1

0.2

0.3

0.4

0.5
Pfa

0.6

0.7

0.8

0.9

1

5.3

(c) At 800 MHz

Figure 5: Spectrum sensing performance

Cooperative spectrum sensing performance

By using the performance of those individual sensors,cooperative sensing performance changing the number of cooperating sensors were evaluated. First of
all the possible combination of 2 cooperating sensors
to 7 cooperating sensors were calculated. Then cooperative performance in terms of Qd and Qfa of all
those combination were calculated using Eq. 4 and 5
respectively and finally averaged over total numberof combination of cooperating sensors. Performanceof cooperative sensing at 3 different frequency wereshown in Figure 6, 7 and 8 for scenarios before earthquake and after earthquake.
These figures shows that the cooperation between
sensors drastically increases the performance of sensor network and also the performance after earthquake is better due to destruction of obstructing houses.
It can be also observed that as the number of cooperating sensors increases the performance also increases but there is a limit beyond which significant

Insite and the total noise power of the receiver waspractically determined to be −98 dBm for bandwidthof 25KHz in [3]. Using these datas performance of individual sensors under fading environment in termsof Pd and Pfa were evaluated.
12 out of 48 uniformly distributed sensors wereselected randomly and Pd at different value of Pfa
were calculated for 520 Tx location. Since all location suffers different fading, the average Pd at 12
sensors were calculated using Eq. 3. The whole procedure was repeated for 100 trials. ROC curve asshown in Figure 5(a), 5(b) and 5(c) for transmission frequency of 200, 400 and 800MHz respectively
for scenarios before earthquake and after earthquake
were plotted. These figures shows that as Pfa increases, Pd also increases.
Comparison of Pd at 10% Pfa before and after27

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

1after earthquake

0.95

1

0.9
PD

0.95

before earthquake

0.85

200MHz400MHz800MHz200MHz400MHz800MHz

0.9
QD

0.8

0.85

2 sensor
3 sensor
4 sensor
5 sensor
6 sensor
7 sensor

0.8
0.75
0.7
0.65
0

0.1

0.2

0.3

0.4

0.5 0.6
Qfa

0.7

0.8

0.75
0.7
2

0.9

collaboration gain cannot be absorbed with increasing number of collaborating sensors.
Cooperation gain at different carrier frequency
with increasing number of cooperating sensors wereshown in Figure 9. Considering worse sensing casewhich is at 800 MHz, 92% Qd can be obtained by
4 cooperating sensors in scenario before earthquake
whereas 3 cooperating sensors can give same performance in after earthquake scenario.

1
0.95

Qd

0.9
0.85
2 sensor
3 sensor
4 sensor
5 sensor
6 sensor
7 sensor

0.75
0.7
0.65
0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Qfa

6

Channel model of the specific area that was destroyedby earthquake was done by using site specific propagation modeling tool, Wireless Insite and the destroyed area was modeled using it’s graphic edit tool.
The height of sensors suitable for sensing and geolocation was determined. The performance of individual sensing and cooperative sensing in pre and postdisaster cases using energy detection method wereanalyzed at different emergency frequency band. It
was found that sensors performs better in post-disastercase and at lower frequency. It was also found thatcooperation between sensors increases the sensing
performance but there exist a limitation beyond whichsignificant cooperation gain cannot be achieved byincreasing number of cooperating sensors.

Figure 7: Cooperative sensing at 400 MHz

1
0.95

0.85

Q

D

0.9
2 sensor
3 sensor
4 sensor
5 sensor
6 sensor
7 sensor

0.75
0.7
0.65
0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
QReferences

1

fa

[1]

V. I. Kostylev, “Energy detection of a signal with random amplitude,” Proc. of IEEE Internation Conference on
Communications, (ICC03), pp. 35753579, May 2003.

[2]

F. F. Digham, M. S. Alouini, and M. K. Simon, “On theenergy detection of unknown signals over fading channels,”
Proc. of IEEE Internation Conference on Communications, (ICC03), pp. 35753579, May 2003.

(a) Before Earthquake

1
0.95[3] M.A. Rahman, S. Khadka, Iswandi, M. Gahadza, A. Haniz, M. Kim, J. Takada, “Development of Spectrum Sensing
System with GNU Radio and USRP to Detect Emergency
Radios”, IEICE Technical Report, SR2009-57, Oct. 2009.

Qd

0.9
0.85
2 sensor
3 sensor
4 sensor
5 sensor
6 sensor
7 sensor

0.8
0.75
0.7
0.65
0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Qfa

Conclusion

1(b) After Earthquake

0.8

7

Figure 9: Comparison of cooperation

1(a) Before Earthquake

0.8

3
4
5
6
Number of Cooperating sensors[4] Ettus Research LLC, http://www.ettus.com/[5] “Shuuhasuu-chou (Book of frequencies),” Sansai Books, vol.
176, 2007.
[6]

H. Urkowitz, “Energy detection of unknown deterministic
signals,” Proceedings of IEEE, vol. 55, pp. 523231, April
1967.

[7]

A. Ghasemi, E. S. Sousa, “Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments,” FirstIEEE Proc. of DySPAN’05, Nov. 2005.

1(b) After Earthquake

Figure 8: Cooperative sensing at 800 MHz[8] Remcom, “Wireless InSite Site-Specific Radio Propagation
Prediction Software,” User 痴 Manual, Ver. 2.0.5. 2004
[9] http://www.bousai.go.jp/jishin/chubou/shutochokka/15/
shiryou3.pdf

28

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TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

PROCESS OF METHANOL PRODUCTION FROM BY-PRODUCT OFF-GAS GENERATEDIN COKING OF MONGOLIAN COAL
Student Number: 08M51522

Name: Jargalsaikhan Zoljargal

Supervisor: Egashira Ryuichi

モンゴル産石炭のコークス化における副生ガスを原料としたメタノール製造プロセス

ジャルガルサイハン ゾルジャルガル
A Mongolian coal was carbonized in laboratory to characterize the generated off-gas.
Furthermore the readily available technologies for methanol production from coking offgas were explored. Based on these results, appropriate process flow diagram in case ofMongolia was synthesized and computational simulation and rough cost estimation werecarried out for this process. Consequently, this process was proposed for efficientutilization of Mongolian coal resources.

1. Introduction
Establishment ofcokemaking plant

the
has

The computational simulation of the process
was carried out using commercial processsimulator. The resulting dimensions ofcountry’s firstcurrently being

discussed in Mongolia. However there is lackof study on possible way of utilization of byproducts of cokemaking, namely the cokeoven gas (COG) and coal tar [1].
Conventionally the off-gas generated incoking plants has been used as fuel for heating
applications or passes out to atmosphere after
combustion causing waste of energy and air
pollution [2]. Alternatively, due to highconcentration of hydrogen and methane, theCOG has being considered as suitable sourcefor production of methanol [3]. Methanol has
appeared to be not only highly demanded
feedstock for chemical industry but also a
promising alternative fuel for transport
vehicles and gas turbine power generation [4].
In the current study a Mongolian coal was

streams, units, and energy balance were used
for cost estimation of the process.

carbonized at laboratory scale. The generated
off-gas was analyzed by gas chromatography(GC) to demonstrate its suitability formethanol synthesis. Furthermore, the available
technologies for conversion of COG tomethanol were explored, and an appropriate
process flow diagram (PDF) was synthesized.

such as Mongolian TT coal.
Furthermore, Mongolian NS coal was
carbonized at temperatures up-to 9000C. The

2. Study of Mongolian coal
The two main bituminous coal mines play animportant role in economics of the country Tavan-Tolgoi (TT) and Naryn-Sukhait (NS).
However NS coal has not been studied tosatisfactory extends, and therefore it was
selected for the case study.
The examination of available data ofprevious studies allow classifying NS coal tohigh volatile weakly coking coal that cannot
be used for cokemaking by its own (Table 1).
In coking blend NS coal might be used as highvolatile supplementary component if the basecoal with strong caking property is available,

obtained off-gas was analyzed by GC. The
carbonization apparatus consisted of electric
furnace, fixed-bed reactor equipped withthermocouple and condensation system for29

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

requirements. The tubular steam reformer waspreferred to the autothermal reformer as it
does not require expensive air separation unit
(ASU) for pure oxygen. For methanol
synthesis, low-pressure and low-temperature(LPLT) quench reactor was chosen due toeasier operation and lower cost compared to
LPLT tubular reactor, and the readiness forcommercial application compared to the liquid
phase reactor. For purification of raw
methanol, the two-column distillation waspreferred to single or multi-column distillation
as those may either result in in-sufficient
purity of methanol or higher cost and energy

removal of liquid and gaseous products(Fig.1). Table 2 shows the experimental
conditions. The yield of coke was 67w% in
average, the condensable liquid 18w% andoff-gas 15w%. The off-gas was generally
characterized by excess of hydrogen comparedto carbon oxides, which was desirable for themethanol synthesis. It also contained
considerable amount of methane which may
become a source of syngas through reforming(Fig.2). Thus, the experiments havequalitatively proved that off-gas generated incarbonization of Mongolian NS coal could besuitable for synthesis of methanol.

consumption.
The
literature
indicates
availability of gas turbines (GT) that canoperate on low calorific purge gas frommethanol synthesis after minor modification.
Heat of GT exit gas as well as other excess
process heat streams with temperatures higher
than 3000C was assumed to be converted to

3. Development of an appropriate process
flow diagram for methanol production fromcoke-oven gas
It was assumed that the cokemaking plant willproduce 1 million tons of coke per year. The
amount of COG available for methanol
synthesis
wouldbeapproximately
259,875,000 cubic meters per year [5].
Various processes for each technologicalsection of COG-to-MEOH plant were studiedcomparatively. The appropriate processes
were selected according to following
priorities: technological simplicity, lowconsumption of chemicals, less pollutants andlow cost. Selection of deep desulfurization
process within the methanol plant was closely

electricity using steam turbine with 33%
efficiency. The comparative technologicalstudy has revealed that methanol can beproduced from COG using readily available
and reliable processes.
The appropriate process flow diagram ofthe plant was developed based on theseselected processes (Fig.3) and then simulatedusing a commercial process simulator
ChemCAD III (Fig.4). The simulation has

affected by the selection of bulk
desulfurization process in the by-productrecovery cokemaking plant. Therefore
relatively extended focus was paid on COG
purification
processes.
Forbulk
desulfurization,
theconventional
absorption/stripping process was selected due
to lower consumption of chemicals and lesspollutant. Rectisol was selected for the deep
desulfurization due to reliability andindependence from consumable chemicals
although it has higher investment and energy

resulted in annual production of 104,500 tons
methanol, 206,716 MWh electricity and125,600 MWh heat for district heating. The
methanol conversion efficiency of the plant
was estimated at 43.4% and the total
efficiency at 78.2%. The methanol conversion
efficiency might be increased by improving
water removal unit after SMR and increasing
recycle ratio in the synthesis loop.
Several options were compared for further
improvement of the process. A combination ofcoal firing coke ovens and coal gasification

30

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

References[1] Ministry of Energy of Mongolia, “Current
trend of coal sector”, April 2010.
Available at: www.mmre.energy.mn

unit [2] with the hot raw COG cracking andhot gas cleaning [8] has appeared as thepromising way to improve the production
process, although it faces some technicaldifficulties to be overcome in the future. The
dehydration of methanol into DME may also
be considered as a possible improvement of
the process since DME could be blended with
LPG and thus increase the marketability of the
products. Once the methanol production plant
will be installed, the DME producing facility
can be installed with small additional
equipment cost of approximately 10% of thatof the methanol producing facility [9].

[2] Sun et al, Study on a multifunctionalenergy system producing coking heat,
Elsevier, Fuel 2009[3] Lundgren et al, Methanol production at an
integrated steel mill, 18th International
Congress of Chemical and Process
Engineering,2008, Prague, Czech Republic[4] George A. Olah, Alain Goeppert, and G.
K. Surya Prakash, “Beyond Oil and Gas:
The Methanol Economy”, 2009[5] A.Kaufman et al, “Theory and practics ofmodern coking process”, Ural State
Technical University, 2005 (in Russian)[6] N. Hamelinck, and C. Faaij, “Future
prospects for production of methanol andhydrogen from biomass” Utrecht
University, Copernicus Institute,
Department of Science, Technology andSociety, September 2001
[7] Methanol price list 2010, available atwww.methanex.com

4. Cost estimation for methanol production
from coke-oven gas
The results of computational simulation wereused for cost estimations. The capital cost of
the plant was estimated at 166 MUSD2010
using scaling factor and installation factors
available in literature. An installed cost
method was used for estimation of production
cost [6]. The sensitivity analysis has shownthat methanol might be produced at cost of 62213 USD/t or at 138 USD/t in the base case.
Compared to the current price of methanol in
Asian market at 310-350 USD/t [7], theobtained values allow concluding that
production of methanol from COG with theselected process flow diagram could be[8] Peter Diemer et al, “Potentials forutilization of COG in integrated iron andsteel works”, 2nd International meeting onironmaking, Sep.2004,
Brazil[9] Francois Bollon - Total, “International
DME standardization”, presented at 6th

economically feasible.

Asian DME conference in Seoul, 2009
Table 1: Comparison of coking property of NS
coal with typical coking coal

5. Conclusion
According to the results of this study,
combination of cokemaking plant andsynthetic methanol plant within the same
production boundary has appeared feasible
both technically and economically. Moreover
it might contribute to reduction of country’s
total dependence on imported transport fueland to the development of chemical processindustry in Mongolia.

Parameters
Volatile matter, dmmf
Reflectance of Vitrinite
FSI
weakly caking coalmoderately caking coalstrongly caking coal
RI
Sapozhnikov thicknessof plastic layer
Maximum fluidity
Fe2O3 in ash
CaO in ash
CSR (predicted)
NKK window model

31

Typ. coking coal
19-35%
1.2-1.3%

NS coal31-37%
0.5-0.84%

3
4-5
6-7
>45
14-17mm

1-6

60-75
7-11mm200-1000ddpm
<10%
<3%
>80
-

14ddpm7.8-11.8%
6.4-12.7 %
53-68
III

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Table 2: Experimental conditions
Coal sample

Mongolian coal
~65 g

Carbonization temperature

ambient ~ 900 °C

Carbonization atmosphere

nitrogenContent, w%

Fig.1: Schematic diagram of carbonization
apparatus

100.0
H2

90.0

O2
N2

80.0

CO
CH4

70.0

CO2

60.0

C2H4
C2H6

50.0
40.0
30.0
20.0
10.0
0.0
100

Temp., C
200

300

400

500

600

700

800

900

Fig.2: Change of off-gas composition from
Mongolian NS coal at various temperatures ofcarbonization

Fig.3: Process flow diagram for COG-toMethanol plant32

Fig.4: Process Flow Diagram of the COG-to-MeOH plant(Synthesized using ChemCAD III)

Mass of coal sample

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

CHARACTERIZATION OF GROUNDWATER DEVELOPMENT POTENTIAL
IN AGUSAN DEL NORTE, PHILIPPINES
Student Number: 08M51539

Name: Pierre Anthony JOVENSupervisor: Shinobu YAMAGUCHI
Jun-ichi TAKADA

フィリピンのアグサンデルノルテ州における地下水開発潜在力の特徴
ピエル

アンソニー

フベン

Optimum groundwater utilization to augment agri-industrial water demand requires understanding of local
hydrogeology. This study evaluates the groundwater development potential of Agusan del Norte, Philippines using
Groundwater Resource Potential Index (GRPI) model. Geographic Information System (GIS) was utilized to manage,
process and analyze existing data of well lithologic logs and to produce thematic maps of eight GRPI indicators. The
provincial hydrostratigraphy shows that the study area is characterized by a shallow aquifer and a confined aquifer
located at varying depths of 38-55m below ground surface. Further analysis delineated approximately 60%, 21% and
18% of the study area to have moderate, high and low groundwater development potential respectively. This study is
expected to strengthen the provincial water management strategies in ensuring agri-industrial productivity.

hydrogeology, which requires the characterization ofgroundwater development potential.

1 Introduction
Growing population, increasing urbanization andemerging agri-industrial production are the major
contributors to the unprecedented huge water demand forthe province of Agusan del Norte, Philippines. Regarded
as an under-valued resource, groundwater is one of themeans to augment the provincial water demand.
Though 96% of the world’s readily available
freshwater is trap in groundwater aquifers; sufficientknowledge, awareness and understanding of thegroundwater resources are still weak in many places
especially in developing countries.
One of thefundamental constraints of groundwater studies indeveloping countries is the lack of data and informationof groundwater resource [1].
The advent of geographical information system (GIS)
provides an opportunity of conducting cost-effectivehydrogeologic studies. While well drilling requires
significant amount of time, human and financial
resources; GIS can utilize available data such as welllithologic logs without well drilling. With GIS analysis,organizations with limited financial resources such as
local government units can play a significant role inmanaging its groundwater resource.

1.2 Objectives of the Study
This study intends to contribute to the provincial
government units’ water management strategies bycharacterizing the provincial groundwater development
potential. The specific objectives of the study include thefollowing:
1. Establish a well geographic database by gathering
all possible groundwater-related information for
Agusan del Norte.
2. Understand the provincial aquifer characteristics
by visualizing the provincial hydrostratigraphy.
3. Evaluate the groundwater development potentialof the province and discuss its implications with
policy recommendation.
4. Examine the capability of QGIS 1.4 as analternative GIS platform to ArcGIS 9.3.

2 Characterization Framework andMethodology
Groundwater development potential (GDP) is theability of an area to support present and future
groundwater demand for domestic, agriculture andindustry use. The characterization of GDP in Agusan delNorte, Philippines takes five steps: 1) data collection andrevalidation, 2) identification of groundwater resource
potential index (GRPI), 3) development of wellgeodatabase,4)
visualizationofprovincialhydrostratigraphy, and 5) analysis of groundwater
development potential.
Hydraulic conductivity (K) of soil sample in the welllithologic log was utilized to differentiate thehydrostratigraphic units [3]. Subsequently, a model wasformulated to evaluate the GDP. The GRPI model iscomposed of eight indicators, categorized in three subindices.
Geographic information system (GIS),
particularly ArcGIS 9.3, was utilized to manage, process
and analyze existing data of well lithologic logs and to

1.1 Problem Statement and Significance ofStudy
The province of Agusan del Norte has inadequate
access to safe and potable water, including irrigation.
Only about 70% of the households are connected to pipewater distribution system and only 15% of the farmlands
are covered by an irrigation system [2]. People makenecessary means to tap any available water source to havesufficient water supply in their households andbusinesses. Consequently, random well drilling andunregulated groundwater extraction to augment domestic,
agricultural and industrial water demand eventually result
to unproductive wells and deterioration of aquifer
characteristics. Thus, in order to optimize groundwater
utilization, it is necessary to understand the provincial

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produce thematic maps of the eight GRPI indicators
namely: 1) depth of confined aquifer, 2) thickness ofconfined aquifer, 3) static water level, 4) land use, 5) soil
type, 6) slope, 7) effective rainfall and 8) rice consumptive
use. The GRPI indicators and sub-indices were integratedusing weighted multi-criteria overlay analysis to generate
the GRPI map. Finally, the GRPI map was classified into
five classes to create the GDP map. The GDP map
delineated the study area as: 1) very high, 2) high, 3)
moderate, 4) low, and 5) very low GDP.

2.2.2 Local Seminar-Workshop
On January 2010, the researcher organized aprovincial seminar-workshop, which was participated by
local government planners and groundwater professionals.
Specific objectives of the local seminar-workshop were: 1)
to present the research proposal; 2) to revalidate collected
data; 3) to rationalize the proposed GRPI indicators andindices; and 4) to discuss sustainability issues of theresearch outputs.

2.3 Groundwater Resource Potential Index(GRPI) Model

2.1 Study Area
The study area comprises mainly of the Agusan Valley(Fig. 1). It is characterize by a low gradient alluvial
floodplain with recent geology and lies between thelongitudes from 125o 12’ 50.4” to 125o 39’ 21.6” andlatitudes from 8o 42’ 18.0” to 9o 27’ 39.6”. The study
area has a total geographical area of about 1,121 km2,
which occupies about 40% of the province of Agusan delNorte. This areal extent constitutes 76% of all thesettlement area wherein 95% of rice is being produced.

A model was developed for integrating the GDP
indicators and sub-indices into a value called
groundwater resource potential index (GRPI). GRPI is acombined statistic used to describe and differentiate areasby the level of GDP.
The GRPI model was rationalized and justified in a
local seminar-workshop (Table 1). During the workshop,
the proposed indicators and sub-indices were defined andtheir relationship relative to GDP was discussed. Then,worksheets were disseminated wherein each participant
provided weight to the indicators and sub-indices basedon their experiences and perception of the factors
affecting groundwater availability, rechargeability anddemand. Also, participants provided their justification
behind the relative weights given to each indicator and
sub-index.
Table 1. The GRPI Model

Figure 1. The study area (created by the author, April
2010)

GPRI Sub-indices and Indicators

Weight (%)

A. Availability sub-index
1. Depth of confined aquifer
2. Thickness of confined aquifer
3. Static water level
B. Rechargeability sub-index
4. Land Use
5. Soil type
6. Slope
7. Effective rainfall
C. Consumptive use sub-index
8. Rice consumptive use

55
37
35
28
32
36
26
20
18
13
100

Source: Consolidated by the author, February 2010

2.2 Data Collection
A fieldwork in the Philippines was conducted in
December 2009 until January 2010. The main objectivesof the fieldwork were: 1) to gather existing data of welllithologic logs, 2) to revalidate gathered data, and 3) torationalize the GRPI indicators and sub-indices.

Subsequently, all the responses were consolidated andthe average weight of each indicator and sub-index wascomputed to create the GRPI model. The GDP was
evaluated based on the GRPI model.
The GRPI sub-indices are: 1) availability, 2)
rechargeability, and 3) consumptive use. The sub-index
value is the summation of all indicators under each subindex category multiplied by its respective indicator’s
weight, as shown in the model. This model utilized
weighted multi-criteria overlay analysis.
Literature
supports that this analysis has been adopted recently ingroundwater availability and groundwater recharge
studies [4]. Similarly, GRPI was calculated by thesummation of the three sub-index value multiplied by its
respective sub-index weight. Clustering of indicators into
categories or sub-index can be effective in analyzing thestrengths and weaknesses of a certain area or locality.

2.2.1 Identification of Relevant Data
The researcher visited multiple national and local
government agencies in Metro Manila and Agusan delNorte, Philippines to gather existing data. Relevant datacollected include: lithologic logs or borehole logs ofwells, geologic formations, soil type, land use, slope,
rainfall, static water level, seismic faults, and riceproduction volume. The researcher had a consultation
with local well professionals to discuss about theperceived location of deep confined aquifer within theprovince.

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Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

the entire study area agreed with the previous study thatmost arable land in the Philippines is overlain with ashallow aquifer [5]. Second, there has been discussion
among local professional well drillers that the area is
exemplified with a deep confined aquifer. However, thisstudy is the first to visualize its existence, supporting theargument of local professionals.
Thethree-dimensional
cartography ofthehydrostratigraphic layer was limited to a depth of 70m
because most wells have lithologic log data until thisdepth. Although some wells have lithologic logs up to
150 meters, especially wells owned by water utility
service providers, analysis results would be unreliable
due to limited number of data points.
Understanding the provincial hydrostratigraphy will
enable households and other groundwater users todetermine the feasible location of wells, design well
specifications and estimate construction cost of wells.

3 Data Analysis and Findings
3.1 Well Geodatabase
In order to create a significant well geodatabase, twoessential attributes of a database were examined. These
are the reliability and content validity.
1) Reliability of the thematic layers. Reliability of thethematic layers in the well geodatabase is consistent withthe data source. The vector and raster features classes in
the well geodatabase are the same vector and raster data
used by the Department of Environment and Natural
Resources (DENR) and the Provincial Local Governmentof Agusan del Norte.
2) Content validity of tabular data. Content validity ofgeneral well database is based on the accuracy of data.
Tabular data of general well database came from theauthorized Philippine government agencies to collect andpublish their specific data of concern and thus, all tabular
data in the well geodatabase are considered authentic andaccurate.

3.3 Groundwater Development Potential
Groundwater development potential (GDP) describes
the ability of an area to support groundwater demand fordomestic, agricultural and industrial use. GRPI value of
5 means that the area has abundant presence ofgroundwater and it requires minimal amount of power topump groundwater whereas, GRPI value of 1 means that
it is not feasible to extract groundwater in the area.
Results showed that the lowest GRPI value is 1.7979
while the highest GRPI value is 4.5768. A large area of
Butuan City has relatively low GRPI value whereas mostareas of the municipalities of Magallanes and Tubay haverelatively high GRPI value.

3.2 Provincial Hydrostratigraphy
The provincial hydrostratigraphy was visualized using
three-dimensional cartography in ArcGIS 9.3 ArcScene
application. The provincial hydrogeology shows thefollowing major findings. First, the entire study area ischaracterized by a shallow aquifer, which is defined aswater bearing formation of depth less than 20m. Second,
the analysis found that another water bearing formationof deep confined aquifer is located below the shallow
aquifer with depths ranging from 38 to 55 meters below
the ground surface (Fig. 2). This can be interpreted thatthe shallow aquifer could be a satisfactory source of
irrigation water whereas the deep confined aquifer is thesuitable source of potable water.

Figure 2. The provincial hydrostratigraphy (created by
the author, April 2010)

Figure 3. The groundwater development potential map(created by the author, June 2010)

These findings are particularly significant on thefollowing two contexts. First, the presence of a shallow
aquifer concurred with the previous reliable study of
rapid assessment of water supply sources conducted by
the National Water Resource Board in 1982. Thisextensive study using GIS further identified larger scaleof shallow aquifer. The presence of shallow aquifer in

In order to illustrate the GDP of Agusan del Norte,
GRPI map was classified into five categories.
This
resulted in the GDP map (Fig. 3). The GDP map
delineated approximately 60%, 21% and 18% of thestudy area to have moderate, high and low groundwater
development potential respectively (Table 2).

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Technical Report of International Development Engineering

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Limiting the density of deep wells would stabilize pumpflows and enable constant rate of groundwater recharge.
Extensive rehabilitation of brush lands, grasslands andlogged-over areas should be considered as a provincial
policy. All LGUs in the province are encouraged toincrease investment in reforestation projects such as biofuels plantation in brush lands and watershedreforestation projects.

Table 2. GRPI value range and area of each GDP class.
GRPI
value range
4.2001 – 5.0000

Description
Very highArea in %of total area
0.38

Area,
Km2
4.23

3.4001 – 4.2000

High

21.22

237.862.6001 – 3.4000

Moderate

60.29

675.911.8001 – 2.6000

Low

18.07

202.531.0000 – 1.8000

Very low

0.04

0.46

5 Evaluation of QGIS 1.4 as an
Alternative GIS Platform

Source: Summarized by the author, June 2010
The coastal zones of the municipalities of Jabonga,
Nasipit and Carmen; and the entire area of Magallanes
have high GDP. These areas can be considered aspriority area for establishing agri-industrial and special
economic zone. The southern part of Butuan City has
low GDP, which can be inferred that the locality has
difficulty of supporting current and future groundwater
demand.
The municipalities of Kitcharao, Santiago, Buenavista
and northern part of Butuan City have moderate GDP.
This can be interpreted that the locality has the means ofsupport current groundwater demand. However, unlike
areas with high GDP, the ability of these localities tosupport future groundwater demand is uncertain. To
ensure that the locality can augment future groundwater
demand, groundwater management strategies must be
implemented by the concerned local government unit.

One of the constraints of GIS implement in local
government units is the cost of commercial GIS software.
To overcome this constraint; evaluation QGIS 1.4, which
is a free and open source software for GIS, was pursued.
This motivation would also avoid the illegal use ofcommercial GIS software.
QGIS 1.4 capability was evaluated by analyzing the
groundwater development potential.
After detailed
investigation, it was recognized that QGIS 1.4 performs
very well with vector and raster datasets through fTools,
GRASS and other plugins. QGIS 1.4 is capable ofperforming standard geoprocessing and spatial analysis,
with outputs comparable to ArcGIS. It can be inferredthat QGIS 1.4 has a great potential to be deployed in local
government units of Agusan del Norte, Philippines.

4 Policy Recommendations

Reliable lithologic logs from various government
agencies were identified in order to conduct a valid data
analysis. The provincial hydrostratigraphy confirms thatthe study area is characterized by a shallow aquifer aswell as deep confined aquifer. The GRPI model provided
a holistic and comprehensive approach in evaluating the
groundwater development potential of Agusan del Norte,
Philippines. Further analysis delineated about 60%, 21%
and 18% of the study area to have moderate, high and
low GDP respectively.
These specific findings can assist the provincial
government’s decision to pursue water resources
planning for sustainable groundwater utilization. Further,
it is envisioned that this research will strengthening theprovincial water management strategies.

6 Conclusions

After careful considerations of the analyses andfindings including the discussion during the local
seminar-workshop, four over-arching policy themes
persistently arises.
These are: 1) prioritization ofgroundwater for potable water use; 2) improved
efficiency of groundwater utilization; 3) regulation ofgroundwater pumping; and 4) rehabilitation of brushlands, grasslands and logged-over areas.
Groundwater for potable water use in households
should be prioritized over agricultural and industrial use
purposes. All local government units (LGUs) can widen
agricultural irrigation coverage by taking advantage ofthe presence of shallow aquifer in their respective locality
through shallow tube well irrigation. As much as
possible, deep confined aquifer should be utilized
exclusively for drinking water in households.
With high groundwater availability, the municipalities
of Magallanes, Remedios T. Romualdez, Tubay and
Cabadbaran City should be the key administration units
to establish efficient groundwater utilization mechanism.
LGUs should devise a system to store or divert surplus
water coming from free-flowing wells. Establishment oflocal water utility service provider in these municipalities
is urgently needed. This not only improves water use
efficiency but also promotes equitable distribution andwider access to safe and affordable freshwater.
The municipalities of Kitcharao, Santiago, Buenavista
and Butuan City have scarce groundwater availability.
Groundwater pumping should be regulated in these
localities. One approach of regulating groundwater
pumping is limiting the density of deep wells in the area.

References
[1]

[2]

[3]

[4]

[5]

36

M.K. Jha, A. Chowdhury, V.M. Chowdary, and S. Peiffer,“Groundwater management and development by integrated
remote sensing and geographical information systems: Prospects
and constraints,” Water Resource Management, vol. 21, pp. 427467, 2007.
PGAN, Provincial Profile-Agusan del Norte. [Online], Available:
http://www.agusandelnorte.gov.ph/index.php?option=com_conte
nt&task=view&id=20&Itemid=84. (accessed on February 12,2010).
K. O. Thomsen, C. Pokorny, and J. Agnoletti, “Groundwater
resource planning – defining the shallow aquifer system,” Proc.
26th Annual ESRI Education User’s Conference, 2006.
V. Madrucci, F. Taioli, and C. C. Araujo, C.C., “Groundwater
favorability map using GIS multicriteria data analysis on
crystalline terrain, Sao Paulo State, Brazil,”
Journal of
Hydrology, vol. 357, pp. 153-173, 2008.
W. David, “Constraints, opportunities and options in irrigation
development,” Philippine Institute of Developmental Studies,
Discussion Paper Series No. 2000-39, 2000.

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

FINANCIAL FEASIBILITY STUDY OF A NEW CEMENT PLANT
IN MONGOLIA
Student number: 08M51551

Name: Binderiya DONDOVSupervisor: Shinya HANAOKA

モンゴルにおける新セメントプラント建設の財務的実行可能性分析
ドンドブ

ビンデリア

In order to successfully implement the project, financial feasibility study along with risk analysis is conducted forpotential investment. The source of the project cash inflows is revenue from its sales. Therefore using the past 9
years time series observations on the total consumption of the country, the future years demand is forecasted
applying Holt’s linear exponential smoothing technique. To see if the project is worth more than it costs, both NPV
and IRR are also estimated. Stochastic risk analysis is conducted based on the discounted cash flow modeling todetermine the magnitude of consequence of important parameters like inflation rate and sales volume etc. to the total
value of the project. The outputs illustrate that the cement plant project is worth undertaking an investment.

1

the annual net cash flows, both project owner andinvestor have to insure what is the probability thatNet Present Value of the project reaches less thanzero, what are the most influential economic factors
to the total value of the project and risks that theymust bear. On the other hand, question of whether
market is capable of growing in the future is vital for
project’s revenue generation from its product sales.
In order to answer these questions, the thesis aims todetermine and analyze 1) Forecast of future cement
demand derived from quantitative time series
analysis 2) Estimation of annual cash flows, Net
Present Value and Internal Rate of Return and 3)
Risk analysis and modeling of uncertainty associatedwith future cash flows and NPV (Monte Carlosimulation using @Risk).

Introduction

The transition to a market economy and aseries of severe winters (called ‘Zhud’) in Mongolia
have resulted in the large scale migration of lowincome families into the gerr areas 1 of Ulaanbaatar(UB) city [1]. Clearer policy directions, such as the“Compact City” concept of the UB city Master Plan
2030, have emerged in recent years to control spatial
expansion and promote high-density development for
the gerr areas [2]. Mongolia, on the other hand,
imports approximately 70 percent of total cement
consumption as the domestic cement producers are
not being able to supply fast growing consumption
needs. The residential market of Ulaanbaatar andprovincial centers has consistently grown over thelast few years, with supply of construction materials
very much struggling to meet the high level ofdemand. With growing cement demand not only in
Mongolia but in adjoining Siberian region as well,investments in new cement production capacity will
yield good returns [3]. Thus it is vital to establish a
new cement production facility to supply the growing
needs to enhance economic activity as well as
alleviate foreign trade balance deficit.
Hence the thesis is aimed at analyzing
financial feasibility of a potential cement production
facility for private investment when limited sourcesfrom the Government constrain the country. The
most critical question is whether the revenue driven
from the proposed project is worth more thaninvestment made by the projects stakeholders. Given

2

Proposed project

The new cement plant project owner is oneof Mongolian private entities operating business inconstruction materials trade as well as mining
exploration, extraction and prospecting works. It
plans to implement processing facility to produce 1
million tons of Traditional Portland cement productper annum.
The cement plant location is planned to bewithin the area of ‘Biluut’ limestone deposit in the
Dalanjargalan soum of the Dornogobi province inMongolia. The deposit is located about 17km northwest from 25th railway cross-roads in Dalanjargalansoum, 320km south-east from Ulaanbaatar city and
15km from Choir - Sainshand earth road. The ‘Biluut’deposit has approx. 30 million tons of limestone
reserve as to be accessible to electricity, built

1

The word ‘gerr’ refers to the felt tents used traditionally by
Mongolian herdsmen. In this context, the ‘gerr areas’ refers
to the periurban areas surrounding Ulaanbaatar and otherprovincial centers occupied by informal settlers.

37

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

infrastructure and auxiliary materials supply. The
construction phase is estimated to be 2 years andoperational phase will commence in 2013.The figure
1 below shows the location of the proposed project.

management then needs to make any adjustments toreflect current changes in the marketplace [4]. Thus
the following algorithm (Holt’s linear exponential
smoothing) is applied based on the above yearly timeseries to generate future years’ forecasts.

‘Biluut’ deposit =  + 1 +  

estimate = − ++ 1 −of the trend
where 0 ≤ α, β ≤ 1, St – the smoothed, Bt – the best estimate

 =  +  =  +   , where m - number of period ahead

to be forecasted

The smoothing parameter, α is optimized
using Solver function on Excel spreadsheet and foundto be 0.4 whereas trend smoothing constant, β is used
as 0.2. The forecasted values up to 2015 as an
example are shown together with actual consumption
plots in the Figure 2 below. As a consequence ofDouble exponential smoothing method (Holt’s linear),the forecasted values maintain monotonous rising
trend in the upcoming years up to 2030. The demandof 2013, the first year of operation is estimated to be
1,149.0 thousand tons.
Another approach is utilized based on thequarterly time series. The forecasted values based on
the quarterly time series do exhibit the rising trend as
well and generate much closer data to the forecasts
based on yearly time series. Quarterly time series
reveals seasonal variation due to ceased constructions
during winter period. Thus seasonal factor isestimated in order to measure how the period(quarter) compares to the overall average for an entire
year. Hence seasonal effects are removed using
estimated seasonal factors to generate forecasts
applying exponential smoothing method. Since
revenue from sales is a critical factor for the project
to be viable, the yearly time series generated
forecasts are market volume used as an uncertain
parameter for Monte Carlo simulation in thestochastic risk analysis.

Figure1. Location of ‘Biluut’ deposit
The two domestic producers: ‘Erel’ and‘Khutul’ cement plants struggle to meet the present
demand of 1 million tons per annum. The two cement
plants produce approx. 100,000 tons and 170,000
tons of cement per annum, respectively.

3

Future Demand Forecasting

The main source of project cash inflows is
the revenue from its sales. Thus it is important toanswer to the question of whether the market iscapable of growing in the future. Therefore using thepast 9 years time series observations on the total
consumption of the country, the future years demandis forecasted applying Holt’s Linear Exponential
smoothing technique. The total annual consumptionof cement in 2001-2009 is shown in Table 1. Due tothe Lehman shock hit the world market, Mongolia’s
cement consumption which increased gradually overpast years fell dramatically in 2009.
Table 1. Annual consumption
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009

Consumption, ‘000 tons
94.8
200.2
253.2
275.7
415.0
450.7
595.0
978.7
673.1

Consumption, '000 tons (yi)
1,500
1,000

Actualvalue

500
DoubleFortunately, when historical sales data are
available, some proven statistical forecasting
methods have been developed for using these data toforecast future demand. Such a method assumes that
historical trends will continue, so a company

2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015

0

38

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

interest rate. Then applying the formula of NPV, we
have:

Figure 2. Scatter plot of forecasted values up to 2015



4

Financial evaluation

The economic viability of the project
depends on the adequacy of the cash flows generated
as compared to the cash flows that must be expended.
Thus discounted cash flow (DCF) analysis is crucial
in determining the economic viability of a proposed
project and the adequacy of the rates of return thatproviders of capital to the project can expect torealize. Projecting the cash outflows and inflows is a
critical part of the DCF analysis [5]. The proposed
cement plant project’s total cost was estimated on abasis of sound assumptions and current market data.
Subsequently the project’s total annual operating
costs as well as capital expenditures are computed
based on the Turnkey-Lump sum Engineering
Procurement Construction contract establishedbetween the project owner and International
Consulting firm.

= $# 153, 6 )**)+,

Internal Rate of Return
Another method, Internal Rate of Return(IRR) for evaluating the proposed project is utilized.
It is commonly referred as the capital investment
project’s expected rate of return. The IRR for the
project is the discount rate that makes the NPV
equals zero:


 !







0=
=  + .
1 + -.. 1 + -.. !

Thus we have IRR = 16% > 9.9%. As a result,
expected rate of return, IRR > r.

4.2
4.1

 !



 = = 
1 +  
1.099 

Discounted cash flow analysis

Risk analysis of financial forecasting

Both the project owner and investor have toinsure what is the probability that Net Present Valueof the project reaches less than they expected andwhat are the most influential economic factors to thetotal value of the project. Monte Carlo simulation
using @Risk is applied in order to model theuncertainty associated with the future cash flows
estimated and answer to the above questions. In
Monte Carlo simulation, uncertain inputs in a model
are represented using ranges of possible values
known as probability distributions. Probability
distributions are much more realistic way ofdescribing uncertainty in variables of a risk analysis.
In business and government, one faces having to
make decisions all the time where the outcome isuncertain. Understanding the uncertainty can help us
a much better decision [6].
Thus discounted cash flow model isdeveloped from the results of section 4.1 anduncertainty is described assigning probability
distributions of input random variables: unit cost,
inflation rate, no. of competitors, sales price, market
volume, sales volume and overhead costs.
Output variables from the Monte Carlosimulation using @Risk are Net Present Value and
Net Income during operational phase up to 2030. As
a result, the average NPV equals $US 118.6 million.
The probability that NPV<0 is about 2.5% as
illustrated in the following figure 3. Monte Carlosimulation is ran using @Risk with 10,000 iterations– the spreadsheet is calculated repeatedly with a set

Thus the total operating annual cash cost is
estimated to be $US 20,122,753 which comprises ofall direct and indirect costs, such as raw materials
purchase, utilities (electricity, water), maintenancecosts, transportation costs, wages and welfare of theplant employees, mining license fee, royalty andother costs.
The total capital cost is estimated to be $US
101,713,129 which includes investment costs forcivil engineering, erection as well as plant machineryand equipment. In addition to the total annual cash
outflows as well as total capital costs, revenue fromthe products sales is computed based on theforecasted values of demand and current market price.
Consequently, the annual revenue from sales is
estimated to be $US 83 million. Finally, net operating
cash flows are estimated on an annual basis.
%et Present Value
The NPV of the proposed investment projectis the present value of all of the after-tax cash flows(CF) connected with the project – all its costs andrevenues, from now and the operational phases up to
year 2030. The decision rule to follow: Undertake an
investment to the project if NPV is positive (NPV>0).
Previously in the thesis, it is assumed that the
project’s cost of capital; r is 9.9 percent on a basis ofInternational Finance Corporation long-term loan

39

Technical Report of International Development Engineeringof new possible values sampled
distributions and each iteration.

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

from

each

5

The project appears to generate significant
economic return under sound assumptions. It is
expected to contribute significantly to the economic
development of Mongolia. Government of Mongolia
aims to develop more and more infrastructure
projects such that there is a necessity to ensuresmooth supply of raw materials, in particular cement
product for the those projects.
As a result of forecasting of future cement
consumption in the country using Holt’ linear
exponential smoothing technique, it is estimated thatthe market volume will increase with monotonous
rising trend in the upcoming years.
The DCF analysis involved estimating theamount of the initial investment, projection of aftertax-cash flows, estimating cost of capital, and thenusing the NPV and IRR method to determine whether
the project is worth more than it will cost.
Financial forecasting model was developed
for stochastic risk analysis to appraise the uncertainty
attached to the project. If the project owner andinvestors are strongly risk averse, the project is theone to undertake an investment though the riskincreases over time along with increase in the Net
Income. There is a very low, i.e. 2.5% chance thatNet Present Value will be negative. Thus the result
illustrates that the project is expected to yield highexpected future cash flows.

Resulting NPV (9.9%)
59.4
1.2

186.0

5.0%

90.0%

5.0%

1.0

0.8
NPV (9.9%)
Minimum
Maximum
Mean
Std Dev
Values0.6

0.4-$1251646.3669$267425647.1987$118600780.3415$38366772.8145
10000

0.2

0.0

NPV in $US
Values in Millions ($)

Figure 3. Histogram distribution of resulting NPV

30

25

20

15

10

5

0

-5

-10

-15

@Risk Tornado graph – Regression mappedvalues shown in Figure 4 below displays the mostinfluential factors affecting the NPV that are sales
price of the year 2013 and no. of competitors entering
the market in 2016 followed by unit cost of year 2013.

-20

Conclusions

Figure 4. Tornado graph – regression mapped values

References
No. of competitor that enters the market is 2 at
maximum and 1 at minimum values in the Uniform
distribution assigned in the simulation. Though it isassumed there will be 3 plants in total over time toshare the market, the Net income is expected to growgradually, as a result. The summary trend graphbelow shows increasing risk over a range of outputs:
Net Income/2013 to Net Income 2030. As the band
gets wider, it illustrates increasing uncertainty in the
Net income estimates over the years.

[1] World Bank, Gerr area development report, Mongolia:
Enhancing Policies and Practices for Gerr area
Development in Ulaanbaatar, March 2010
[2] Asian Development Bank, Mongolia: Towards a
Coherent Local Development Framework report, March,
2007[3] Asian Development Bank, Mongolia: Private Sector
Assessment report, April, 2009[4] Hillier, F.S., and Lieberman, G.J., (2001) Introduction
to Operations Research, Seventh edition, The McGraw-Hill
Companies, Inc., USA

Net Income / 2013 to Net Income / 2030
100
80
60

Mean

[5] Finnerty, J.D., (2007) Project Financing, Asset based
Financial Engineering, Second edition, John Wiley & Sons
Inc., USA+/- 1 Std. Dev.

40

5% - 95%
200-20[6] Vose, D., (2009) Risk Analysis – A Quantitative guide,
Third edition, John Wiley & Sons Ltd. England

Figure 5. Summary trend graph40

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

REASONABLE CONCESSION PERIOD FOR BUILD-OPERATE-AND-TRANSFER (BOT)ROAD PROJECTS IN THE PHILIPPINES
Student Number: 08M51568

Name: Hazel Perez PALAPUS

Supervisor: Shinya HANAOKA

This research provides a methodology to determine the reasonable concession period that would be advantageousboth to the government and the private sector with the effect of risks taken into consideration in the financial evaluation
using Monte Carlo simulation and bargaining game theory. To demonstrate the applicability of the proposed
methodology, two Build-Operate-and-Transfer (BOT) road projects in the Philippines were used as case studies. The
results revealed that the resulting concession period of the case studies should have been longer than the actual
concession period granted to the private sector indicating the impact of risks in the cash flow. With the proposed
methodology, the government could further enhance its policies in processing BOT projects with the end in view ofincreasing private sector participation in infrastructure development.

1 Introduction
Concession period starts from the signing of theconcession agreement between the government and theprivate sector indicating the span of time within which theprivate sector is responsible for the construction phase andoperation phase of the Build-Operate-and-Transfer (BOT)
project. In the Philippines, the length of concession period
is usually decided by the infrastructure implementing/headagency. Currently, there are two BOT road projects
implemented in the country. The Southern Tagalog
Arterial Road (STAR) Tollway is currently operationalwith a 30- year concession period from 2000 to 2029 while
the Tarlac-Pangasinan La Union Expressway (TPLEX) is
under construction with a 35-year concession period from2008-2043. Prior to the projects’ submission to theevaluating agency to facilitate subsequent approval, thelength of concession period is already defined and thus
treated as an input parameter in project evaluation.
Moreover, a quantitative risk analysis which considers theimpact of variability in the concession parameters has not
yet been put into practice. At present, the evaluating
agency adopts a single point or deterministic modeling in
project evaluation wherein a single best guess value ofeach variable is used to determine the financial indicators.
While Shen et al.’s [1, 2] studies offered an analytical
deterministic method for determining the length ofconcession period to be granted to the private sector, theycannot be applied directly to the case studies used in thisresearch. The cash flow structure generated for the casestudies does not follow the cash flow trend of the
hypothetical project used in previous studies. Likewise, thesimulation model proposed by Shen and Wu [3] cannot beapplied to the case studies as the same hypothetical project
was utilized in the study. On the other hand, Ng et al.’s [4]proposed methodology was not used as it is believed thatnet present value (NPV) analysis is a more powerful
method of project evaluation compared to internal rate of
return (IRR) analysis used in the study. NEDA et al. [5]affirmed that the NPV criterion is widely accepted byaccountants, financial analysts and economists as the only
one that yields correct project choices in all circumstances.
This paper aims to provide a methodology to determinea reasonable concession period that considers the effect ofrisks on uncertain concession items in project evaluation.
The proposed methodology generated a concession periodinterval within which a specific concession period could be
agreed upon by the government and the private sector. Any
point within the interval could be considered as the41

optimal concession period that would be advantageous toboth BOT players. The results showed that the length ofthe concession period of the case studies should have
longer than the actual concession period granted to theprivate sector.

2 Concession Terminologies
Terms related to the analysis of concession period were
defined as follows. The relationship of theseterminologies is shown in Figure 1.
NPV
Repayment Period

Payback PeriodT0Concession Period
Breakeven Point

Construction
PeriodEnd of Economic Life

Operation Period

Concession Period
Transfer Point

Figure 1. Typical NPV Structure of a BOT Project Concession Period – counts from the time when
project owner and investor start signing the agreement[6] and includes construction and operation period.
 Breakeven Point – a point where net present value
equals zero [7].
 Payback Period – time required to recover the cost of
initial investment [8]. It ends when project reaches
breakeven point.
 Repayment Period – the period which the debt
obligation/loan is to be repaid [9].
 Transfer Point (TC) – any time/year (T) between the
breakeven point and the end of economic life; point
where the project will be transferred back from theprivate sector to the government.
 Concession Interval/Concession Period Interval –interval/period starting from the project’s breakevenpoint until the end of economic life, period wheretransfer point (TC) decided based on the result of
negotiation between the government and the private
sector could occur.
 Economic Life – period over which the project would
generate net gains [10].

3 Methodology
3.1 Concession Parameters

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

The case studies’ concession parameters used as inputs tothe financial model were categorized into (i)deterministic/certain and (ii) uncertain parameters.

3.1.1 Deterministic Parameters
Deterministic parameters are defined in this research asparameters of which values could be considered as stable
over time.
a. Construction Period – The private sector would ensurethat construction will be finished on time or even aheadof schedule to maximize generation of revenues by
longer period of operation. Construction period(inclusive of the period for pre-construction activities)
used in the model for STAR Tollway and TPLEX wasyear 2000 to 2007 and year 2008-2014, respectively.
b. Tax rates – Corporate tax and value-added tax imposedon the project are prescribed by law to be 32% oftaxable income and 12% of revenues, respectively.
c. Other expenses during operation – This includes items
such as insurance, rental fees and other management
expenses apart from the regular annual operations andmaintenance (O&M) cost spent for both projects.

3.1.2 Uncertain Parameters
Uncertain parameters, on the other hand, are those inputs
which may be subject to change and high level of
uncertainty over time since their future values are difficult
to predict.
a. Investment cost – This includes expenditure items
needed until the completion of the project. Quantity andcost of construction materials could change during thecourse of implementation. For TPLEX, Program
Evaluation and Review Technique (PERT) distribution
will be assigned for the cost components which are
likely to be risky and prone to variation with distribution
parameters of 120%, 100% and 90% of base cost. Since
the construction of STAR Tollway was completed, its
investment cost was considered deterministic in thefinancial evaluation. Base cost estimated for both cases
was PhP3.4 billion in 2005 prices and PhP 16.8 billion
in 2007 prices for STAR Tollway and TPLEX,
respectively.
b. Macroeconomic variables – The fluctuations of themarket variables such as inflation, interest rate andexchange rate maybe caused by the changes in thepolicies of the government and economic conditions.
Normal distribution (mean (µ)=0, standard deviation(σ)=2.2%) will be employed to model inflation
variability with σ computed using the historicalinflation. As exchange rate is a function of inflation, itsvariability was already captured. For interest rates,
normal distribution will be employed with µ=0 and
σ=1%.
c. Expected rate of return – This was set equal to theweighted average cost of capital (WACC) determined by
calculating the relative weights of the capital resources
and multiplying them with the corresponding
opportunity cost of capital (bank interest rate in the caseof debt/loan) for each of the capital resource.
d. Traffic volume – As revenue of the case projects
depends greatly on the realization of the assumed traffic
projection, there is a great risk that the expected rate of
return from the project may not be achieved if the42

assumed traffic is not realized. Normal distribution will
be used to model the risk associated with traffic volume
with σ=5% of estimate at a particular year.
e. Toll fee – Toll fees to be charged to the users both forSTAR Tollway and TPLEX as well as periodic increases
follow a parametric formula which is a function ofmacroeconomic variables.
f. O&M cost – Cost of labor and materials/ equipments
needed for the annual O&M may vary over time.
Uniform distribution with parameters U[minimum=
100% of base O&M cost, maximum=110% of base
O&M cost] will be employed.
g. Revenue – This is determined by the toll fee and thetraffic volume expected for the project which are
uncertain parameters.
h. NPV – The net cash flow (NCF) calculated byobtaining the difference of revenue and cost was
discounted by the WACC to obtain the NPV of the
project.

3.2 Monte Carlo Simulation
After the financial model has been set-up with NCF and
NPV set as the outputs, Monte Carlo simulation was
performed using the software @Risk 5.5.1 Industrial for
Excel. @Risk is a spreadsheet add-in to Excel that
performs risk analysis to show possible outcomes and tell
how likely these outcomes are to occur. @Risk was set toperform 10,000 iterations in each case study.

3.3 Proposed Methodology for Locating
Concession Period Boundaries
The equations proposed by Shen et al. [1,2] were modified
to consider the impact of loan repayment period while
ensuring the expected rate of return from the project. The
following equations will be used in the proposed
methodology for determining the concession period:
NPV(TC_L) ≤ NPV(TC) ≤ NPV(Tf)(1)
NPV(TC_U)= NPV(Tf(2)
TC_L ≤ TC ≤ TC_U
(3)
subject to the following conditions:
NPV(TC_L) ≥ 0 such that TC_L ≥ TPB(4)
TC_L ≥ TRP(5)
where:
TC_L – lower boundary of concession interval where NPV= NPV(TC_L)
TC_U – upper boundary of concession interval where NPV= NPV(TC_U)
Tf – end of economic life where NPV= NPV(Tf)
TPB – end of payback period
TRP – end of loan repayment period

Given an NCF and NPV resulting from the simulation
as shown in Tables 1 and 2 and applying the principles ofEquations 1 to 5, the following procedures are proposed
for locating the concession boundaries.
 To locate TC_L:
a. Look for TRP) and check whether NPV(TRP) ≥ 0.
b. If NPV(TRP) ≥ 0, this year will be considered as
the TC_L.
c. If not, look for TRP+n where NPV(TRP+n) ≥ 0 to
be considered as TC_L. TRP+n is the year following
the TRP where n= 1,2..n.
 To locate TC_U :a. Look for the last year where the net cash flow(NCF) ≥ 0.
b. Consider that year as the end of economic life(Tf) and hence the TC_U.

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Section 3.3, the concession period could occur at thefollowing interval:

3.4 Bargaining Game Theory
In an attempt to identify a specific concession, bargaining
game theory was employed. The formulas proposed by
Shen et al. [2] were likewise modified to consider the
impact of repayment period and to employ the procedures
proposed for locating the boundaries of concessioninterval.
As the BOT players behave rationally, they will noteasily give up but bargain for a best deal during thenegotiation. For the benefits that the private sector andgovernment would have to share within the concession
period interval, the proposed formula to be used is:
Benefits generated between [TC_L, TC_U]=NPV(Tf ) – NPV(TC_L)

2043 ≤ TC ≤ 2062
2035 ≤ TC ≤ 2057
Table 1. NCF and NPV of TPLEX
Year
rNCFi
rNPVTc2009-1004.66-816.21(6)

If the first round offer is initiated by the government,
its best strategy is viewed as making the first round offerthat can allow the investor to gain a similar range of
payoffs to what the investor would gain from his possible
counteroffer. If the first offer is rejected, the private sector
is expected to make a counter offer. But in making acounteroffer, he would have to bear the bargaining cost fp,
and the cost of time value by applying the discount factor
δp, thus, the investor can get a minimum payoff, δpqp - fp
and maximum payoff, δpQp - fp. Consequently, thestrategy would allow the government to get a maximum
payoff, Qg and minimum payoff, qg equivalent to:
Qg ≤ [NPV(Tf) – NPV(TC_L)] – (δp qp – fp)
qg ≥ [NPV(Tf) – NPV(TC_L)] – (δp Qp – fp)

(7)
(8)

If indeed the investor makes a counteroffer, his best
strategy is to make an offer that allows the government togain a similar range of payoffs to what the government
can gain if bargaining continues to the next round. At thispoint, the government will bear the bargaining cost of 2fg,
for producing the first offer and the counteroffer, and thecost of time value by applying the discount factor δg, thus,
the government can get a minimum payoff, δgqg - 2fg andmaximum payoff, δgQg – 2fg. This gives the investor amaximum payoff, Qp and minimum payoff, qp of:
Qp≤ [NPV(Tf) – NPV(TC_L)] – (δg qg – 2fg)
(9)
qp ≥ [NPV(Tf) – NPV(TC_L)] – (δgQg – 2fg)

( 1 − δp )[ NPV ( Tf ) − NPV ( TC _ L )] − 2δpfg + fp
1 − δpδg
T’C_U = T[NPV = NPV(Tf) – Qg]

(10)
(11)

491.63

…

…

…

2051

3484.432013-4324.51-10342.50

2052

3585.16525.43

2014
…
2023

1543.04…
2012.65-9596.48…
-4829.24

2053
2054
2055

3200.95
3268.192812.32

552.84578.19597.75

…

…

…

2056

2826.81615.462042
2043

4065.83
3901.68-83.16
10.71

2057
2058

2291.722245.88628.49639.992044

4069.64

98.80

2059

1605.76647.212045

3873.81174.562060

1486.14653.32

2046
2047
2048

4032.693805.163948.50245.75306.39363.02

2061
2062
2063

726.49427.13-515.69655.96657.35655.972049

3676.79410.642064-1527.50

651.73

Table 2. NCF and NPV of STAR Tollway
Year
Year
rNPVTcrNCFi
2005-1019.57
0
2045
2006
-654.16-1817.67
2046
…
…
…
2047

rNCFi
1240.32
1192.06
1135.92

rNPVTc387.17409.38428.61

2017
…
2035
2036

1177.031104.591021.231040.6446.7
462.12475.07487.07357.33…
944.611021.02-1407.03
…
20.36
70.46

2048
2049
2050
2051

2037

1033.92

116.5

2052

931.25496.832038
2039

1045.251128.28

158.74200.132053
2054

806.44785.62504.53511.362040
2041

1131.31129.82237.8
271.942055
2056

620.82420.18516.29519.37

2042
2043
2044

1210.18
1200.071180.04305.14335.03361.71

2057
2058
2059

199.77-53.95-348.11520.78520.61518.93

1+ r

(12)

(13)
(14)
(15)
(16)
(17)

The new lower concession boundary (T’C_L) is located at:
T’C_L = T [NPV = NPV(TC_L) + Q’p]rNPVTc455.03

Bargaining costs fg and fp for the government (G) andthe private sector (P), respectively, could be in the formof losses due to costly delayed agreement. If the length ofconcession period is not agreed at once, the benefits thatwill be generated on TC_L could be considered losses toboth BOT players. With various combination of fg and fp
as percentage of NPV(TC_L), NPV at the concession
boundaries, and the computed δp and δg plug into theequations in Section 3.4, the new concession boundaries[T’C_L , T’C_U] are shown in Table 3.
Since it is difficult to find a perfect converging pointof concession period, tolerance level, α is used to indicatethat any point within the concession interval would be an
agreeable concession period if the following criterion can
T' C_U − T' C_L
be met:(20)

Similar procedure is done when the private sector
makes the first round offer. The modified equations are as
follows:
Q’p ≤ [NPV(Tf ) – NPV(TC_L)] – (δp q’g – fg)
q’p ≥ [NPV(Tf ) – NPV(TC_L)] – (δg Q’g – fg)
Q’g ≤ [NPV(Tf ) – NPV(TC_L)] – (δp q’p – 2fp)
q’g ≥ [NPV(Tf ) – NPV(TC_L)] – (δp Q’p – 2fp)
( 1 − δg )[ NPV ( Tf ) − NPV ( TC _ L )] − 2δgfp + fg
Q' p = q' p =1 − δp δ g

rNCFi
3801.10

To narrow down the resulting concession interval,
bargaining game theory was employed. With a cost ofmoney of the government of 10% and cost of money ofthe private sector equal to 10% and 13% for TPLEX andSTAR Tollway, respectively, the BOT player’s discountfactor (δ) can be computed using Gibbon’s formula [11]1(r=cost of money of BOT player):(19)δ=The new upper concession boundary (T’C_U) is located
at:

Year
2050

4.3 Narrowed Concession Period Boundaries

Transforming Eqs. 10 and 9 to Eqs. 7 and 8, resp:
Qg = qg =for TPLEX
for STAR Tollway(18)

4 Results and Discussion

α≥

4.1 Simulation Results
The resulting NPV and NCF with risk factors denoted by
rNCFi and rNPVTc, respectively, are shown in Tables 1 and
2.

4.2 Concession Period Boundaries
With TRP of 2023 and 2017 for TPLEX and STAR
respectively, and applying the procedures prescribed in
43

TC_U − TC_L

For a tolerance level of 10%, the range at various
scenarios is acceptable as the computed α for both casestudies as shown in Table 3 is smaller than the assumed
level. Consider the scenario where BOT players would
have a 50-50 % share of NPV(TC_L). As shown in Figure
2, there is about 54% probability that TPLEX’s NPV at

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

that the government is protecting the interest of the private
sector, thus, giving more confidence to the private sector
to invest in government infrastructure projects throughBOT. The resulting concession period also protects theinterest of the government as it ensures that the project isstill profitable for the government to operate before
reaching the end of its economic life.
The proposed methodology is envisaged to help thegovernment in further enhancing its policies in processing
BOT projects. With the concession period treated as an
output parameter, chances of eliminating potential BOT
project will be avoided. The evaluating agency may find a
BOT project not feasible at a pre-set concession period andmay recommend disapproval thereof. With the analysis ofconcession period, a BOT project maybe found feasible ata longer concession period with a confidence of achieving
the expected returns indicated by the statistics resulting
from the simulation. This broadens the current
deterministic evaluation criteria which focus on NPV andIRR results as viability indicators.
The proposed methodology should also be tested onother BOT infrastructure projects which may havedifferent cash flow structure to establish its effectiveness.
Future works may also focus on further refinement of thesimulation model by gathering more information on key
variables to accurately assign their probability distribution.
Further, it is recommended that the evaluating agency
should already shift from using the current deterministic
model to the use of simulation model in the financial
evaluation of future BOT projects. While it is recognizedthat the software used for Monte Carlo simulation is quite
expensive, the government should invest in acquiring thistool to improve its evaluation techniques and properly
manage risks taking into account the likelihood of
occurrence resulting from the simulation.

year 2047 will be equal or less to the mean computed by
the simulation. In other words, there is about 46% chancethat the NPV at year 2047 would further exceed the mean.
This indicates that the private sector is secured of its
return until the end of concession period should year 2047
be decided as the transfer point. For STAR Tollway, if
year 2040 is selected as the transfer point, there is a 53%
chance that the NPV at this point will be equal or less to
the resulting mean NPV as shown in Figure 3. This
indicates that an NPV greater than the mean at year 2040
could still be expected with 47% probability.
Table 3. Sensitivity Analysis with Bargaining Cost Scenarios% Share to Bargaining Cost
G
10%
20%
40%
50%
60%
P
90%
80%
60%
50%
40%
TPLEX
T'C_L
2046.12046.42047.02047.32047.6
T'C_U
2046.52046.82047.42047.72048.0
α
1.71%
1.71%
1.83%
1.83%
1.83%
STAR Tollway
T'C_L
2036.52037.22038.62039.32040.1
T'C_U
2037.82038.52040.02040.82041.7
α
5.97%
6.24%
6.49%
7.08%
7.48%

80%
20%

90%
10%

2048.32048.72.18%

2048.72049.12.25%

2041.92043.78.27%

2042.82044.8
8.96%

References

3

2

1

0

-1

-2

-3

-4

Figure 2. NPV of TPLEX at Year 2047

[1]

Figure 3. NPV of STAR Tollway at Year 2040

5 Conclusion and Recommendation
The proposed methodology will enable the evaluating
agency to analytically determine the reasonable concession
period to be granted to the private sector taking into
account the uncertainties associated with various
concession parameters in the financial evaluation. The
analysis of NPV results generated by the simulation
provided a range of concession period for the government
and the private sector to negotiate. The negotiation toarrive at a specific concession period is considered as abargaining situation and thus bargaining game theory wasutilized in this research. While a perfect converging pointof concession period was not achieved, it can be inferredthat any point chosen on the resulting concession interval
is a reasonable concession period that would be
advantageous both to the government and the private
sector.
The concession period of the case studies were foundto be longer using the proposed methodology indicating
the impact of risks in the cash flow with the use of Monte
Carlo simulation. Longer concession period would give
private sector a greater chance to exceed the expected
return from its investment. Granting a longer but
reasonable concession period is also one way of ensuring
44

[2]

[3]

[4]

Shen, L.Y., Li, H. and Li, Q.M., Alternative Concession Model for
Build Operate Transfer Contract Projects, Journal of Construction
Engineering and Management, ASCE, 2002
Shen, L.Y., Bao, H.J., Wu, Y.Z. and Lu, W.S., Using BargainingGame Theory for Negotiating Concession Period for BOT-Type
Contract, Journal of Construction Engineering and Management,
ASCE, 2007
Shen, L.Y. and Wu, Y.Z., Risk Concession Model for
Build/Operate/Transfer Contract Projects, Journal of Construction
Engineering and Management, ASCE, 2005

Ng, S.T., Xie, J., Cheung Y.K., and Jefferies, M., A simulation
model for optimizing the concession period of public-private
partnerships schemes, International Journal of Project Management,
2007[5] National Economic and Development Authority, CanadianInternational Development Agency, and United Nations
Development Programme, Reference Manual on Project
Development and Evaluation, Volume 1, Philippines, 2005
[6] Xie, L. and Xu, W., Calculation of the Concession Period forGeneralized BOT Project, IEEE Xplore, 2008
[7] Fragniere, E. and Sullivan G., Risk Management: Safeguarding
Company Assets, Thomson Netg, Crisp Fifty-MinuteTM Series,
2007[8] Schniederjans, M., Schniederjans, A., and Schniederjans D.,
Outsourcing and Insourcing in an International Context, M.E.
Sharpe, Inc., 2005[9] Organisation for Economic Co-operation and Development (OECD)
Publishing, OECD Glossary of Statistical Terms, 2008[10] UNIDO, Guidelines for Infrastructure Development through BuildOperate- Transfer (BOT) Projects, 1996[11] Gibbons, R., Game Theory for Applied Economists, Princeton
University Press, USA, 1992

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Representation of the Double-Directional Channel
by using Spherical Wave Expansions
Student Number: 08M18111

Name: Kenji Nakai

Supervisor: Jun-ichi Takada

球波動関数を用いた双角度チャネルモデルの表現
中井

健二

本研究では，アンテナの電磁界を球波動関数で表せるように，従来平面波モデルで表されてきた双角度チャネ
ルモデルに対しても球波動関数を使って構築することを試みる．もっとも単純な例として，半波長ダイポールア
ンテナと自由空間見通し（Line Of Sight:LoS）伝搬路を取り上げ，従来の平面波を用いたモデルとの比較を通じて球波動関数を用いたチャネルモデルの導出を試みる．

I. I NTRODUCTION
There is an approach that extracts or de-embeds thetransmitting (Tx) and receiving (Rx) antennas from themultipath radio channels, as they are parts of communication devices of the channel in particular in the state-ofart Multiple-Input Multiple-Output (MIMO) technology.
This approach is called double-directional channel, since
it constructs a radio propagation model independent ofantennas. This approach enables the comparison of performances of different antennas in the same propagation
environment by embedding the antennas into the doubledirectional channel.
Conventionally, the double directional channel is expressed as a superposition of local plane waves at both Tx
and Rx antennas. Although the radio wave emitted froman antenna propagates as a spherical wave, Rx antenna
is very far from Tx antenna, so that this spherical wave
is modeled as a plane wave locally. Moreover, in the
scattering processes such as reflection and diffraction,
image source and diffraction point are also very far fromboth Tx and Rx, so that they are also modeled as planewaves.
This thesis tries to express the double-directional channel by the spherical wave expansions (SWE). Not only theantennas, but also the propagation channel is expressed by
SWE as shown in Fig.1. The advantage of the use of SWE
is that the resolution in angular domain is automaticallydetermined by the size of the antenna. In contrast, therequired angular resolution of the plane wave is infinite
regardless of the antenna size.
This thesis reviews the basic spherical wave theory[2−3] at first. Next, how to find the unknown coefficientsfor the spherical harmonics expansion of the antenna
pattern is explained. A new modeling approach of the
double-directional channel using the spherical harmonics
is finally proposed by using the electric dipole. Finally, byutilizing the measured antenna pattern, the convergenceproperty of the finite sum of the spherical harmonics is
discussed.

Transmitter

ReceiverFig. 1. Double-directional channel expressed by spherical wave

are obtained. Their components of travelling wave repre(2)sented by using the spherical Hunkel function hn (kr)of the second kind can be expressed as [2]
(
)m
1
1
m
F 1mn (r, θ, ϕ) = √ √
−
e−jmϕ
|m|
2π n(n + 1)
(
)
|m|
|m|
−jmP̄n (cos θ)dP̄n (cos θ)(2)(2)
hn (kr)θ̂ − hn (kr)
ϕ̂
sin θ
dθ
(1)
(
)m
1
1
m
F 2mn (r, θ, ϕ) = √ √
−
e−jmϕ
|m|
2π n(n + 1)(
n(n + 1) (2)hn (kr)P̄n|m| r̂kr) dP̄ |m| (cos θ)
1 d (
n
+
krh(2)θ̂n (kr)
kr d(kr)dθ)) −jmP̄ |m| (cos θ)
1 d (
n
(2)
+
krhn (kr)ϕ̂kr d(kr)sin θ(2)where n = 1, 2, 3, ... and m = −n, −n+1, ..., 0, ..., n−|m|1, n, and P̄n (cos θ) is the normalized associated Legendre function. In general, n and m represent the modeindices in θ and ϕ. Equations (1) and equation (2) canbe rewritten by using the vector spherical harmnonics
Ym(1,2,3),n which are orthogonal mode functions of theelectromagnetic field in spherical coordinates as follows.
m
F 1mn (r, θ, ϕ) = h(2)(3)
n (kr)Y 1,n (r, θ, ϕ)
(
)
1 d
m
krh(2)
F 2mn (r, θ, ϕ) =n (kr) Y 2,n (r, θ, ϕ)kr d(kr)√n(n + 1) (2)+hn (kr)Y m
3,n (r, θ, ϕ) (4)kr

II. S PHERICAL WAVE T HEORYIn this thesis, time dependence of exp(jωt) is assumed.
By using a separation of variables and solving the freespace homogeneous vector Helmholtz equation in thespherical coordinates, the vector spherical wave functions
45

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Q
Q

V (t)
in

Tx ports(1,-1,1)……

(1,0,1)

AntennaconfigurationT

Rx ports
Multiport Q’
network Q’… …
V (t)
model
M
AntennaconfigurationR(1,-1,1)(1,0,1)out

Fig. 3. Equivalent circuit model

solution, i.e.

Fig. 2. Amplitude of spherical Hunkel function of the second kind vsphase

Q = F −E(8)−where F is the Moore-Penrose pseudo inverse of F .
Therefore, the vector radiation pattern is reconstructedsince the unknown coefficient Q is found by solving thesimultaneous equation as the Eq.(8).

Here, the spherical vector harmonics are the orthonormal
mode functions, therefore the following relation can bewillingly defined.
∫ 2π ∫ πm′ ∗Yms,n (r̂)Y s′ ,n′ (r̂) sin θdθdϕ = δss′ δnn′ δmm′
ϕ=0III. F OR C HANNEL M ODEL BY USING SWE

θ=0

(5)
Here, some weighted coefficients of arbitrary radiation
patterns (see Eq.(6)) can be derived by using this orthogonality afterwards.
By using SWE, the outward propagating electric fieldfrom the source in angular domain can be expressed as2Nn√ ∑∑ ∑
E(r, θ, ϕ) = k η
Qsmn F smn (r, θ, ϕ)

By representing antennas using Eq.(6), the doubledirectional channel can also be expressed as an equivalentmultiport circuit. Multipath environment such as Fig.1
can be represented as Fig.3, where each individual port
corresponds to a mode of spherical harmonics. In my
research, it is assumed that Tx antenna is connected tothe matched generator and Rx antenna is terminated by
the matched load.
Here, Q of the outward propagating modes at Tx
antenna, and is mapped into Q of the inward propagating
modes at Rx antenna, through the propagation channel
H prop are determined as follows.

s=1 n=1 m=−n(6)√where η is the wave impedance given by η = µ0 /ϵ0 ≃120π ≃ 377[Ω], k is the propagation constant given by√k = 2π/λ = ω µ0 ϵ0 is the wavelength. Regarding theindex s, s = 1 indicates TE-wave, while s = 2 indicates
TM-wave. Moreover, the spherical wave series can betruncated at a sufficient value of n = N determined by thesize of the antennas due to cut off property as Eq.(6) not
an infinite number of spherical wave modes that would
be required to exactly characterise the radiated field ofa general source, as shown in Fig.2. This graph can beseparated into the two regions. One is the far field or
propagation region following 1/r is emvelop attenuation.
The other one is the near-field or evanescent region where
the field attenuate exponentially. Even a strong field isgenerated within the region, it is attenuated exponentiallyand does rarely contribute to radiation field.
1) The derivation of Q coefficients: Here, from thesimulated radiation pattern, the coefficients Q in eq.(6)can be derived by solving the following set of linear
equations, although the orthonormality of vector sphericalharmonics which is eq.(5) that is usually used to derive thecoefficients. That is because all spherical pattern can not
be obtained in experiment data. By substituting Eq.(11)and Eq.(12) into the left-hand side of eq.(6), the outward
electric field can be expressed as matrix form as
E = QF

Q′ = H prop Q(9)where H prop is a propagation channel matrix determined
for the spherical harmonics. In the similar manner, Tx
antenna and Rx antenna are also mapped into their signals
vin (t) and vout (t), through the antenna transmitting coefficients and the antenna receiving coefficients as follows.
Q(t) = T vin (t),vout (t) = RQ′ .

(10)

where T is a SM N × 1 column matrix with elements,
R is a 1 × S ′ M ′ N ′ row matrix with elements. Therefore,
it is clear that T ∝ Qsmn and its reciprocal (Rsmn =(−1)m T s,−m,n ) at Rx as R ∝ Q′s′ m′ n′ . Rgardeing, thecomplex amplitudes of incoming and outgoing waves on
guide, the nomalization is chosen such that the power
carried by these waves are 12 |vin |2 (The insident power)and 12 |vout |2 (the radiated power), respectively. By using
spherical harmonics, the equivalent circuit of the channel
can be derived. In my thesis Tx antenna was represented
by using SWE first. And then, Rx antenna was also
represented by using SWE. Finally, the channel matrix
was also obtained by SWE. Each of the propagating mode
coefficients is finite due to cut off property so that thechannel can be also finite by SWE (S ′ M ′ N ′ × SM N ).
Data size is reducted remarkably compared to the plane
wave modeling, therefore, we can deal with discrete data
due to the SWE model.

(7)

where E is the simulated electric field column vector,
F is the matrix of the spherical wave functions andcolumn vector Q is the weight vector. Usually, Eq.(7)is overdetermined and E is derived as the least square46

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

10

Relative Error (%)

10

10

10

10

10

Fig. 4. half-wave dipole

0

-5

-10

-15

-20

-25

1

2

3

4

5

6
N

7

8

9

10

11

Fig. 5. Relative error of z-polarization

IV. S IMULATION OF A NTENNA PATTERNR EPRESENTATION

Z-polarization

A. Tx Antenna Side
A simulation is conducted by a simple antenna which
is a half-wave dipole to verify the theoretical concept.
As an example, in case of the far field of electric halfwave dipole in z-polarization (see Fig.4) where the dipole
is located in the origin of the coordinate system, theequation for vertical positioned half-wave dipole is asfollowscos( π2 cos(θ))
jηI0E θ (r, θ, ϕ) =exp[−jkr0 ](11)
2πrsin(θ)(12)
E ϕ (r, θ, ϕ) = 0
Then, simulated conditions are determined by.
• λ = 0.1m is assumed.
• Conducted for whole sphere to get the radiation
pattern in whole solid angle (Angle steps of θ, ϕ
are every 1 degree.)• r = ∞ is assumed.
1) Pattern Reconstruction using Finite Spherical Harmonics: Now, the question is how to select the appropriate N . Here, the relative error ϵ is considered as a functionof N∑, which)∑ (is defined as 22
sin θRR
S |∆Eθ (θR , ϕS )| + |∆Eϕ (θR , ϕS )|()ϵ= ∑ ∑,
2 + |E (θ , ϕ )|2 sin θ
|E
(θ
,
ϕ
)|
θ R
S
ϕ R
S
R
R
S
(13)
where, the error at individual angle, sin θ is weighted
considering the density of the point and ∆ means thedifference of reconstructed the electric field and simulated
the field simulated at each index (R, S), that are also thenumbers of samples in elevation angle and azimuth angle.
In this case, N in Eq.(6) is predicted as N ≃ 2.
Fig.5 shows the relative error as function of N for
the simulated antenna. For example, to suppress the error
lower than 1%, N should be at least 1. Fig.6 compares
the simulated and the reconstructed pattern for N = 2. It
is quite similar to the simulated pattern.
B. Rx Antenna SideRx antenna can be modeled in the same manner as
Tx antenna. To derive the inward propagating modes
Q′s′ m′ n′ , it is needed that plane waves arriving at the Rx
antenna are expressed by using SWE since on the farfield the waves is arriving there. This is owing to fact that
we suppose that a plane wave is arriving at Rx antenna
to simplify the model. Then, around the Rx antenna the

Simultion

Reconstruct at N=2

Fig. 6. Pattern comparizon

electric field includes the origin, so that in the field thespherical Bessel function is used as the solution of radialfunction in place of the spherical Hunkel function of thesecond kind in Eq.(6) since the Bessel function is notdivergent at the position.
Now, a plane wave E(r) = E 0 exp(−jk k̂ · r) (E 0 is aplane wave spectrum) with k̂ means the unit vector of
propagation direction of plane waves, which is observedat the position r measured from the origin. Then, it canbe expanded into regular spherical vector waves F ′s′ m′ n′using spherical Bessel function in place of the spherical
Hunkel function of the second kind of F smn used in theeq.(6) as follows [2], [3]√ ∑E 0 exp(−jk k̂·r) = k η
2

′

n
∑

′

N
∑

qs′ ′ m′ n′ F ′s′ m′ n′s′ =1 m′ =−n′ n′ =1(14)
Then, it can be shown that the unknown expansion
coefficients of the electric field in the Rx antenna can bederived by using the orthonomalization of vector sphericalharmonics of eq.(5) as
∫
∫ 2π
′
′
′
4π(−j)n −s +1 π∗′
E0 · Y m
qs′ m′ n′ =√s′ ,n′ (k̂)dΩk η
θ=0 ϕ=0(15)
where, for deriving eq.(15), the relation exp(−jk k̂ · r) isrepresented by SWE and used [4].
exp(−jk k̂·r) = 4π

2 ∑
∞ ∑
n
∑

′

′

∗
m
(−j)n jn (kr)Y m
s′ ,n′ (k̂)Y s′ ,n′ (r)s=1 n=1 m=−n

(16)
47

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Here, for empty space we have Q = Q′ . If there is no
dipole present, the total field is√ ∑ ′{Qsmn F (1)E(r, θ, ϕ) = k η
smn (r, θ, ϕ)smn+ Qsmn F smn (r, θ, ϕ)}√ ∑ ′Qsmn F ′smn (r, θ, ϕ)= 2k η
(17)smn

where F (1)smn means the vector spherical wave functions
using the spherical Hunkel function of the first kind whichis the inward propagating wave, and the relation of thespherical Bessel function and Hunkel function is used.
The expansion coefficients Q′smn , of the inward propagating modes are related to the expansion coefficientsqs′ m′ n′ of the regular waves as 2Q′smn = qsmn .
The both antennas model were derived. In the next
step, the double-directional propagation channel will bemodeled by using the eq.(9)Fig. 7. Relative error of measured and reconstructed pattern

VI. A PPENDIX
A. Experimental result of antenna pattern
The antenna pattern measurement has been conductedby using SATIMO Stargate [5], which is a spherical nearfield measurement system. The measurement is conducted
for the whole sphere to get the radiation pattern in thewhole solid angle. A biconical antenna (0.345λ × 0.60λ)
was measured at 2.385 GHz. Obviously this measurement
system itself may use the same spherical harmonics
approach inside the system and it just outputs far field
vector antenna pattern in whole sphere.
Fig.7 shows the relative error as function of N for themeasured antenna. For example, to suppress the error to
1%, N should be at least 3. The question is why the
behavior decays rapidly when N changes at 10 from 9
about above figure. The answer is because this system
cut off at the N = 10

V. D OUBLE - DIRECTIONAL C HANNEL M ODEL USINGS PHERICAL H ARMONICS
Now, we consider the same electric dipole antennas
at both stations are used and they are polarized in +zdirection at the origin. Then, the dipole field has only
one term in the summations, i.e. s = 2, m = 0 and n = 1[2]. So there are only six spherical modes altogether
with n = 1. Moreover, for a lossless antenna, T and
R explained in the section 3 are satisfying the unitarycondition which implies that the norm, the sum of theelements absolute squared, of every column and every
row. Therefore, |T |2 = 1. Since T vin = Q, and |vin |2 =|Q|2 due to power conservation. In case of our modeling,
T201 = R201 holds due to the reciprocal theorem, whichshows that only one mode of the incoming field willtransfer power to the load. So we have vout = Q′201 as it
is explained in the section 3. Hence, all the energy of theincoming mode is received by the dipole and absorved in
the load.
Here, F ′smn = F ′201 is the only mode to have a zcomponent at the origin. Setting r = 0 in eq.(17) therefore
yields by using the caluculation result of F ′201√
6π 1′vout = Q201 =(18)√ Ez (0, θ, ϕ)
2 k η
Equation (18) expresses the received signal in the dipole
in terms of the electric field component parallel to thedipole.

VII. C ONCLUSINON AND F UTURE W ORKS
This thesis presented how to express the doubledirectional channel modeling. By using measured or
simulated antenna pattern, the antenna model can bederived. And, a plane wave is expressed by using SWE
in Rx antenna. And, by using the electric dipole atboth stations, it is confirmed that the power received theprove when aligned parallel to the electric field of thetest antenna is the same compared to the plane wave
modeling.
In the next step, our taget is the MIMO
technology. Before multiplexing a channel, antenna offset should be considered since in the spherical coordinate
system the vector cannot define parallel shift. Therefore,
first the x−shift and z−shift may be a good commencing
point to validate it. If the shift is allowed, for MIMO
channel by augmenting both Tx and Rx. By comparing
the conventionally modeling, if new information is obtained, this is a way to ameliorate the quality of wirelesscommunication system and save frequencies.

A. Numerical Example
Here, the plane wave spectrum E 0 in the eq.(15) is
given by using the reconstructed electric field obtained in
the section 2√ e−jkrd(19)
E 0 = lim E(r, θ, ϕ) 4π
kr→∞
krd
√
where an additional factor 4π implies that expressions
for gain and directivity become simpler, and rd is the
distance between the Rx and Tx antennas. Finally, by
combining them, it is confirmed that the power received
the prove when aligned parallel to the electric field of
the test antenna is the same compared to the plane wave
modeling.

R EFERENCES[1]K. Araki, T. Ohira and J. Takada, “Eigen mode expansion of freespace and its application to an expression for antenna equivalentcircuit”, IEICE Tech.Rep, A.P2006-128, Jan.
[2]J.E.Hansen, Spherical Near-Field Antenna Measurement, IEE Electromagnetic waves series 26, London, U.K, 1988
[3]A.A.Glazunov, On the Antenna-Channel Interactions: A Spherical
Vector Wave Expansion Approach, Doctoral Thesis, Lund University, Lund, Sweden, February, 2009.
[4]J.D.Jackson, Classical Electrodynamics−second edition−, Wiley &Sons, 1975
[5]SATIMO,“Antenna Measuremnt”, http://www.satimo.com/content/
products/sg-24

48

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Effects of Power Sector Reform in China - A case study of Liaoning province
Student Number: 08M51545 Name: ZHENG Mingyue Supervisor: Naoya ABE
中国における電力部門改革の影響 ～遼寧省に着目して～
鄭 明月
本研究は、2002 年中国における電力部門改革の影響を評価するため、イギリスおよび日本における電力部門改革の影響と比較分析を行った。その際、中国の経済規模の巨大さなどを踏まえ、遼寧省の事例研究を行った。国レベルでの比較する上で、遼寧省の事例研究を行った。具体的には、①電力価格制度の定性的比較の後、②各国および省における電力発電部門における競争状況の創出の有無に着目し、市場集中度、ハーフィンダールハーシュマン指数及びジニ係数による三つの数値を算出・比較を行い、③さらに Chow テストにより、中国の電力発電部門に対して構造的影響を与えているか否か確認した。
mechanism in those three countries. The
second is to figure out whether or not there isa structural change in the Chinese power
sector after the reform.
In China, which is a broad and vast
country, there are 23 provinces, 5autonomous region and 4 municipalities.
There is a huge gap among them in terms of
economy scale, distribution of natural
resource, and population size. In thisresearch, Liaoning province, which is on oneof the economically rapidly growing provinces
in China, is selected as a case study region
because there were two pilot projects for new
electricity pricing policy.
3. Approaches
3.1 Concentration Ratio (CRn)
To assess the competition conditionsof the electricity generation sectors in China,
the concentration ratio is calculated. It
indicates the percentage of market shareowned by the largest n firms,(Ep.1)
where R: market share of company
In general, if CR4 <30%, there is acompetition market. If 30%<CR4<65%, there
is a middle level monopoly market. If
CR4>65%, there is a high level monopoly
market.
3.2. Herfindahl-Hirschman index (HHI )The U.S. Department of Justice uses HHI
to measure market concentration for
purposes of antitrust enforcement. HHI is
also used by the Japan Fair Trade
Commission to check the consequence ofmonopoly level when there is a proposed

1. Introduction
Since the establishment of thePeople’s Republic of China (RPC) in 1949, theelectricity industry had been controlled by
the central government until 1985. The
Ministry of Electric Power Industry managed
both administration and the single power
utility enterprise.
To meet the rapidly growing
electricity demand, the Chinese government
conducted the drastic reform of the power
sector in 2002, resulting in that the power
generation assets were allocated to fivenational power companies and the power gridassets was reorganized into national power
grids respectively in 2002. The sector reform
resulted in the division between power
generation and power transmission. StateElectricity Regulatory Commission of China(SERC) also was established to manage andmonitor electricity market.
The total capacity of China power
generation has indicated very high rates ofgrowth. In 1952, the total capacity was only
0.19GW, in 1987, it reached to 103GW, andthen in 2009, it had raised to 874GW. The
electricity generation in China depended on
fire power, which has 80% share of the total
capacity in 2009.
2. Objective
The first objective is to access thecompetitive market conditions of theelectricity generation market in China withcomparison with those in both UK and Japan,
after reviewing the electricity price49

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

M&A proposal between companies.
HHI uses the market shares of all thefirms in an industry and the corresponding
HHI value of the industry is the total squared
values of each firm share. It gives moreweight on those firms which occupy thelarger portion of the industry.

Regression for the total period:---- (Ep.2)
Range of HHI value is from 0 to
10,000. If HHI<1,000, there is a competition
market. If 1,000<HHI<1,800, there is a
middle level monopoly market. If HHI>1,800,
there is a high level monopoly market.
3.3 Gini Coefficients
Gini Coefficients are calculated tomeasure the equality or distribution of power
generation capacities of the power companies
as shown in Eq.3.

---(Eq. 7)
Then Chow test is required tocompute the following statistics, which
follows the F distribution.
The null
hypothesis is that there is no change in 2002.

---(Eq. 5)
Regression for the period before change---(Eq. 6)
Regression for the period after change--- (Eq. 8)
where k: number of variables, N: total sample
numbers,
Residual sum of squarefrom (Eq.5),
Residual sum of squarefrom (Eq.6),
Residual sum of squarefrom (Eq.7)(Eq. 3)Calculated F value will follow the
distribution with α (k, N-2k) degree of
freedom. In the example above,T : numbers of samples
Ct: electricity generation capacity
Rank Ct : rank of sample
G : Gini Coefficient value

if the significance level is at 5 % (p=0.05)
The computed F value is:When the value of Gini Coefficient isapproaching to 0, the distribution isapproaching to fair situation. When the valueof Gini Coefficient is approaching to 1, itmeans the distribution has a huge
differential.
In addition to the three approaches
above, qualitative analysis is adopted tocompare Chinese pricing policy with those inboth UK and Japan.
3.4 Chow test
Chow test, which is an application of
F-test, is a method to assess whether the
expected values of a quantitative variable
within several pre-defined groups differ fromeach other.
For example, the Chow test is adopted
to examine if there is a structural change in2002 for the relationship between Chinese
electricity generation amount and its GDP
from 1979 to 2009. Three regression runs
are then necessary as below.

--- (Eq. 9)

Because computed F value is smaller
than the critical F value, hypothesis was not
be rejected; that is, there is no structurechange happened in 2002.
4. Data
As for the necessary data sets for
China, interview survey was conducted in
China in November, 2010 to visit StateElectricity Regulatory Commission of China(SERC) in Beijing and Northeast Grid
company in Shenyang. By this survey, thefollowing data sets were collected; they are(1) total capacity of all generation companies
in China 2002~2009, (2) total production ofelectricity, (3) GDP 1996~2009 and total
capacity of all generation companies in
Liaoning province 2002~2009.
As for the data of the total
50

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

monopoly market. In China, before thereform, CR4>65%, there is a high levelmonopoly market. But after the reform,
30%<CR4<65%, it turned to middle levelmonopoly market. In Liaoning, before andafter the reform, CR4 >65%, so there is a highlevel monopoly market.
5.3 HHIThe HHI results of UK, Japan, China and
Liaoning are shown in Figure2. In UK, before
the reform, HHI>1,800, there so was a highlevel monopoly market. However 1,000<HHI<1,800, it turned to middle level monopoly
market. In Japan, both before and after thereform, 1,000<HHI<1,800, there is a middle
level monopoly market.
In China, before the reform, HHI>1,800,
there was a high level monopoly market.
After the reform, HHI<1,000, it turned tocompetition market.
In Liaoning, both before and after thereform, HHI>1,800, there was a high levelmonopoly market.

generation capacity of the electricity
companies in both UK and Japan, it was
obtained from each generation company
homepage.
5. Results
5.1. Price system
In UK, there are two pricing systems. One
is Contracts Market which generators makecontract directly with electricity suppliers tosell a fixed amount of electricity at a point in
the future. The other is Balancing Market
where generators resolve difference between
their contracted amount and amount ofelectricity they actually delivered.
Electricity price in Japan is determinedby the electricity companies under the
regulations of the government.
The Electricity price in China is controlled
by NDRC (National Development and reform
Commission). NRDC adjusts the priceseveral times one year according to coal pricefluctuation. The pricing system in Liaoning
province is consistent with the national
regulator which is decided by NDRC.

9000
8000

5.2 Concentration Ratio (CR4)
The concentration Ratio results of
UK, Japan, China and Liaoning are shown in
Figure1.
100
highmonopoly

90
80
70
60

middle50
monopoly
40

7000

high
monop
oly

Liaoning

※

China

6000
5000
4000

※
middlemonopo
ly

UK
Japan

Liaoning

UK

※
※

compe
titive

※

※

2000
1000

China

30

3000

Japan

※ ※

0

20
competitive 10※:electricity sector※:electricity sector

0

Figure2. Herfindahl-Hirschman index resultsof UK, Japan, China and Liaoning province

Figure1. Concentration Ratio results of UK,
Japan, China and Liaoning province

Numbers of generation companies in China
is much more than UK and Japan, so the
HHI value of China is lower than UK andJapan. However, in the provincial level, in
Liaoning province, the number of generationcompanies close to UK and Japan. But the
HHI value is much larger than UK and

In UK, before and after the reform,
CR4 >65%, so there is a high level monopoly
market. In Japan, before and after the reform,
30%<CR4<65%, so there is a middle level

51

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Japan. In the province area, monopoly levelof China is higher than UK and Japan.
Table1. Number of electricity generationcompanies in 3 countries and Liaoning
province

UK
8

Japan
10

China
33

capacity. As hindsight, the unbundling of
the power sector, especially the generation
sub-sector, could be possible only after the
total size of electricity generation capacity
became large enough.
Table 2 result of Chow test on generation
production & GDP of China 1996~2009
Comouted Critical F
Year
Hypothesis
F value
value
1996
1.3
3.1 can not reject
1997
2.3
3.1 can not reject
1998
4.1
3.1
rejected
1999
7.7
3.1
rejected
2000
16.3
3.1
rejected
2001
5.9
3.1
rejected
2002
1.3
3.1 can not reject
2003
0.9
3.1 can not reject
2004
0.6
3.1 can not reject
2005
0.4
3.1 can not reject
2006
0.3
3.1 can not reject
2007
0.2
3.1 can not reject
2008
0.1
3.1 can not reject
6. Conclusion
The conclusion of this study issummarized as the following. As for the firstgoal, it is confirmed that electricity pricesystem in China has not been formed througha competition market comparing with UK
and Japan. Then, based on the results of the
3 methods for the assessment of market
competitiveness in China, the study foundthat the electricity generation market in
China cannot be regarded as competitive. At
Liaoning province, the number of generationcompanies is limited and its state is close tomonopoly. As for the second goal, according to
the result of Chow test, it could not confirm
that power sector reform induces obvious
effect on the structure change of generation
production and GDP at the national level.
References[1]Asgari, Mohammad Hssein. Market power
analysisi for the electricity market. Energy
Policy, 2010, 38(2010)5582-5599.
[2]Colella,
Whitney.
Implicationsofelectricity liberalization for combined heatand power fuel cell systems: a case study of
the United Kingdom. Journal of Power
Sources106, 2002, 397-404.
[3]H.W.Ngan. Electricity regulation andelectricity market reforms in China. Energy
Policy, 2010, 38(2010)2142-2148.

Liaoning
6

5.4 Gini Coefficients
The Gini Coefficients results of UK, Japan,
China and Liaoning are shown in Figure3. In
UK, after the reform, Gini value is getting
smaller, reaching to much less value
compared to others. It means the scale ofgeneration companies is close to each other.
In Japan, Gini value has not changed much,it similar to Liaoning. Japan and Liaoning
are between UK and China. In China, after
the reform, it has the highest Gini value, itmeans the capacities of China concentrated
in several companies.
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Liaoning
UK

※

Japan

China

※
※

※:electricity sector
reformFigure3. Gini Coefficients results of UK,
Japan, China and Liaoning province
5.5 Chow test
The result of Chow test for the
possible structural change which can befound in the simple relationship between
electricity generation and GDP in China from1996 to 2009 is shown in Table 2. The results
indicate that there are structural changes
from 1998 to 2001 (4 consecutive years),
implying that this study could not find astructural change after the Power sector
reform in 2002. Some structural change could
occur before the Power sector reform in 2002,
possibly due to massive investment for theaugmentation of electricity generation
52

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

EXPERIMENTAL STUDY ON THERMAL BOUNDARY LAYER
ALONG A BUILDING WALL
Student Number: 09M18019 Name: Shiho ONOMURA Supervisor: Manabu KANDA
建物の鉛直壁面に発達する熱境界層の観測研究
小野村

史穂

本研究では，建物鉛直壁面に発達する熱境界層の観測を行った．壁面近傍には，法線方向に，複数の熱電対と超音波風速計を配置し，熱駆動上昇流の詳細な温度分布と速度情報を取得した．壁面近傍の温度及び温度変動強度の分布から，壁面近傍の上昇流が温度差による乱れと大気中の擾乱との影響により，三層に分類できることが明らかとなった．自然対流とは異なり，壁面からの対流熱伝達は，大気の乱れにより促進されていた．

1. Introduction

wind tunnels. However, the studies were based on

In the field of urban meteorology, it is important tonatural convection or combined convection with laminarknow the temperature and velocity distribution withinflow in the outer layer and the fetch scale of the heaturban canopy layers. Nakamura and Oke [1] and Kanda

plates was much smaller than that of building walls.

et al. [2] obtained the overall temperature/wind patterns

Therefore, it is unclear that those results can be applied

within street canyons. An alternative way to predict

to the thermal boundary layers along building walls

temperature and velocity fields within urban canopy

which have the overwhelmingly long fetch of building

layers is numerical models. In numerical approaches, thewalls and high Re number.

parameterization of boundary conditions is important.

In the present study, the measurements of thermalMonin-Obukhov similarity (MOS) is applied to thermalboundary layers developed along a building wall wereboundary layers along horizontal surfaces, whereas suchconducted to provide better understandings of thermal

theoretical frameworks have not been established forexchange process along vertical walls and to lead to thevertical walls. Thus, the measurements of the thermal

better modeling of the process.

boundary layer developed along the vertical walls of

2. Experimental setup

buildings are very important to know the mechanism ofheat transfer process near walls and formulate the

The measurements were conducted on the dates

parameterization. However, the previous field studies did

written in Table 1. The south-southwest oriented wall of

not focus on thermal boundary layers very close to

Ishikawadai 3rd Building, Ookayama campus, Tokyo

vertical walls.

Institute of Technology was selected for the thermal

Thermal boundary layers along a vertical heated plateboundary layer observation (Fig.1 (a)). The wall ishave been investigated well in the field of mechanicalconstructed by concrete and the surface was covered byengineering. Tsuji and Nagano [3] and Hattori et al. [4,beige tiles. The weather was almost fine through the5] have precisely measured temperature and velocity

observation.

profiles normal to vertical heated plates using verticalperpendicularly to the wall at a height of 22.16 m from53

A

thermocouples

rake

was

set

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

the ground and at the lateral center of the wall (Fig.2).

3. Result

On the rake, the thermocouples were installed more

3.1. Mean temperature profiles

densely with shorter distance from the wall. The

The shapes of mean temperature profiles normalized

diameter of thermocouples is only 0.05 m on the edges toby temperature difference, T  Tw  T  , almost

reduce the influence of directly radiation heat. Threeagree with each other. However, as observed in Fig.3, theultrasonic anemometers were set at 2m above the roof

profiles of 3  T are shifted to higher temperature

top (S1), 35 mm away from the wall (S2), and 2 m away

compared with that of 1  T  3 . This detail will befrom the wall (S3). For Case4, part of the wall was

discussed in chapter 3.5.

covered by black-painted Styrofoam boards to make3.2. Intensity of temperature fluctuation

large temperature differences and to amplify the wall

Profiles of the intensity of temperature fluctuation,  t ,temperature fluctuations, so that they can be captured byan infrared camera (Fig.1 (b)).

in Case 3 are shown in Fig.4. Three layers can berecognized. The ranges of each layer are (1) the nearest
layer (y  0.001 m), (2) the middle layer (0.001 m < yTable 1. Dates and measurement heights
No.

Case 1Case 2

Case 3

Case 4Date

1st Aug.

28th Aug.

3rd Sep.

16th Dec.

2.87 m22.16 m22.16 m22.16 mMeasurement
height< 0.04 m), and (3) the farthest layer (0.04 m  y). The
characteristics of each layer can be explained by using
two

main

elements

which

generate

temperaturefluctuation. One is natural convection resulted fromtemperature

difference,

and

the

other

is

forcedconvection contained in the outer layer. Although somesurrounding factors like heat from the ground and winds
blowing through buildings can affect thermal boundary
layers, it can be assumed that they are included into
natural or forced convection. At the nearest layer, theformer is dominant and the latter is very small, since  t(b) Case 4(a) Case 1-3

can be normalized by T . The value of  t is small

Figure 1 Wall of Ishikawadai 3rd building

because viscosity suppresses it. That means the influenceof forced convection is small. At the farthest layer, theDistance from wall [m]profiles are independent of T . Turbulence in forced

0

Ultrasonic anemometersconvection mainly induces the temperature fluctuation.

0.00030.0005roof top S1

0.0007close-up

0.001

2m

0.003

θ (=(T-T∞) / (Tw-T∞))

35 mm

0.005

u

S2

S3

0.008

0.012
0.018

w
22.16 m

0.026

v

0.04
0.056

316 mmX

Thermocouples rake

Z

0.07
0.1

0.12
0.15

2.87 m

Y

0.2

1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.0001

5≦ ΔT
4≦ ΔT
3≦ ΔT
2≦ ΔT
1≦ ΔT

0.001

0.01

0.1

1

distance from wall y [m]0.316

Figure 2 Experimental setup

Figure 3 Mean temperature profile

54

5
4
3
2

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Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

The absolute value of  t seems to be related to the3.3. Instantaneous phenomenon

intensity of disturbance in the outer layer. The reason

Time variations of temperature at all the measurement

why  t gets smaller with y is the effect of heat frompoints and velocities measured near wall are shown in

the wall decreases as the measurement point is farther

Fig.6. The three layers as mentioned above also can befrom the wall. The middle layer includes both influences

recognized in this time series. The characteristics of eachbecause the value of  t is largest. In this layer, naturallayer can be explained as described in the chapter 3.2.

convection generated by the temperature difference andFocusing on the middle layer (0.001 m < y < 0.04 m), theforced convection in the outer layer can be intermittently

patterns of the flow close to the wall are discussed. In themixed.

time range of 1 - 3 s, temperature in the middle layer

For investigating the influence of forced convection

rapidly dropped with strong upward wind coming fromfrom the outer layer on temperature fluctuation, thethe outer layer. In the time range of 3 - 6 s, velocities

profiles in the range of 3  T  4 in Fig. 4 is further

decrease and the temperature of the middle layerclassified into each range of turbulence kinetic energy,

increases.

e . As seen in Fig.5, the intensities of temperature

convection is dominant in the former time period (1 - 3 s),

fluctuation are larger, as e

is higher. A profileand natural convection is dominant and the thermalsatisfying the condition of 3  T  4 and e  0.3

boundary layer is established near the wall in the latterfrom Case 2 is also plotted by plus signs. It agrees welltime period (3 - 6 s).

Thisobservationsuggests

thatforcedwith that taking the same condition in Case 3. This

3.4. Heat transfer

agreement indicates the result that the intensity oftemperature fluctuation depends on mainly T and e .

Fig.7 shows heat transfer rates in Case 4. Nux
number decreases as Rax number increases. This result

0.18
5≦ ΔT
4≦ ΔT
3≦ ΔT
2≦ ΔT
1≦ ΔT

0.16

0.14

σt /∆T

0.12
0.1

is different from the relationship that Nux increases

5
4
3
2

with Rax under natural convection in the wind tunnel
experiments (Tsuji and Nagano (1988) and Hattori et al.

0.08(2006)). It is because heat transfer rate is related to not

0.06

only natural convection but also forced convection. As

0.04
0.02
0
0.0001

0.001

0.01

0.1

1

10

distance from wall y [m]σt / ΔT

Figure 4 Intensity of temperature fluctuation (Case 3)

0.26
0.24
0.22
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
0.0001

0.8 ≦ e0.3 ≦ e 0.8
e 0.3
e 0.3 (Case2)

0.001

0.01

0.1

1

10

distance from wall y [m]Figure 5 Intensity of temperature fluctuation(3<<4, in Fig.4) classified into the range of

Figure 6 Time variations of temperature at all points
and velocity components of the anemometer (S2)55

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

development of thermal boundary layers is very various.
Thermaldriven

flows

were

locallydeveloped

everywhere on the wall and some of them merge to
become longer while others were dispersed due to the
disturbances of forced convection.

3.7. Conclusion
Figure 7 Relationship between Nux and Rax

The thermal boundary layers developed along abuilding wall were measured with thermocouples,

0.020

ultrasonic anemometers, and an infrared camera. The
○ 60

ΔT

○ 55

ΔT<60

○ 50

ΔT<55

○ ΔT<50

remarkable results are summarized as follows:Integral area
0.010
0.015

(a)

In thermal driven flow, three layers can becharacterized by the influence from naturalconvection from temperature difference and forced

0.005

convection from the outer layer.
(b)Heat transfer rate, Nux , decreases with Rax due to

0.000

forced convection in the outer layer.
(c)
0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

The thickness of temperature profiles depends one and just one measured T .

e

Figure 8 Relationship between integral area versus e

Reference
the forced convection gets stronger, T

becomes[1] Nakamura, Y. and Oke, T. R. : Wind temperature andsmaller. Since T is almost equivalent to Rax , it leads

stability conditions in an east-west oriented urban canyon,

to larger heat flux.

Atmos.Environ., Vol122, No12, pp. 2691-2700, 1988
[2] Kanda, M., Kasamatsu, F., and Moriwaki, R. : Spatial

3.5. Thickness of thermal boundary layers

variability of turbulent fluxes and temperature profiles in an

The integral area of a temperature profile wasurban roughness layer, Boundary-Layer Meteorology,

introduced as an index of thickness of thermal boundary

Vol.121, pp.339-350, 2006

layer. The relationship between the integral area and e[3] Tsuji, T. and Nagano, Y. : Characteristics of a turbulence

can be categorized with color coding of the data for eachnatural convection boundary layer along a vertical flat plate,

range of T . As seen in Fig.8, the integral area

Int. J. Heat and Fluid flow, Vol.31, No8, pp. 1723-1734,

increases with T , whereas it decreases by e . In

1988

addition,T andehave a strong correlation.

[4] Hattori, Y., Tsuji, T., Nagano, Y., Tanaka, N. :Therefore, this figure will be useful for roughly modelingCharacteristics of turbulent combined-convection boundary

the normalized temperature profiles in terms of e andlayer along a vertical heated plate, Int. J. Heat and Fluid

just one measured T .

flow, Vol.21, pp.520-525, 2000
[5] Hattori, Y., Tsuji, T., Nagano, Y., Tanaka, N.: Turbulence

3.6. Images of thermal boundary layers

characteristics of natural-convection boundary layer in air

Through consecutive thermal images captured by thealong a vertical plate heated at high temperatures , Int. J.

infrared camera, it was observed that the scale of theHeat and Fluid flow, Vol. 27, pp.445-455, 2006

56

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TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Analysis of International Freight Transportation Characteristics ofLandlocked Countries
Student Number: 09M18025

Name: Hironori KAWAI

Supervisor: Shinya HANAOKA

国際貨物輸送における内陸国の特性分析
河合弘則

本研究は内陸国間に存在する差異に着目し、貿易と内陸貨物輸送において存在するボトル
ネックの特性を抽出する事を目的としている。貿易に関しては、貨物が内陸国に隣接する沿岸国の港湾を出るまでのプロセスにおける問題点を取り扱い、内陸貨物輸送に関しては、内陸国の輸送時間を決定づけている要因の分析を行った。さらに、そこで明らかとなった要因をもとに、各内陸国の輸送環境の特性について考察を行った。

1. INTRODUCTION

Time and Cost to export
7000

There are 43 landlocked countries (LLCs) in theworld. And United Nations reported 31 of them arebeing called Landlocked Developing Countries(LLDCs). One of the factors of their harsh economic
conditions is considered to be difficulty in trade. As
LLCs have no their own seaport, they have to cross atleast one border when they trade by maritime transport
and their access to the seaports of transit neighborcountries (TC). In case that TC doesn’t have efficienttransit trade facilities or doesn’t allow LLC to conduct
cross-border trade without frequent cargo inspection or
cumbersome procedures, in addition to inlandtransportation with long distance to the seaport, LLC
suffers from long transportation time, hightransportation cost. Consequently some LLCs sufferfrom high trade cost and slow economic development.
Although LLCs have a lot of disadvantages regarding
trade and economic development, some LLCs in Europe
such as Andorra, Austria, Czech Republic, Hungary,
Liechtenstein, Luxemburg, San Marino, Slovakia andSwitzerland, are classified into high income countries
and Azerbaijan, Belarus, Kosovo, Macedonia, Moldova,
Serbia and Kazakhstan are upper middle income
countries (definition of World Bank). This fact can
prove that LLCs have chance to realize economic
development, if they are under certain environment.
And from the fact that some LLCs are high income
countries and others LLCs are developing country,
homogeneity among LLCs cannot be claimed. In fact,
each LLC has characteristics.
As also will be noted from Figure.1, although LLCs
basically suffer from high export cost and long export
time, but data of LLCs are quite variable, stateddifferently, trade environment are quite different from
LLCs to LLCs. And some other conditions such as
GDP per capita and trading partners are quite different
in each LLC.

6000

Cost to export

5000
4000

Coastal Countries

3000

Landlocked Countries
2000

1000
0
0

10

20

30

40

50

60

70

80

90

Time to export

Fig. 1 Total Cost and Time to Trade
Source: World Bank, Trading across border 2011

2. RESEARCH OBJECTIVE AND
FRAMEWORK
Some researches have proved disadvantages of LLCs.
For instance, Limao and Venables (2001) stated thatbeing landlocked raises transport costs by around 50%(for the median landlocked country compared to themedian coastal economy). Radelet and Sachs (1998)
proved that transport and insurance costs are twice ashigh for landlocked countries as coastal countries andthey demonstrate that there is a link between transport
costs and economic growth. And some other papers
have demonstrated the impact of being landlocked on
trade, transport cost and economic growth. But there is
no previous research which focuses on disparity among
LLCs. So this paper analyses characteristics of each
LLC.
This thesis focuses on disparity among landlocked
countries (LLC) and analyzes the characteristics ofthem. Analyses are conducted related to international
trade and freight transportation of LLC by using Doingbusiness data 2011 (World bank) covering the process
between the vessel’s arrival at the port of LLC’s transit
neighbor countries (TC) and the cargo’s delivery at thewarehouse in LLCs (for import). And this study
consists of four analyses.

57Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

inland transportation and are enforced to havecumbersome documentation. Although Central African
Republic, Zimbabwe, Chad, Niger (in Cluster 5, 6)
don’t have physical constraint, inefficient bureaucracy,
poor infrastructure, conflict in neighbor countries andother factors make them in the difficult condition.

Characteristics analysis of bottlenecks of LLCs’
trade procedures
II. Determinants of inland transportation of LLCs
III. Characteristics analysis of inland transportation of
LLCs
IV. Case study focus on Bhutan, Nepal, Chad andCentral African RepublicI.

4. DETERMINANTS OF
INLAND TRANSPORTATION TIME OF LLCs

3. CHARACTERISTICS OF BOTTLENECKS
OF LLCs’TRADE PROCEDURES
6-8hours

By using cluster analysis (Ward method), bottlenecksof procedures required for trade such as documents
preparation, customs clearance and technical control,
and ports and terminal handling are extracted to define
which processes have significant problems for each
LLC. Although this analysis focused on export time andcost, import time and cost data, now the result using
export time are shown in Table 1 and Table 2.

UgandaKenya

Fig. 2 Sketch map of access to seaport (e.g Uganda→Kenya)

Table 1.Result of classification
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6

Inland transportation is one of the most serious
bottlenecks for LLCs. Take Uganda for example,
inland transportation time from its capital, Kampala to
Kenyan seaport, Mombasa (1187 km) takes 18 days,
while Austria, needs only 2 days to complete thetransport to the German port of Hamburg despite thedistance of 900 km. This difference is made by bordercrossing time (Uganda needs 6-8 hours to cross theborder), infrastructure quality, and some other factors.
Now factors which contribute to inland transportation
time (TT) for LLCs’ access to seaport is analyzed using
multiple regression analysis. Transport infrastructure(road and railway), country risk, distance to seaport,
and land form of LLCs and TCs are incorporated inregression models as explanatory variable and theirimpact on freight transport are measured.

Botswana, Mali, Lesotho, Kosovo, Swaziland,
Hungary, Macedonia, Serbia
Burundi, Nepal, Paraguay, Uganda
Belarus, Slovakia, Bolivia, Czech Republic, Armenia,
Austria, Luxembourg, Switzerland
Bhutan, Rwanda, Malawi, Zambia, Moldova, Azerbaijan,
Ethiopia, Burkina Faso, Laos, Mongolia
Niger, Uzbekistan
Central African Republic, Zimbabwe,
Chad, Kyrgyzstan

Table 2.Characteristics of each cluster
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Cluster6

Good condition
Documents,
Inland, Customs
Document
All
Port

Bottlenecks

Document
Document, Port, Inland
Document, Inland

4.1 EXPLANATORY VARIABLES

LLCs in Cluster 3 have few bottlenecks in trade
procedures. Bolivia is exceptional of this group where
mostly European countries occupy. Cluster 2 is thegroup in the second best condition among LLCs.
Botswana, Mali Lesotho, and Swaziland in Sub SaharanRegion are classified into this group except for
European countries. Countries in Cluster 2, Burundi,
Nepal, Paraguay, and Uganda suffer from long inlandtransportation time but documentation is relatively
efficient. Cluster 4 consists of African countries andAsian countries, and their bottleneck is documentation..
LLCs in Cluster 5,6 and Afghanistan, Kazakhstan,
Tajikistan (outlier in this analysis). These African andCentral Asian countries have a lot of bottlenecks. Their
trade procedures are most inefficient among LLCs.
Especially time to documents preparation and Inlandtransportation are serious problems. Many of Central
Asian countries have to cross more than two borders
and are far from the seaport, so they cannot avoid long

1) Transport infrastructure
As LLCs depend on more than one TC for theiroverseas exports and imports by maritime
transportation, in the process of their access to seaport,
transport infrastructure quality of LLCs (LLCInfra) andTC (TCInfra) can be significant. Each LLCs' and TCs’
Infrastructure quality are measured by composite
variables constructed from three variables (kilometersof road, kilometers of paved road, kilometers of rail
(each per square kilometer of country data)) by
Principal Component Analysis (PCA), because thesemeasures are highly correlated among themselves.
2) Country risk
Efficiency in bureaucracies of LLCs and TCs are
considered to exert an influence on border-crossing
time and frequency of cargo inspection.

58

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TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

This analysis uses data named “Euromoney Country
Risk” which shows efficiency of bureaucracies. In thisdatabase, economists and heads of researchers
worldwide were asked to rate each country across six
categories:
government
stability,
regulatoryenvironment, non-payment of loans / dividends / traderelated finance, non-preparation of capital, corruption
perception and information access / transparency. And
higher the scores, more efficient bureaucracies thecountries have.

LLC forest: Percentage of forest area in total land areaof LLCs
TC forest: Percentage of forest area in total land area ofTCs
4.3 ESTIMATION RESULT
Table 4. Estimation results
Dependent Variable: Inland transportation time ( TT )1)

ConstantDist
LLC Infra

TC CR
Latitude
LLC forest
TC forest

t-value

Coefficientt-value

Coefficientt-value

Coefficientt-value

5.031
0.008

0.971
4.787

14.100
0.007

2.432
4.620

11.950
0.008

1.345
3.944

12.05
0.01

3.39
5.53

-2.572-1.559-3.299-1.994

-0.71

-3.62

-0.487-1.472-0.001-0.558-0.001-0.001

0.000

0.067

-0.037-0.456-0.017-0.203-0.023-0.325-0.028-0.331

0.006

0.049

-0.026-0.197-0.004-0.039

0.002

0.534

0.616

0.508

37

37

37

37

0.644850906
37

Based on the factors confirmed by regression analysis
in the last part, characteristics of inland transportation
environment are extracted by cluster analysis again.
Transport infrastructure, country risk of LLC and TC
and distance to seaport are used as variables.

TCCR

Table 5. Result of classification
1
Cluster1

1) TT = f (Dist, LLCInfra, Latitude,
TCforest)
2) TT = f (Dist, TCInfra, Latitude,
TCforest)
3) TT = f (Dist, LLCCR, Latitude,
TCforest)
4) TT = f (Dist, TCCR, Latitude,
TCforest)

0.012

0.512

5. CHARACTERISTICS OF INLAND
TRANSPORTATION OF LLCs

Table 3. Coefficient of correlation

0.925718
10.795664 0.769989
1
0.679597 0.744667 0.644349-3.390-0.347

From the result, Distance to seaport from the capitalsof LLCs to seaport in TCs (Dist), transport
infrastructure quality of LLCs (LLCInfra) and TC(TCInfra), country risk of LLCs (LLCCR) and TCs(TCCR) are confirmed to be the factors influencing on
inland transportation of LLCs. But Latitude of capitals
in LLCs (Latitude), Forest area (LLC forest, TC forest)
could not be significant variables. This result assumes
that data used for land form were not appropriate orland form doesn’t affect transportation time.

The variables of LLCInfra, TCInfra, LLCCR, TCCR are
highly correlated among themselves, so these variables
are separately incorporated into regression models to
avoid multicollinearity.

LLCCR-0.733-0.001

Adjusted R2
Observations

4.2 REGRESSION MODELSTCInfra5)

4)

Coefficient

0.773
4.889

LLC CR

Distance to seaport from the capitals of LLCs toseaport (Dist) data of which are quoted from“Landlocked Developing Countries Fact and Figures”(by United Nations) are incorporated into regression
models, since importance of distance for transport time
is obvious.
And as many of LLCs are located in mountainous
areas, there is a possibility that some LLCs’ freighttransportation suffer from difficulty in land form.
Latitude of capitals in LLCs (Latitude) and Forest area
(percentage of land area) (LLC forest, TC forest) areselected.

LLCInfra
1

3)

t-value

4.062
0.009

TC Infra

3) Geographical features

LLCInfra
TCInfra
LLCCR
TCCR

2)

Coefficient

LLCforest,
Cluster2

LLCforest,

Cluster3

LLCforest,

Cluster4

LLCforest,

Cluster5

BurkinaFaso, Mali, Niger, Uganda, Ethiopia,
Malawi, Paraguay, Afghanistan, Mongolia,
Zambia, Burundi, CentralAfricanRepublic, Chad,
RwandaArmenia, Azerbaijan, Bhutan, Belarus,
Macedonia, Moldova
Laos, Nepal, Zimbabwe, Lesotho, Swaziland,
Bolivia, Botswana
Kyrgyzstan, Tajikistan, Uzbekistan, Kazakhstan
Austria, Switzerland, CzechRepublic,
Luxembourg, Hungary, Slovakia

Table 6. Characteristics of each claster

TT: Inland transportation time from the capital of LLCs
to seaports of TCs at the average of transportation timefor exporting and importing
Dist: Closest distance from the capital of LLCs toseaports of TCs
LLC Infra: Infrastructure index of LLCs
TC Infra: Infrastructure index of TCs
LLCCR: Country risk of LLCs
TCCR: Country risk of TCs
Latitude: Latitude of LLC’s capitals

Good condition

59

Bottleneck

LLCInfra, TCInfra,
LLCCR,TCCR

Cluster1
Cluster2
Cluster3
Cluster4

Dist,
Dist, TCCR

Cluster5

LLCInfra,
TCInfra,
LLCCR,TCCR

LLCInfra, TCInfra, LLCCR
Dist,LLCInfra, TCInfra,
LLCCR,TCCR

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Cluster 1 and 4 are in difficult condition among
LLCs. Cluster 1 consists of African and Asian countries,
especially infrastructure developments of them are theworst. In Cluster 4, all countries are Central Asian
countries. They suffer from long distance to seaport,
bad condition of TCs and have poor infrastructure andinefficient bureaucracies.
Countries in Cluster 5 are in the best condition
among LLCs. They all are high income European
countries and have few bottlenecks on freighttransportation. Cluster 2 consists of second best LLCs.
Except for Bhutan, middle income European countries
are in this group. Cluster 3 is exceptional considering
region and their transport environment are relatively ingood condition among landlocked developing countries.
Basically LLCs are classified by region in thisanalysis, but there are some exceptions. Difference
between Paraguay (Cluster 1) and Bolivia (Cluster3) inSouth America is due to the distance to seaport andcountry risk of their TCs. These factors also make East
Asian countries, Mongolia (Cluster 1) and Laos
(Cluster 2) classified in different clusters. The reason
why Bhutan (Cluster 2) and Nepal (Cluster 3) are in the
different group is their own country risk.

6.2 CHAD, CENTRAL AFRICAN REPUBLIC

6.

7. CONCLUSION

Chad and Central African Republic (CAR) are one ofthe poorest countries in the world. Their internationaltransittransport
costs
are
the
highest.
(CAR:3900$/TEU,24days, Chad:4405$/TEU, 30days)
It’s because of their poor domestic infrastructure andpolitical conditions and they suffer from civil wars inmajor transit countries.
Chad has no choice but to go through Cameroon evenwhen transit seaport is in Nigeria due to geographicalrestriction. Although CAR has two transit countries,
Cameroon and Congo Republic, transit route throughCongo Republic is time-consuming. So they mainly
depend on Cameroon when they conduct transit trade.
But even in Cameroon, transport environment has a lotof problems such as frequent cargo inspection, lackingof efficient border facilities and customs proceduresystem and another problem is that railway of
Cameroon are not linked to the border with Chad and
CAR. In addition to the problems of inlandtransportation, long dwelling time at Douala port in
Cameroon is serious. They need approximately 30days
until the cargo left the Douala.

CASE STUDYThis paper showed characteristics of bottlenecks of
LLC’s trade procedures and inland transportation,
determinants of long inland transportation time andactual condition of some LLCs. Characteristics of each
LLC are not only determined by region they arelocated in but also by each infrastructure level,bureaucracy efficiency, distance to seaport,
relationship and agreement with neighbors, conditionsof TCs and other factors.

As analyses above-mentioned are based on secondary
data of each country, actual conditions of freighttransportation are hard to be seen. Therefore case study
has to be conducted. This study covers Bhutan, Nepal,
Chad, Central African Republic and now focuses onagreement with TCs.
6.1 BHUTAN, NEPAL
Although both Bhutan and Nepal are located in South
Asia and have common transit country, India, transport
environment of Bhutan is better than that of Nepal
according to the analysis in the last part. And in fact,
inland transportation time for exporting of Bhutan is
13days, while that of Nepal is 19days. Now theirdisparity is analyzed.
There is difference in their own country risk anddistance to Kolkata (Indian seaport), but in detail majordifference is linked to each transit agreement with India.
Although they both signed transit trade agreement with
India, there is difference of its history and content. India
allows Bhutanese transit trade to be conducted under
the supervision of Bhutanese customs, yielding little
administrative hassle. In contrast, Nepal’s relations with
India have frequently been strained, with India often
seen to have more influence in the negotiation oftreaties and disputes. (Faye et al (2004))
This case shows relationship between LLCs and TCs
influences on inland transportation especially in case
LLCs don’t have more than one transit country.

Reference
1)

2)

3)

4)

5)

60

Euromoney Country Risk
Onlineavailable:http://www.euromoney.com/Article/267
5673/Country-risk-Methodology.html
Limao, N, and Venables AJ. (2001) ” Infrastructure,geographical disadvantage and traosport costs. “WorldBank Economic Review 15(3): 451-479.
Michael L. Faye; John W. McArthur; Jeffrey D. Sachs;
Thomas Snow(2004), “The Challenges Facing
Landlocked Developing Countries”, Journal of Human
Development and Capabilities, Volume 5, Issue Number
1 March 2004 , pages 31 - 68
Raballand, G., (2003) ”Determinants of the negative
impact of being landlocked on trade: an empiricalinvestigation through the Central Asian case“,Comparative Economic Studies 45: 520–536.
World Bank Doing Business 2011
Online available: http://www.doingbusiness.org/

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

OPERATION AND MANAGEMENT OF WIND POWER PLANTS
UNDER RISK OF FAILURE
Student Number: 09M18048

Name: Yoshihiro Sato

Supervisor: Naoya Abe

故障リスク下にある風力発電設備の運用と管理佐藤 好浩
本論文では、風力発電運営に伴う不確実性を考慮にいれた運営シミュレーションを行い、予想される事業収益の分布を示した。想定した不確実性として、ワイブル分布による風速の出現、1 基当たり年間故障事故回数、1 回あたり稼働停止時間を入力した。シミュレーションでは、故障リスク、稼働停止の長期化、包括契約の利点についてシミュ
レーション結果を基に考察した。

potential of wind power [1]. It mentioned thatcosts such as capital, land, O&M (Operationand Management), and decommission are
required to calculate the cost of generation ofelectricity also mentioned that internationalcosts of generation of electricity of wind power
plants are different from country by country,
due to wind condition, transportation cost andsubsidies. Khalfallah et al (2007) suggested thattheoretical method by assuming power curve asa linear function [2]. NEDO (New Energy
Development Organization), one of theJapanese government research funding
agencies, conducted a comprehensive windspeed survey in Japan from 1997 to 2000 [3].
To examine the profitability of wind
power plants under risk of failures, their failure
data are indispensable. However, these data
have been only available qualitatively and notaccumulated enough quantitatively in Japan,
making the statistical study of failure events
associated with wind power plants difficult.
In this area, Abe and Sato (2009)[4]
studied the relationship between failure eventsand its properties statistically for the first time
based on the failure data from METI (Ministerof Economy, Trade and Industry) Hokkaido
questionnaire survey [5] and NEDO (New
Energy Development Organization) research[6].
Its results demonstrate that the larger the output
size is, the smaller the failure risk is. Windpower plants located in coastal area face higher
risk than the ones located in inland area. The
present study is the extension of the previous
study and simulated profitability of wind power
plants based on the failure data with Monte
Carlo simulation.

1. Introduction
Suttsu-town, a local municipality in
Hokkaido with the population size 3,744 (2005
Census), earns 85 million yen from wind power
plants annually, partially due to the preferable
wind conditions there. They pay back the profit
to citizens in the forms of scholarship to local
students and subsidies to local community
activities. By contrast, Jyoetsu-city, anotherlocal government in Niigata prefecture, loses
about 35 million yen from wind power plants
annually. According to NHK, about 60% of
wind power plants operated by Japanese local
government are suffering budget deficit.
The reasons for the latter case can be:(1) longer downtime of plants than theyplanned, (2) inadequate maintenance works,
and/or (3) less generation of electricity thaninitially expected, partially due to inadequate
feasibility study.
A maintenance expert from NEIC Japan,
which is a wind turbine parts providing
company, pointed out that some small wind
power plant operators cannot arrange adequate
maintenance services because those expenses
are too expensive and those operators are often
in deficient1. On the other hand, Suttsu-town
is currently operating five 1,990 kW classturbines and has paid serious attentions to themaintenance of the plants by making a
package-deal type contact with a privateengineering firm. It is becoming clear thatmaintenance works are crucial for the operationof wind power plants.

2. Review of existing studies
Milborrow (1994) studied economic
1 Interview to a NEIC Japan expert was held on Oct,28, 2010.

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TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

electricity ignoring failures and t is annual
downtime of the plant.

3. The Study Framework
3.1 Weibull distribution
The present study applied Weibull
distribution to simulate wind speed. In
probability theory and statistics, the Weibull
distribution is a continuous probability
distribution. The probability density function ofa Weibull random variable v is

3.3 Discount Cash Flow analysis
Discounted cash flow (DCF) analysis
is a method of valuing a project, company, orasset using the concepts of the time value ofmoney. All future cash flows are estimated anddiscounted to give their present values (PVs) –The sum of all the future cash flows, indicating
both incoming and outgoing, indicated that thenet present value (NPV), which represented thevalue of the cash flows.

(3.1),where v is wind speed, k is shape parameterand c is scale parameter. The mean of a Weibull
random variable can be expressed as follow(3.4),where NPV is net present value, N is the
project term, NBn is net benefit in nth year and r
is discount rate. The present study assumed thediscount rate to be 2%, which thought to be thesame as interest rate of local government bond.
The rate of the profitability of wind powerbusiness should be more preferable at least than
the interest rate of the bond.
IndeedSuttsu-town issued local bonds when they
constructed the wind power plants to financethe plants.

(3.2)

Γ is gamma function. NEDO had researchedabout wind speed in Japan. The present study
referenced the report to decide the shapeparameter and mean of wind speed. Scaleparameter is calculated by expression (3.2).
3.2 Generating electricity estimates
The output of a wind turbine is a function ofwind speed. Because of mechanical friction,wind turbine cannot generate electricity whenwind speed is too slow. Minimum wind speed
which generates electricity is called as cut-in
wind speed. If wind speed is too fast, theturbine cannot also generate electricity becauseof the limitation of the turbine. Maximum windspeed which generates electricity is called as
cut-out wind speed. Minimum wind speed
which generates rated power called rated windspeed. Power curve, a relationship between
wind speed and output, was assumed a simple
linear power curve suggested by Khalfallah(2007) [2] as follow:

3.4 Monte Carlo methods
Monte Carlo method is a class ofcomputational algorithms that rely on repeatedrandom sampling to compute their results.
Monte Carlo method results in the distributionof the calculated value. The present study input
Weibull distribution, frequency distribution of
ANA (Annual Numbers of Accident per
turbine) and frequency distribution of DPA (Downtime Per Accident) as random variables.
Weibull parameters are based on NEDO
research [3]. Summary of ANA and DPA data
are presented in Table.1. Constant parameters
are introduced in Table.2.Project period are
indicated in Table.3.

(3.3),where Pr is rated power, v is wind speed, vin
is cut-in wind speed and vr is rated wind speed.
The present study assumed that
Weibull distribution provided an average windspeed per day, and power curve is estimatedone day generating electricity. Annual
generating electricity ignoring failure is thesum of one day generating electricity. To
consider generation loss, annual generating
electricity is the product of annual generating
electricity ignoring failure and availability ratio
as follow expression

Table.1 Summary of random variables
ANA(annual
DPA(days)numbers per
turbine)Sample size
Term
Mean
Std.Dev
MinMax

277 turbines
From 2004 to 2008
0.15
22.4
0.25
59.0
0
3
5
365

The present study considered fitting thefrequency distribution of ANA to probability
distribution such as Poisson distribution. But
the frequency distribution cannot be fitted.

(3.4),where Pannual is annual generating electricity
considering failures, P is annual generating
62

Technical Report of International Development Engineering

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categorized as large (1,000kW-), middle(500-1,000kW) and small (-500kW). Location
is categorized as coastal area and inland area.
Aomori prefecture and Koushinetsu area werechosen as local region. As a result, large wind
power plants are more profitable than small
plants. Especially, the fact that the maintenancecost rate of the small plants always supposed tobe higher than the large ones was demonstrated.
(Fig.2)

Table.2 list of constant parameters
Numbers of Periodic Check
2 times per year
Days of Periodic check
5 days per year
Cost of Periodic check2.5 million yen per check
Construction cost
Over 1000kW
200 thousand yen per kW
Less than1000kW
300 thousand yen per kW
Numbers of Labors
1 person per 4 turbines
Annual salary
6 million yen
Price of electricity
9 yen/kWh
Subsidies
30% of total construction cost
Cost to recovery
1.5 million yen per accident
Discount rate/ Debt service term
2% / 10 years
Decommission cost
100 million yen

Decommissioning

Maintenance

Table.3 Project period of wind power plants
Period (total 19 years)
Construction (1st year )Operation(2nd ~18th year)

Incoming
SubsidiesElectricity
selling

Outgoing
Construction
Debt service
Maintenance
Labor
Decommission

Decommission (19th year)◆◆

Interest

The present study cannot confirm thedifference of profitability of locations and local
region.
4.3 Evaluation of maintenance costs
The plants operating more than one
turbine will reduce deficient risks because
single wind turbine plants cannot earn revenues
when an accident occurred. The simulation
revealed relationship between numbers ofturbines and the profitability. As a consequence,
the more numbers of turbines are, the more
profitable wind power plants are. Four 900kW
turbines should be constructed at least with
Probability to gain positive profit over 90%.
Longer downtime will also be
supposed to be a critical issue of the operation.
The simulation revealed relationship between
downtime per accident and the profitability.
(Fig.4)The simulation supposed that every
accident interval to recovery is constant.
Horizontal axis indicates the numbers of days
to recovery. Vertical axis indicates theprobability of annual benefit in surplus. If
downtime is longer than 3 months, eight
900kW turbines should be constructed to earn
profit with probability over 90%.

4.1 Evaluation of model reliability
To evaluate the reliability of the model,
simulated values of the model were comparedwith three operational results such as Suttsu
town, Tomamae-town and Jyoetsu-city.
Suttsu-town case is a one profitable case. While,
Jyoetsu-city case is an unprofitable one.
Tomamae town is break even case. The results
are shown in Fig.1.
Tomamae town

Labor

Fig.2 Detail total project cost by output size.

4. Results

Jyoetsu-shi◆

Construction

Actual cases

Suttsu town◆

Million yen

Fig.1 a range of simulated annual profit
Although Suttsu-town actual result
was more profitable than a range of simulated
values, the simulated results and actual cases
are always same order of magnitude. It was
confirmed that the model can simulate both
success case and failure case.
4.2 Evaluation in condition of risk
The present study simulated the
profitability by categorized risk factors, output
size, locations and local region. Output size is63Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

small plants’ maintenance cost has a higher rate
than the large ones.
(b) When downtime is always longer than 3months, eight 900kW turbines are necessary atleast to earn profit with probability over 90%.
(c) Package contract can increase about 105
thousand yen of average annual profit and 3%of the Probability to gain positive profit
respectively.
The results suggests that local
governments are required several wind turbinesof mega watt class at least to earn profit stably.
In addition, long downtime more than several
monthsreducestheprofitability
atimpermissible level. Small wind power plants
have great risk to gain profit. Those local
governments that are operating wind power
plants can need to have both adequate number
and size of the plants as well as suitable
maintenance system; otherwise they may needto consider shutting down their plants tominimize the loses.

Probability to gain positive profit
Numbers of turbines

Days

Fig.4 Relationship between downtime and
the Probability to gain positive profit.
Package contract is a maintenancecontract adapted by Suttsu-town case, which
pay average maintenance cost annually. This
contract is beneficial because maintenance costis always constant regardless of the numbers ofaccident. Maintenance companies are given an
incentive for short recovery time. Commonly,
package contract determine upper limit ofrecovery time with backup fee. The simulation
revealed an advantage of the package contract.
The simulation supposed that every downtime
is two days and maintenance cost replace
average maintenance cost. As a result, Package
contract increase about 105 thousand yen of
average annual profit and 3% of the Probability
to gain positive profit.

References[1] D. Milborrow, ECONOMICS OF WIND
POWER AND COMPARISON, Renewable
Energy, Volume 5, pp.692-699, 1994
[2] M. G. Khalfallah, Aboelyazied M. Koliub,
Suggestions for improving wind turbines power
curves, The Ninth Arab International
Conference on Solar Energy, 2007

4.4 Sensitivity analysis
In the present study, cost parameters
are qualitatively determined by hearing
research and case studies. It remains possiblethat the model cannot simulate correct impactof the maintenance cost. Thus sensitivity
analysis was implemented by changing
recovery cost per accident (Fig.5).

[3] NEDO, Huuryoku Kaihatsu Field test
(Field test for wind energy development, theresearch of wind speed), 2000
[4] N. Abe. Y. Sato, “Survival Analysis of Wind
Power Plants”, Journal of JWEA, vol.92,
pp-98-103, 2009

Probability to gain positive profit[5] METI Hokkaido, Hokkaido ni okeru
Huuryoku Hatsuden no Genjyo to Kadai(Current situation and issue of wind power in
Hokkaido), 2004 -2008.
[6]NEDO, Huuryoku Hatsuden Riyouritsu
Koujyou Iinkai oyobi Koshou Jiko tou Chousa
Iinkai (upgrading availability of wind power
council and research report of the accident),
2004-2008

Numbers of turbines

Fig.5 Profitability impact of recovery cost
As a result, the profitability is decreased withan increase in recovery cost.

5. Conclusions
The present study simulated profit andloss of wind power plants considering failurerisks. The following conclusions wereconfirmed.
(a) Large wind power plants are more
profitable than small ones. It was proven that64

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Adhesion between elastic beam and rigid bodyStudent Number : 09M18054

Name : Yu SEKIGUCHI

Supervisor : Kunio TAKAHASHI

弾性梁と剛体の間に働く凝着力
関口 悠
弾性梁側面が剛体に凝着する際に、弾性梁に働く凝着力を理論的に求め、実験により検証した。弾性梁の剛体への接触を線接触と面接触の二つに分け、弾性梁の変形と系全体のエネルギーを考慮することで、弾性梁に働く力を求めた。また凝
着力が凝着仕事に関するパラメータにより変化することを示した。静的条件と動的条件を仮定した実験を行い、理論に沿う結果を得るとともに、理論に沿わない範囲における原因について考察をした。

1. Introduction

theoretically and experimentally.

Adhesion is used by some insects like gecko togrip and climb walls or move on ceiling. Gecko has
a lot of micro-nano-hair on its foot, which arecalled seta and spatula. These micro-nano-hairs
enable the gecko to grip surface in order for thehairs to adhere to the surface [1]. The hairs
deform to adhere to the surface even if it has somesurface roughness. And also they can be detachedfrom the surface easily because of the hairs’
structural feature.
An elastic beam with an adhesive tip is used toexpress a mechanism of gecko’s foot hair [2]. Thismodel has a limited adhesive area. Gecko’s foothair is also assumed to be a tape and Kendall’s
theory of thin-film peeling [3] is used to express
the adhesion mechanism [4].This theory considers
the change of the adhered area but not the
deformation of the hair.
In the present study, an elastic beam, which has
adhesive beam side, is assumed. Therefore boththe flexible adhered area and the deformation are
considered. The adhesion force between theelastic beam and a rigid body is obtained

2. ModelFigure 1 shows a model of adhesion between anelastic beam and a rigid body considered in thisstudy. The elastic cantilever beam, which has thedimension of length L, thickness H and width W,
contacts to the rigid body at angle  (see Fig.2).
When approaching the rigid body, the elastic
beam firstly contacts to the rigid body at the edgeof the elastic beam. This contact is called line
contact. In loading process, the rigid body presses
the edge and the elastic beam starts deforming.
Consequently, the side surface of the elastic beamadheres to the rigid body. This contact is called
area contact. During unloading process, theadhered area keeps decreasing until the
separation occurs.

3. Force between elastic beam and rigid body
When the elastic beam contacts to the rigid body,the force occurs between the elastic beam and therigid body. As for the calculation, x-y axis is fixed
as shown in Fig. 3. The force is applied to theelastic beam with the strength, -fz cos, iny-direction. The force and the deformation in
x-direction are considered as negligibly small. The
deformation in y-direction is considered as smallenough to neglect the effect of bending for the
moment. The force is considered as concentrated
load at x=L for line contact and at x=l for area
contact.

Fig.1 Schematic illustration of the elastic
beam

3.1 Line Contact
At line contact, shear force and moment areconsidered to obtain the deformation of the beam
at x=L. By considering the relation among thedisplacement, the deformation and the length, theforce is obtained as

Fig.2 Model of adhesion between theelastic beam and rigid body.
65Technical Report of International Development Engineering

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The adhered area is equilibrium at the lowest
energy condition. Therefore the total energy is~
considered to obtain the stable length l . Totalenergy can be expressed as a sum of the elastic
energy and the loss in surface energy. From theminimal point of the total energy, the stable
length is obtained and the force is expressed as

Fig.3 Deformation of the elastic beam.
~~ cos 2  1  dfz
  1 ,sin 
4  sin 

(1)3
 3~W~  d~8 tan   sin ~ cos 2 


fz

3
sin 


~W~ d~3 1  12 tan  sin   1
2
 3~W~ 2 tan  



2


~W~ d~3 1  12 tan  sin   1
~for 3~W / tan   0 andwhere the force and the displacement are~~normalized as f z  f z /(12EI / L2 ) and d  d / L .
The contact changes into area contact when thegradient of the elastic beam at x=L becomes equal~
to that of the rigid body (i.e. d / sin   1 / 3 ).

3.2 Area Contact
At area contact, the force occurs at x=l and theelastic beam for l<x<L adheres to the rigid body.
Therefore shear force and moment for this range
is considered as 0. As the same manner with theline contact, the deformation at x=l is obtained
and the force is expressed as~~~sin   dl ,f z  ~32
l cos   sin  2 

(3)~ cos 2 
fz

sin 

1
(4)
~ 2
 d 

54

 sin  ~
for 3~W / tan   0 , where  is the work ofadhesion, which is required to separate a unit
area of adhered surfaces.  and W are normalized~as ~   /( 6EI / L3 ) and W  W / L .

(2)~where the length is normalized as l  l / L .

4. Adhesion ForceThe relation between the force and thedisplacement is shown in Fig. 4. During theloading process, the force moves from P1 to P2. At
P2, the contact changes into area contact. During
the unloading process, the force moves following
the curve A3A4 (B3B4 or C3C4). The curve A3A4,
B3B4 and C3C4 are plotted with different .
The adhesion force is the minimal force, i.e. themaximum tensile force. Therefore it is obtained as

Fig.4 Relation between force anddisplacement.

~ 2~
cos 2  1  3~W 
f adhesionsin 
6  tan  


for

(5)

~
3~W / tan   1 ,~3~W 1~cos 2 f adhesion


sin 
3 tan 6

(6)~
for 0.5  3~W / tan   1

Fig.5 Relation between adhesion force andadhesion parameter.

~and 0 for 3~W / tan  0.5 .
The relation between adhesion force and the66Technical Report of International Development Engineering

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considered. In the theory, the static condition isassumed. But the dynamic condition has to be
discussed for the applications. Therefore, bothconditions were considered. As for the elastic
beam, shock absorbing gel and silicone rubber
were used. As for the rigid body, aluminum alloy
with polished surface and glass plat were used. To
minimize the effect of friction between the elastic
beam and the rigid body, steal balls were set
under the rigid body. In the experiment, the force,
which was measured using electric scale, wasconsidered as the concentrated force fz in Fig. 2.
Young’s modulus of Gel and Silicone rubber were
measured using tensile testing machine for theelastic range and obtained as 1.3*105[Pa] and2.9*105[Pa]. Results of the dynamic condition are
shown in Figs. 7 and 8. Those of the static
condition are shown in Fig. 9.
Under the dynamic condition, the results
partially predicted the theory (Fig. 7(a) and (b),Fig. 8(a) and (b)). But also some differences exitedas well. One of them was observed at the initialunloading part. In the first part of the unloading
process, the adhered area was constant whereas it
decreases theoretically. Another difference isobserved at the releasing part. Theoreticallyreleasing occurs when the adhered area becomeszero. But experimentally it occurred before it

adhesion parameter is plotted in Fig.5.

5. Experiment
Experiments to measure the force between theelastic beam and the rigid body were carried out
by controlling the displacement using theequipments as shown in Fig.6. Two experiments,
dynamic condition and static condition, were

Fig.6 Equipments for the experiment.

(a)

(b)

(a)

(c)

(b)
Fig.8 Experimental results under the
dynamic condition using rubber.

Fig.7 Experimental results under the
dynamic condition using gel.

67

Technical Report of International Development Engineering

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using silicone rubber. The change of the force wasalmost same as the one using gel. But at initialunloading part and at releasing part, there aresome differences. At initial unloading part, thelength changed whereas it was constant with theresults using gel. At releasing part, the releasing
occurred with smaller adhered area than thatusing gel. These differences are considered to becaused by the material properties.
Under the static condition, the same tendency as
the dynamic condition was observed. But thework of adhesion  was different from that of the
dynamic condition. At the dynamic condition, 
was larger. Also there were differences between
gel and silicone rubber. This is considered to becaused by the material properties.
The maximum force at each experiment isplotted in Fig. 10 against the adhesion parameter.
~
Because l is constant at the first part of theunloading process, the maximum tensile force
observed near the cross point of the theoretical~force curve and the line of the constant l .
Therefore the result doesn’t follow the theoreticaladhesion force curve. Instead, it almost follows
the adhesion force which expresses the force at
the cross point and includes the constant~non-adhered length lconst. . It can be expressed as

became zero. It is considered that the assumptionof elastic beam is one of the reasons. Three
dimensional effect or the effect of large
deformation would exist. Therefore othermechanisms for the initial unloading part and thereleasing part were suggested in the experiment.
Figure 7 (c) shows the different result of dynamic
condition using gel. In this figure, the force waschanged as V-shape. This change was caused by
the difference of the initial unloading part. With~large non-adhered length l (i. e. small adheredarea), the force was changed like V-shape. Thislarge length was related to the displacement at
the start point of the unloading process. With
smaller displacement, the larger length was
obtained.
Figure 8 shows the result of dynamic condition

(a)

~
3~W
cos 2 1~
f adhesion
 ~
 ~2
sin 
3lconst. tan 6lconst.

(7)

and this is shown as the broken line in Fig.10.

6. Conclusion
The model of the adhesion between the elastic
beam and the rigid body is proposed to obtain theadhesion force theoretically. The line contact andthe area contact are introduced to obtain therelation between the force and the displacement.
Experiments to verify the relation between theforce and the displacement were carried out andthe results partially predicted the theory. The
different mechanisms were suggested at theinitial unloading part and the releasing part.

(b)
Fig.9 Experimental results under thestatic condition using (a)gel and (b) rubber.

References[1] K. Autumn, et al., Nature, 405, 681-685, 2000
[2] K.Takahashi, et al., J. adhesion adhesive, 26,
639-643, 2006
[3]K.Kendall, J. Phys. D : Appl. Phys., 8, 1449-1452,

Fig.10 Experimental results of theadhesion force.

1975[4] N. S. Pesika, et al., J. Adhesion, 83, 383-401, 200768

Technical Report of International Development Engineering

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System to make teaching materials
based on collective intelligence
Student number : 09M18060

Name : Yuhei Negishi

Supervisor : Yukihiko Yamashita

集合知に基づく教材作成システム
根岸 侑平

教育の不均衡の問題を解消するため，e ラーニングの重要性が増している。しかしながら，e ラーニ
ング教材の作成にコストがかかるという問題を抱えている。tobe システムは教材をテキストベースで記述することで教材作成コストを削減したが，XML の知識が必要なため容易に作成できない。また，教材を他のクリエイタと共同で作成することができず，１人あたりの作業負荷が大きい。一方，Wikipediaに代表される Wiki は世界中の多数の人々が参加することによって 1 人当たりの作業負荷を抑え，広範囲の情報を持続的に提供している。本研究では tobe システムに集合知の概念を取り入れ，ネットワー
ク上で多数の人々が XML の知識を使わずに１つの教材の作成に参加することを可能にするシステムの構築を行う。	
 1. Introduction

system, combining the tobe (Text
Oriented Bi-Stream Explanation) system
and Wiki system. The tobe system is atext-base learning system that makes
animation from tobeML (XML based
markup language) document. Then, its
combination with Wiki enables to createa tobeML document easily by a group via
a network.

After 1990, due to the innovations ofInformation Technology, utilization ofequipments like PC has become
widespread among general households.
Thennowadays,accordingtodevelopment of the Internet and WWW,
we can exchange information regardlessof time and place. And utilization of
e-Learning system gives a chance toeveryone to learn by using PC	
 anytime and anywhere. However the
problem is that to make teachingmaterials for e-Learning, not only
teaching methodology skill but also IT
skill
are
necessary.
Furthermore,correction and modification of materials
are difficult. Therefore, making materials
costs very much. On the other hand, Wikisystems give an opportunity to people
around the world to exchange theirinformation.
AccordingtoWikiphilosophy, applying Wiki does not need
any especial skill, and making materials
by a group is possible. However, Wikis
are static pages, thus Wikis cannot be aneffective learning system.
Then in this research, I propose a new

2. e-Learning
Electronic Learning (e-Learning) [1] isa type of technology supported education.
E-Learning is used interchangeably in awide variety of contexts. In companies, it
refers to the strategies that use thecompany network to deliver training
courses to employees. Lately in someuniversities, e-Learning is used to definea specific mode to attend a course orprograms of study where the students
rarely attend face-to-face for on-campus
access to educational facilities.

3. tobe system
The tobe system [2] is a system to69

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

are able to generate their own contents.
CI draws on this to enhance the social
poolofexisting
knowledge.
Communication tools enable humans tointeract, share, and collaborate with bothease and speed. With the development of
the Internet and its widespread use, theopportunity to contribute to communitybased knowledge forums, such as Wikisystems becomes possible.

Fig-1 tobe Systemchange the text-base data to animationfor e-learning (Fig-1). In the tobe system,
the program named “Expositor” does thisconversion. In this system all the data
are written in text, therefore the cost of
make teaching materials has been
reduced. Expositor makes a situation
similar to real classroom on the PC
screen. Expositor just can read the text
which is written in the tobeML language
defined originally for this system.
When a learner runs Expositor, awindow (Fig-2) appears on the screen.
As you can see in Fig.2, the tobe system
includes three main parts, a board, aprofessor with pointer, and a talk part(the part that professor explains by
voice).

4.1 Wiki
Wiki [4] comes from a Hawaiian word
“WikiWiki” for "fast". A wiki is a web
page or collection of web pages designed
to enable anyone who accesses it tocontribute or modify the content using asimplified markup language. Wikis areoften used to create collaborative
websites and to power community
websites. The collaborative encyclopedia
Wikipedia is one of the best-known wikis.
Wikis are used in business to provide
networks and knowledge management
systems.
Ward
Cunningham,thedeveloper of the first wiki software,
Wiki-Wiki Web, originally described it
as "the simplest online database that
could possibly works". Wiki is a piece ofserver software that allows users tofreely create and edit Web page content
using any Web browser. And Wikis have
simple text syntax for creating new
pages.

5. tobe system 2.0
Fig-2 Expositor

The tobe system enabled to maketeaching materials based on text andreduced the cost of making teachingmaterials. But to makethe tobeML
documents, the knowledge of XML isnecessary. Therefore making teachingmaterials is a bit difficult.
So I propose the tobe system 2.0. The
tobe system 2.0 is the tobe system basedon CI. In this system, creators can edit
the tobeML documents without theknowledge of XML using the tobeML

4. Collective intelligence
Collective intelligence (CI) [3] is ashared or group intelligence that emerges
from the collaboration and competitionof many individuals. CI can also be
defined as a form of networking enabled
by
the
rise
of
communications
technology, namely the Internet. Web 2.0
has enabled interactivity and thus, users
70Technical Report of International Development Engineering

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Fig-4 tobeML editor
Fig-3 tobe system 2.0

editor. Also, they can edit one tobeML
document in corporate with othercreators. Fig.3 illustrates the tobe system
2.0.
5.2 tobeML editor
The tobeML editor will enable to maketeaching
materials
withouttheknowledge of XML. Furthermore, many
people can edit a teaching material
because the editor can be used on theweb. The tobeML editor is composed of
two parts: the board part and the talkpart.
The board part is to edit the contents ofthe board. To describe the board part, theeditorhas two modes. One is the
explanation mode, and the other is thequestion mode.
The explanation mode (Fig-5) is todescribe the explanation. Creators can
set the font size or color, and layout oftext or images easily. The question mode
(Fig-6) is to describe the question. Whencreators describe the question and its
choices, tobeML text is generated
automatically.

Fig-5 Explanation mode

Fig-6 Question mode

The talk part is to describe the talk andaction of the professor and thecoordinate of his pointer.
Creators can determine the professor's
action by choosing from the pull downmenu (Fig-7), and the coordinate of his
pointer by clicking the point of the board(Fig-8).

71

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)(Smile and say) Today, I'll introduce to be system.
(Point at (10.15, 41.23) and say) To be system is

Fig-7 Selecting action(Point at (10.15, 41.23) and say) To be system isane-learning

systembasedoncollective

intelligence. Everyone can make its contents.
(Point at (10.15, 61.53) andsay) Tomakelectures, you have to use tobe ML.

Fig-9 Teaching material for experiment

7. Conclusion
In this research, I proposed the tobesystem 2.0 to make teaching materials
using CI. Also I developed the tobeML
editor and conducted an experiment toprove that creators can edit tobeML
document easily using the developed
tobeML editor.
As future works, I have to improve theinterface of tobeML editor for creators to
use it more effectively, and develop the
expositor which corresponds to tobeML
2.0.

Fig-8 Selecting the coordinate of the pointer6. Experiment
To evaluate the tobeML editor, I
conducted an experiment. I compared thetime for the subjects to describe a
tobeML document using the tobeML
editor and using only a text editor. Fig-9
shows the teaching material to be
described.
Table-2 shows the result of thisexperiment. Using the tobeML editor, thetime to create tobeML documents was
reduced.

References[1] What is e-Learning,
http://satt.jp/tech/e-learning.htm. (in Japanese)[2] Shoko Tobe: Explanation markup language based

Table-2 Result of experiment

on XML and the expositor (Master thesis, Tokyo
Institute of technology, in Japanese)

tobeML editor
Use
Not useAveraged time
238.8 sec
538.2 sec

[3] Wikipeida: http://en.wikipedia.org/wiki/[4] e-words: wiki, http://e-words.jp/w/Wiki.html

72Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

THE CHARACTERISTICS OF RAINFALL IN TOKYOWITH DETAILED URBAN GEOMETRIC DATA
Student Number: 09M18077

Name: Eriko FUKUMOTO

Supervisor: Manabu KANDA

詳細な都市幾何形状を考慮した東京都における降雨特性の把握
福本恵梨子
本論文の目的は東京都における①都市幾何形状の変遷の把握，②夏季における雨量の空間偏差の把握，および③上記①②の因果関係の把握である．詳細な 3 次元幾何形状データベースより，
東京都心部における幾何形状がここ十数年で顕著に変化したことを示した．また，9 年間の雨量データからは，夏季における雨量の空間偏差が 23 区内で増加したことを示した．空間解像度の高い雨量データからは，これまで明らかにされていなかった 23 区中心部での正の空間偏差を指摘し，都市化や海風収束との因果関係を示唆した．

The change in surface geometry was discussed interms of data obtained from a 3-dimensional urban geometric database for years 1996, 2001, and2009. Data for 1996 were based on data sourcesof the Land Use Survey Results from the Bureau
of Urban Development and data for 2001 and2009 were made by CAD Center Corp. Validation
was done to prove that comparing the two different sources is acceptable. Using these sources,building height 𝐻 was averaged for a mesh of
1/360° and other parameters, such as displacement
height 𝑑, were calculated. 𝑑 represents a virtualsurface due to buildings and was calculated basedon Macdonald et al. [4], where

1. Introduction
In cities, unusual weather, such as localized heavy
rain, has become a serious problem. Urbanizationis thought to be one of the causes for this unusual
weather. Past studies have shown that urban cities
indeed have some kind of an effect to the characteristics of precipitation. Fujibe [1] used AMeDAS data from 1979 to 1994 to show that positive anomaly of rainfall frequency lays in the urban central area. In addition, the possibility ofurban buildings causing upward flow, enhancing
the heavy rainfall was presented [2]. Moreover, anumerical simulation supported the effects of urban configuration on localized heavy rainfall [3].
Due to the economic change and new land
policy at around year 2000, Tokyo Metropolis has
undergone a major transformation. The construction of high-rise buildings accelerated in the central urban area complicating the city’s land surface. It is extremely crucial to clarify this transition and its effects on localized heavy rain. Although urbanization includes other factors, such as
anthropogenic heat, the very basis of them is theurban structure, and was the main focus in thisresearch.
The objectives of this study were: 1) to observe the transition of urban geometry, 2) to observe the spatial anomaly of rainfall during summer, and 3) to find some relationship between 1)
and 2). The greatest strength of this research isanalyzing data that has not been considered thatmuch, until now, which could lead to the possible
causes of localized heavy rain.

𝑑= 1 + 𝐴−𝜆𝑝 (𝜆𝑝 − 1)
𝐻(1)

and 𝐴 (= 4.43) is a constant. Affective elevation
was defined by adding 𝑑 to the actual elevation.
This affective elevation incorporates the urban
effects and was used throughout this research.
The difference between results from 1996 to2009 for 𝑑 is shown in Fig. 1. Figures for theother parameters are not shown here. However, allvalues showed positive difference in the urban
central areas such as in Chuo ward, near Shinjuku
station, and along the bay. This clearly shows theaccelerated growth of high-rise buildings in theurban central area and not throughout the Tokyo
Metropolis.
The actual elevation in the east side of the 23
wards showed values of less than 4 m, as in Fig.
2; whereas in the west area the values are larger
up to 50 m. When 𝑑 was added to this (Fig. 3),
affective elevation at the central areas increased.
However, the elevation gap in the middle of the
23 wards remained.

2. Transition of Urban GeometricParameters

3. Spatial Anomaly of Rainfall
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in 2008, shown in Fig. 8, displayed negative
anomaly along the shore and positive anomaly inthe western side. In addition, strong positive
anomaly at the center of the 23 wards and also in
Setagaya ward was observed.

The information on the amount of rainfall was
obtained from point precipitation data measured
by the River Bureau and from Radar Rainfall Information System called Amesh managed by the
Bureau of Sewerage. Compared to the 10 observation points of AMeDAS, the River Department
has 118 observation points and Amesh has a spatial resolution of 250 m. All rainfall data areshown in the form of anomaly calculated from themean annual/daily cumulative amount of rainfall
from 12:00 to 24:00 during summer time.

3.3. Spatial Anomaly of Localized Heavy
RainfallThe extraction conditions of localized heavy rain
were based on Shimoju et al. [3]: 1) hourly precipitation exceeds 15 mm within the 23 wards, 2)maximum temperature exceeds 30 degree-C, 3)
starting time of strong rain is between 12:00 and
21:00, 4) end time of strong rain is before 24:00,
and 5) are not affected by synoptic weather according to weather maps and weather satellites.
Using the River Department data localized
heavy rainfall days within the 9 years were analyzed (Fig. 5). The northwest area of the 23 wards
obviously had large positive anomaly. It is where
localized heavy rain events are commonly featured by the media and corresponds with the area
where positive anomaly increased in Fig. 4.
The spatial anomaly of localized heavy rainfall events in 2008 to 2010 was obtained by using
the new Amesh data (Fig. 9).There was strong
positive anomaly in Nerima ward and negative
anomaly along the shore and in the old town area,
which agrees with the other results formerly presented.

3.1. Transition of Rainfall Anomaly
The transition of the rainfall characteristics wasobserved by dividing the 9-year data from theRiver Department into first and second halves(Fig. 4). In the first half there was a relatively
clear division between positive and negative
anomaly at the middle of the Tokyo Metropolis.
Meanwhile, in the second half the border became
unclear and positive anomaly increased in the 23
wards. The possible causes of this transition will
be discussed further on.
3.2. Rainfall Anomaly with High Spatial
Resolution
It is crucially important to use Amesh data to see
the spatial anomaly at areas where River Department data could not cover due to its sparse distribution. The spatial anomaly for all rainfall events
Chuo

Shinjuku

Figure 1: Change in Displacement Height

Figure 2: Elevation

Figure 3: Affective Elevationfor 2009(a)Figure 4: Transition of the Anomaly of Rainfall; (a) 2000-2003 and (b) 2004-200874(b)Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

However, Suginami ward was covered with negative anomaly, which was not apparent until now.

as well [3].
4.3. Urban Characteristics and Anomaly ofRainfall

4. Relationship Between Urbanization and Rainfall Distribution

Fig. 8 (b) showed several interesting facts whencompared with other urban affects. First, thestrong positive anomaly in the center of the 23
wards fitted right inside the Yamanote Line railway (red line). This is due to the extreme increasein urban geometric parameters at the area below,
causing this area to become a stagnant zone of sea
breeze. Next, positive anomaly in the southernarea seemed to lie at the region circled in green,where Kampachi clouds frequently form [5].
One definite fact which can be said in comparison with urban geometric parameters is thatlarge positive anomaly did not occur where average building height or displacement height increased.

The spatial anomaly of rainfall was comparedwith affective elevation and other urban characteristics.
4.1. Affective Elevation and Anomaly ofRainfall from the River Department
As shown in Fig. 6, an exponential curve approximated the relationship between rainfall
anomaly and affective elevation for the first andsecond half of the whole Tokyo Metropolis,
which implies that the higher the affective elevation the more positive the anomaly of rainfall.
The slope became steeper in the second half,
which corresponds with Fig.4, where the positive
anomalies decreased in high-elevation regions
and increased in low-elevated urban areas.
Next, the same operation was done for the
results of only the 23 wards (Fig.7) in addition
with the anomaly of localized heavy rainfall. No
curve was found to fit well with the distribution.
However, there was a clear gap at around 10 to 20
m high, which divided the rainfall characteristic
into two types: positive anomaly at high affective
elevation and negative anomaly at low affective
elevation. The variability of the anomaly of localized heavy rainfall was larger than normal
rainfalls, which designates the complexity of localized heavy rain.

Figure 5: Spatial Distribution of Localized Heavy
Rainfall Anomaly

4.2. Affective Elevation and Anomaly ofRainfall from Amesh
Dependency on the elevation gap at around 10 to
20 m high was observed for the anomaly of all
rainfall days in 2008 (Fig. 8 (a)) as well, where
positive anomalies in the western and central areas were located at the high affective elevation region.
For the anomaly of localized heavy rainfalls
in years 2008 to 2010, an obvious relationshipwith affective elevation was not found (Fig.9).
However, the positive anomaly in Nerima ward
was located in the leeward region of where affective elevation increased drastically (red circle).
This could be due to the convergense of sea
breeze at the leeward region of increased 𝑑enforcing the amount of rainfall. This mechanism
has been observed through numerical simulation

Figure 6: Relationship between Anomaly and Affective Elevation (Tokyo Metropolis)
75Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

NerimaSuginami

Figure 9: Spatial Anomaly of Localized Heavy
Rainfall with Affective Elevation

Figure 7: Relationship between Anomaly and Affective Elevation (23 Wards)

3) Using the 9-year data of the River Department, positive anomaly found in the Tamaregion during the first 4 years shifted into the23 wards during the remaining years.
4) The radar rainfall data with high spatial resolution, called Amesh, showed that summertime rainfall in 2008 had strong positive
anomaly inside the Yamanote Line.
5) Nerima ward showed strong anomaly of
rainfall in cases of localized heavy rain due
to urbanization at the central areas.
6) Anomaly of rainfall was not positive in areas
where urban geometry increased.

(a)

Setagaya(b)
Reference[1] Fujibe, F., 1998, Spatial Anomalies andLong-Term Changes of Precipitation in Tokyo, Tenki, 45, 7-18.
[2] Takahashi, H., Nakamura, Y., and Suzuki, H.,
2008, Relation Between the Distribution ofSummertime Intense Rainfall Frequency andthe High-Rise Buildings in Tokyo Metropolitan Area, Abstract of Japan Geoscience Union Meeting, X156-002.
[3] Shimoju, R., Nakayoshi, M., and Kanda, M.,
2009, The Effects of Sea Breeze Convergence
Caused by Urban Geometry on Localized
Heavy Rainfall in Summer in Tokyo, Annual
Journal of Hydraulic Engineering, JSCE, 53,
277-282.
[4] Macdonald, R. W., Griffiths, R. F., and Hall,
D. J., 1998, An Improved Method for the Estimation of Surface Roughness of Obstacle
Arrays, Atmospheric Environment, 32,
1857-1864.
[5] Kanda, M., Inoue, Y., and Uno, I., 2000, Numerical Study on Kanpachi Street Cloud,Tenki, 47, 11-24.

Figure 8: Spatial Anomaly of Rainfall with (a)Affective Elevation and (b) Urban Characteristics

5. Conclusion
1) The urban geometry of Tokyo Metropolischanged drastically from 1996 to 2009.
2) The “affective elevation” increased at thecentral areas of the 23 wards, resulting into
an elevation gap between the east and west
regions.

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TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

A STUDY ON THE EFFECTS OF SEAWATER AS MIXING/CURING WATER
ON THE HYDRATION CHARACTERISTICS OF BLAST-FURNACE SLAG CEMENT
Student Number: 09M18083

Name: Daisuke FURUYA

Supervisor: Nobuaki OTSUKI

高炉セメントに対する海水練り・海水養生の影響
古谷 大輔
本研究は高炉セメントを海水で練混ぜ・養生した影響を、水和・空隙・強度・拡散係数・鉄筋腐食について
調査、関連付けて比較・検討を行ったものである。高炉セメントを海水で練混ぜた場合、スラグの反応率が増加し、空隙が緻密化、初期の圧縮強度が増加した。海水で養生した場合、28 日までの圧縮強度はわずかに増加した。鉄筋腐食はスラグ置換率 50％が最も腐食抵抗性が高く、これは細孔溶液の Cl/OH 比を抑えられた事が原因であると考えられる。

1. Introduction

water. The mixing / curing patterns are shown
table 1.
Table 1 Mixing/curing pattern and symbol

The use of blast furnace slag cement (BFS) is
advantageous from the viewpoint of the CO2
reduction. Blast furnace slag is generated from thesteel manufacturing process as a by-product.
Especially, BFS is used for construction of the port
and offshore structures because of its good
resistance against chloride attack.
In suchlocations, obtaining pure water is difficult.
Moreover, the demand for fresh water as drinking
water will be increased as population increases in
the future. These make it difficult to use purewater as construction material. Therefore, there isa demand to use seawater as mixing/curing waterfor BFS, and the demand will be growing.
However, the corrosion of steel in reinforced
concrete is a threat when using seawater due to a lotof chloride ion content in seawater.
The influence of an inorganic ion to thehydration of the ordinary portland cement (OPC)
has been studied. Also, the influence of alkali(ex : calcium hydroxide) as activator to BFS
hydration has been researched.
However,
researches about effect of seawater as mixed
inorganic compounds are scarce.
In this paper, the author explains therelationship between following effects ofmixing/curing with seawater
1. Effect to hydration characteristics
2. Effect to micropore structure
3. Effect to properties of cement composites
4. Effect to rebar corrosion

CuringdistilledsealedSeawater

Water(Fukan)distilled
Mixing

WW

WS

WF

SW

SS

SF

Water
Seawater

The replacement ratio of BFS to OPC are 50%,
70% and 90% that named as B50, B70 and B90
respectively.
Table 2 show the chemical composition of
cement and BFS respectively. Blaine specificsurface areas of BFS is 4200 cm2·g-1 .
Table 2 Chemical composition of OPC and BFS(mass%)
CaO

SiO2

Al2O3

FeO

MgO

SO3

OPC

63.96

20.38

5.18

3.37

1.88

1.97

BFS

43.9

32.4

13.9

-

6.4

1.6

The paste specimens of 10mm ×10mm × 4mm
were cured with water in bottle or sealed curingwith wrap after 24 hours pre-curing. At a set time,
the specimens were crushed to prepare the powder
specimens passing through the 150μm sieve for theeach chemical analysis.
Measurement items are XRD, TG-DTA and
DSC for hydration characteristics, mercury
intrusion porosimetry for micropore structure,compressive strength test, salt meter, pH meter for
properties and corrosion monitor for rebar corrosion.
Hydration and micropore structure analysis use
paste specimen. While properties and corrosion
analysis use mortal specimen.

2. Overview of the experiment
The experiment compares the effect on paste
and mortar specimens of mixing with seawater or
distilled water and curing with seawater or distilled
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fill the space of BFS cement particles. Therefore
pores of BFS cement become little in amount andfine in size.
Slag reaction ratio[%}

In the case of reaction ratio of BFS,
crystallization heat of Melilite with DSC at aheating rate of 20°C/min. was utilized(1. The
crystallization heat at approximately 950°C was
calculated with DSC, as shown in Fig. 1.
Thermogravimetric analysis (TGA, Mac
Science WS002) of the samples was performed at aheating rate of 10°C/min. under nitrogen from 20°C
and 1000°C.
The cumulative pore volume was measured
using a mercury porosimeter (CE INSTRUMENTS
Pascal240, pressure range: 0.1-200 MPa).
Pore solution for measurement of pH and Cl
content was prepared by JIS A 1154-2003 "Methodof test for chloride ion content in hardened concrete
using hot-water extraction." The pH of poresolution was measured using a handy pH meter(Toko chemical laboratories TPX-999).
The
Chloride ion content was measured by SALMATE－100V, Asahi life science.
The mortar specimens of 40mm × 40mm ×160mm were prepared for the measurement ofcompressive strength. The compressive strength
test(AYC-500P(500kN,load:
0.6±0.4N•mm-2/sec.)), following JIS A1108 "Concrete
compression examination", was carried out.

100
90
80
70
60
50
40
30
20
10
0

WF
SF

1

10

100

Age[log_days]

Fig. 2 Influence of mixing with seawater on slag
reaction ratio on B70
WF

Pore volume[cc/g]

0.4

SF

0.3
0.2
0.1
0
1

10

100

Age[log_days]

Fig. 3 Influence of mixing with seawater on porevolume on B70
The results of compressive strength tests of
B70 are shown in Fig. 4.
BFS cement isstrengthened in entire period by mixing withseawater. Strength depends on the micropore
structure; accordingly, the representative index isthe amount of pore and the pore diameter. Mixing
with seawater decreases the amount of pores.
Therefore BFS cement is strengthened.

Peak area on
crystallization heat of
Melilite[°C]
Fig. 1 Quantitative determination of the residual
BFS (Peak area on crystallization heat of Melilite)

Compressive strength[N/mm2]

3. Experimental Results
3.1 Effect of mixing with seawater to cement
The results of slag reaction ratio tests of B70
are shown in Fig. 2. Mixing with seawater raises
the BFS reaction ratio compared to mixing with
distilled water for the entire period. The increasing
has the largest value at day 1, since then, reduced
and remain.
The results of mercury intrusion porosimetryof B70 are shown in Fig. 3. The amount of poresis decreased by mixing with seawater compared todata of 1st day. As hydration advances, products

70
60
50
40
30

WF

20

SF

10
0
1

10

100

Age[log_days]

Fig. 4 Influence of mixing with seawater oncompressive strength on B70

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Compressive strength[N/mm2]

3.2 Effect of curing with seawater to cement
The results of slag reaction ratio tests of B70
are shown in Fig. 5. Difference between specimen
mixing distilled water and mixing seawater has thelargest value at day 28th. Curing influence effects
cement paste from surface. Hence, the effect of
curing with seawater retards.
Slag reaction ratio[%]

80
70

WW
WS

1

10

100

Age[days]

60
50
40
30

WW

20

WS

Fig. 7 Influence of curing with seawater oncompressive strength on B70

1003.3 Effect to rebar corrosion
1

10

100

The results of rebar corrosion test are shown in
Fig. 8.
In case of mixing with distilled water andcuring with seawater(Fig. 8-(a)), curing withseawater causes rebar to corrode in OPC specimen.
It suggests that BFS cement has high-resistance forrebar corrosion by salt attack even if it is exposed toseawater at extreme early stage.
In case of mixing with seawater and sealed
curing(Fig. 8-(b)), mixing with seawater does notcause corrosion except for B90.
In case of mixing and curing with seawater(Fig.
8-(c)), there is similar trend to case of mixing
distilled water and curing seawater, OPC specimens
corroded significantly compared to BFS specimens
at day 91. corrosion rate trend is in order of OPC> B70 ≥ B90 > B50. The results is uncorrelatedwith replacement ratio. Because corrosion ratio
determines the balance between chloride content
and pH of pore solution(2.

Age[log_days]

Fig. 5 Influence of curing with seawater on slag
reaction ratio on B70

The results of mercury intrusion porosimetryof B70 are shown in Fig. 6. Amount of poredecreases from day 7. The Influence effects
earlier than the influence on slag hydration ratio.
This happens because Pores are filled by not only
increased hydration products but also precipitatefrom seawater.
0.4
Pore volume[cc/g]

50
45
40
35
30
25
20
15
10
5
0

0.3
0.2

WW
WS

0.1

OPC
B50
B70
B90
OPCWW

0
1

10

100
corrosion current Density [μA/cm2]

Age[log_days]

Fig. 6 Influence of curing with seawater on porevolume on B70

The results of compressive strength tests of
B70 are shown in Fig. 7. Specimen is strengthenslightly by curing with seawater until 28th day.
The smaller effect than paste specimens is causedby size effect.

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

20

40

60

80

100

Age[days](a) Mixing with distilled water / Curing with seawater

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OPC
B50
B70
B90
OPCWW

2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0

1.6
1.4
Cl- content[wt%]corrosion current Density[μA/cm2]

Technical Report of International Development Engineering

1
0.8
0.6
0.4
0.2
0
0

0.01

0.02

0.03

0.04

OH- content[mol/L]
0

20

40

60

80

100

WS
■

Age[days](b) Mixing with seawater / Sealed curing

1.4

SS
▲

SF

OPC

B50

B70

B90

Fig. 9 Relationship between corrosion and Cl/OH

OPC
B50
B70
B90
OPCWW

1.6

SW
●

OPCWS
OPCSS
B50WS
B50SS

4

1.2
Igloss[wt%]corrosion current Density[μA/cm2]

1.2

1
0.8
0.6
0.4
0.2

3
2
1
0

0
0

20

40

60

80

1

100

10

100

Age[log_days]Age[days](c) Mixing with seawater / Curing with seawater
Fig. 8 Influence of mixing/ curing water on

Fig. 10 Influence of curing water on Ca(OH)2(OPC, B50)

corrosion rate

4. Conclusions
The following conclusions are derived from theinvestigations of this study:(1) Mixing with seawater with sealed curing raises
the strength in BFS cement specimens byaccelerated slag hydration.
(2) Cured with seawater raises the strength in BFS
cement specimens until 28th by accelerated
slag hydration.
(3) 50% replacement BFS has good resistance forchloride corrosion for both cases of mixing andcuring with seawater.
It is recommended to use high-replacement ratio
BFS (about 50%) if there is no choice but to mix /cure with seawater.

The results of pH and fusible chloride content
measurements are shown in Fig. 9. Rings on thegraph express markedly corroded cases. The rings
are grouped in a low hydroxide ion content area.
OH amount has heavy influence to corrosion rate.
Case of B50 show higher OH amount. This is thereason why B50 has good resistance for salt attack.
The main alkali source of cement is Ca(OH)2,
Ability of Ca(OH)2 generation is lower BFS than
OPC. However, seawater causes Ca leaching incase of OPC shown Fig. 10. Ca(OH)2 reacts with
MgCl2 in seawater and produce Mg(OH)2 andCaCl2. The CaCl2 dissolves in water.

Reference
1.
2.

Imoto, H., Concrete engineering annual thesis collection, 28 [1], pp. 29-34, 2006.
Erlin, Bernard; Verbeck, George J : CORROSION OF METALS IN CONCRETE NEEDEDRESEACH , Corros of Met in Concrete, p39-46

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Identification of main compound in Lao medicinal plant-Kiderm
Student Number: 09M18090

Name: Nariaki Maeshibu

Supervisor: Sachio Hirose,

ラオス薬用植物キデムに含まれる成分の同定
前澁 成昭
ラオスの糖尿病患者が長年服用してきた伝統薬がある。キデムと呼ばれる薬用植物の根を熱水で抽出したものには血糖値を下げる効果があり、過度に摂取しても血糖値が下がりすぎないといわれている。しかし、キデムは慣用名で学名や成分についての報告はない。本研究では分離、定性、定量の同時分析が可能な LCMS によって成分の解析を行った。主成分については、
NMR による分子構造の決定を行うことを目的とした。主成分は分子量が 722 でありフラボノイ
ドでコーヒー酸に置換基が２個ついていると推定された。
The aim of this study is to elucidate the medicinalcompounds in Kiderm using liquid chromatography
hybrid ion trap time-of-flight mass spectrometry(LC/IT-TOF MS) and to identify the molecular
structure of the main fraction in ingredients using
nuclear magnetic resonance (NMR).

1. Introduction
Herbal medicine has a history of several thousand
years and their worldwide utilization has been
increased recently in both developing and developed
countries. The World Health Organization estimatedthat 65-80% of the world population used herbal
medicines as the primary form of healthcare [1]. In
addition, adding value to local medicinal plants andagricultural products to produce herbal medicine orfinding new lead compounds for medication is a way
to improve the living standard in the developing
countries. However, there are many medicinal plantsthat have not been studied.
In Lao P. D. R., Kiderm is one of the medicinal
plants that have been used for the treatment of diabetes
mellitus. A blood glucose level lowering was observed
when a tea form from this plant was applied to patients
who have Type 2 diabetes. It is said that glucose
concentration in blood does not fall even if it isconsumed in excess. However, the scientific nameand the compounds in Kiderm have not been identified
yet.
In herbal medicine, it has been generally acceptedthat the efficacy of herbal medicines can be attributed
to the synergistic activity of various major and minor
compounds of the herbs [3][4]. Therefore, it isessential to develop a method for online analysis of thecompounds in Kiderm in order to understand better thepharmacological effect and ensure the quality controlof the herb in the future.

2. Materials and Methods
2.1 Materials
Roots of Kiderm were purchased from a herbal
medicine shop in Vientiane province, Laos in
November 2008 and dried at room temperature. All
chemicals were purchased from Wako Chemical
Industries, Ltd. (Tokyo, Japan). Ultrapure waterproduced by the Milli-Q Advantage vessel (Millipore,
USA) was used for liquid chromatography mass
spectrometry (LCMS) analysis.

2.2 Extraction of Kiderm roots
2.2.1 Extraction solvent
To investigate the effect of the solvent for extraction,
Kiderm roots were crushed to pieces under 500 μm. 1
g of roots powder was extracted 40 mL of water ormethanol in water bath at 60˚C for 2 hours. The
extracted liquid was filtered with 0.45 μm membranefilter. The filtrate was transferred into 1.5 mL vial for
LCMS analysis. The area of each peak fromchromatogram at 279 nm was compared.
2.2.2 Compounds in skin and core81

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TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

column (250 mm x 9.4 mm, 5 μm, Agilent
technologies, CA, USA).
2.4.2 Stability of the main compound
Stability of the main compound was tested on 55℃
and room temperature.
2.4.3 NMR analysis
Purified main compound was dissolved inmethanol-d4 99.8%. 1H, 13C, HH-COSY and HSQC
was measured.

To compare the compound from skin and core ofKiderm roots, 5 g powder was of each samplewas
extracted with 200 ml methanol by soxhlet extractor
for 24h.
2.2.3 Extraction temperature
1 g of Kiderm roots powder was extracted using 40
mL methanol in water bath at temperature 15℃ 30℃
45℃ 60℃.

2.3 LCMS-IT-TOF analysis
Optimal condition for separation was studied byusing a Shimadzu LCMS 2010EV system. The
separation was performed on ZORBAX Eclipse
XDB-C18 column (150 mm x 2.1 mm, 3.5 μm, Agilent
technologies, CA, USA). The mobile phase consistedof 0.1% formic acid aqueous solution (A) andmethanol (B). The elution was performed at 0.2 mL
min-1. The column temperature was kept at 40˚C. UVspectra were scanned from 200 to 470 nm.
For LC/IT-TOF MS analysis, 500 g Kiderm roots
were extracted with 1.5 L methanol at 60˚C for 7 hours.
The extracted liquid was filtered through filter paper
No.101 (Toyo Roshi Kaisha, Japan) and evaporated byrotary evaporator. A total of 10 g of residual extract
was obtained. An amount of 16 mg was dissolve in 1.6
mL methanol and filtered with 0.45 μm membranefilter. The filtrate was transferred into 1.5 mL vial for
LC/IT-TOF MS analysis using Shimadzu LC/IT-TOF
MS system couple with electrospray ionization source(ESI). ESI-MSn experiments were conducted in both
positive and negative modes. The following
instrumental parameters were used: interface voltage,+4.5 kV (positive mode), -3.5 kV (negative mode);heat-block temperature, 200˚C; curved desolvationline (CDL) temperature, 200˚C; flow rate of
nebulizing gas (N2), 1.5 L min-1; and pressure ofdriving gas (N2), 100 kPa. Mass spectrometry was
performed in the full-scan mode (MS1) and automatic
multiple-stage fragmentation-scan modes (MS2-MS4)
over an m/z scan range of 100-1000.

3. Results and discussion
3.1 Extraction solvent
Almost all of the peak area from methanol extracted
samples were smaller than those extracted with water.
The main peak area decreased 47%. However, one
peak was detected only by methanol. Methanol wasused as extraction solvent further experiments because
the extracted samples did not decay easily.
3.2 Skin and Core
319 mg of extract was obtained from 5 g core and374 mg was obtained from 5 g skin. PDA suggested
that the skin contained much more main compound
than the core. Beside the main compound, skin andcore also contain several different minor compounds.
LCMS analysis for the extract was done without skinand core separation due to less information about thelocation of useful compound.
3.3 Effects of extraction temperature
The amounts of the extraction were compared basedon the main peak area (Fig. 1). The amount of theextract increased as the temperature rose. The mosteffective temperature was 60℃.

2.4 NMR
2.4.1 Separation for NMR
Pure compound is needed for identification of themolecular structure by NMR. To separate main
compound efficiently, liquid-liquid extraction was
done on extract of Kiderm using chloroform and water.
Water phase was freeze dried because the target was init. The extract was dissolved with 0.1% formic acidand filtered for LC. The main ingredient was separated
and collected by LC on ZORBAX Eclipse XDB-C18

Fig. 1 Effect of temperature on extraction

3.4 LC/IT-TOF MS
The optimum condition for analysis wasinvestigated by using isocratic elution and gradientelution using 20% to 70% methanol in water. Figure 2
shows the chromatogram of Kiderm extract at UV 279
nm and the total ion chromatogram (TIC) obtained by
LC/IT-TOF MS in negative mode using optimum82

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

inhibitory activity [2][5][6]. However, the accurate
position of functional group of the flavonoids can not
be determined by mass spectrometry.

condition. Maximum UV absorption and mass data ofeach peak were summarized in Table 1. Most of thepeaks were observed when using mobile phaseconsisted of 20% to 40% methanol. Main peak andother several PDA peaks showed the maximum UV
absorption in the range of 270–280 nm. This indicatedthat the compounds could be flavonoid compounds inthe group of flavones, flavonols, dihydrochalcones,
catechins and flavan-3-ols.
Water and 60% ethanol gave higher peak area thanthose extractions by pure methanol and ethanol (datanot shown). This result suggested that the compounds
in Kiderm were hydrophilic and supported the above
hypothesis. Although water gave higher peak area thanother solvents, water could also easily extract
unwanted components such as sugar and amino acid.
For analytical purpose, we chose methanol as theextraction solvent because it gave a higher ratio of themain peak and also it is easier for evaporation in
separation process.
In negative mode (Table1), the main peak (peak No11) gave ion at m/z 721.1783 in MS1, which indicatedthe deprotonated molecule [M – H]—. In positive mode,
sodium adducted ion [M+Na]+ at m/z 745.1707 wasobserved. This result indicates that the molecularweight of the compound is 722.18. Fragmentation of
ion at m/z 721.1783 gave ion at m/z 541.1340 which
was 180.0443 lower than the precursor ion (721.1783).
The loss of 180 Da from a molecular ion could be dueto the loss of glucoside (C6H12O6). However,calculation of C6H12O6 gave exact mass at 180.0634
which shows 106 ppm higher than 180.0443. The
IT-TOF mass spectrometer provided an accuracy of
about 10 ppm at m/z 1000 in MS2. This massdifference indicates that there is low possibility thatthe molecule has glucosides. Instead, C9H8O4 (exact
mass 180.0423, 11 ppm) was proposed as potential
fragment which was lost from the molecular structure.
This lost fragment could be caffeic acid. Ion at m/z
361.0883 also indicated the cleavage of caffeic acidfrom the molecule. The strong intensity of ion at m/z
299.0861 may be the skeleton structure of thecompounds. From this result, the main compound in
Kiderm was considered to be a flavonoid compoundwith two molecules of caffeic acid. The fragment
pathway of the main peak is proposed in Scheme 1.
Compounds 5, 6, 11, and 15 could have similar basic
structure, since they produced similar fragment ions(Table 1). The compounds could be similar toflavonoids that were reported to have efficacy inlowering blood glucose, and aldose reductase(A)

UV 279 nm

75.0
5.0
Mobile phase B concentration

50.0

2.5

Mobile Phase B (%)

Intensity (×10 mAU)

7.5

25.0
0.0

0.0

0

10

20
30
Retention time (min)

40

50

6.0
TIC

Intensity (×107 )

5.0

(B)

4.0

3.0

11,12

2.0
1.0

13
1
2

3

4

5 6

10
8 9

7

15
14
16

0.0
10

20

30

40

50

Retention time (min)Fig. 2 UV chromatogram and TIC
Table 1 Retention time, UV (λmax) and MS
data from the Kiderm root extract
Peak
No
1
2
3
4
5

Rt min UV λmax

MS1 m/z

MS2 m/z / MS3 m/z (% base peak)

2.82
5.90
8.23
11.35
15.16

281
273
221, 251247
279

359.0993
443.1904
373.1143
403.1241
541.1352

6

16.41

275

541.1342

7
8
9
10
11

17.94
24.39
26.59
27.32
28.58

251
281
273
282, 328
279

403.1249
525.1380555.1487
389.0853
721.1783

12
13
14
15

28.79
29.34
29.84
31.16

279
276
282
279

721.1775
719.1644
717.1436
705.1811

16

31.70

329

343.0823

361.0889(8), 317.1048(14), 299.0899(63), 281.0791(13),273.1132(38), 237.0930(13), 197.0452(30), 179.0375(17)361.0918(43.54), 343.0765(16), 317.1041(25), 299.0936(8),273.1116(100), 255.0968(10), 197.0476(16), 179.0361(7)541.1340 / 361.0936(12), 343.0863(7.58), 317.1019(15),299.0918 (83), 281.0817(18),273.1111(35), 255.0982(7),237.0908(19),197.0441(35), 179.0347(15)541.1351 / 361.0884(8), 343.0783(8), 317.1046(23),299.0914(100), 281.0835(31), 273.1119(68), 255.0969(13),237.0923(16), 197.0447(51), 179.0357(26)-Scheme 1 Proposed fragment pathway of themain compound in Kiderm

3.5 Purification of main compound
The extract did not dissolve in water andchloroform so much therefore it was often dissolvedin the methanol. The 1iquid-liquid extraction was

83Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

done with precipitate. Over 90% of main compound
was collected and over 80% of the weight of theextract was removed. On LC purification method,
good separation of main compound was obtained by
35% methanol on isocratic flow (Fig. 3). Figure 4
shows the chromatogram of the main compound
fraction after LC fraction collection. Unexpectedly afew peaks with shorter retention time than the main
compound were detected. It took one month toisolate and purify the compound.

Fig. 6 13C of the main compound

4. Conclusion
The compounds in Kiderm were effectively
extracted by using polar solvent such as water andmethanol. Most of the compounds were found tobe hydrophilic. They show maximum UV
absorption at 270-280 nm and may be flavonoid
compounds. At least 4 compounds could be
flavoinoid with two molecules of caffeic acid.
Figure 7 shows proposed molecular structure of
the main compound. The glucose level lowering
effect of Kiderm could be due to the effect of thecompounds similar to those of flavonoids reported
previously.

Fig. 3 Purification of main compound by LC
%

mAU

75.0
10
50.0
25.0

0

0.0
0

10

20

30

40

50 minFig. 4 PDA chromatogram of purified
main compound

3.6 Stability of the main compound
The peak of the main compound of the fraction left
in constant temperature reservoir (55 ℃ ) degraded
completely after one week. The peak area of the main
compound decreased 20% at room temperature after
one week. It is necessary to keep the fraction in the
refrigerator (4℃).
3.7 NMR analysis
Figure 5 shows 1H of the main compound. Count
number was 480. But the peaks from compound were
too weak to identify molecular structure. Results of13
C (Fig. 6), HH-COSY and HSQC were also uselessof identification.

Fig. 7 Proposed molecular structure of main
compoundReference[1] Hao, H. P., et al., Anal. Chem. 80, 8187-8194(2008)[2] Igarashi, K., et al., J. nutr. sci. & vita. 58,
496-500 (2007)[3] Liang, X. M., et al., J. Chro. A 1216, 2033-2044(2009)[4] Liang, Y. Z., et al., J. Chro. B 812, 63-70 (2004)[5] Matsuda, H., et al., Chem & Pharm. Bull. 50,
788-795 (2002)[6] Yoshikawa, M., et al., J. Chem. & Pharm. Bull.
46, 113-119 (1998)

MeOH

H2O

Fig. 5 1H of main compound84

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Wastewater Treatment by Adsorption with Activated Carbon
from Wood Residues in Rubberwood Sawmilling Process
Student Number: 09M18108 Name: Saori MITANI Supervisor: Ryuichi EGASHIRA

ゴム木材製材プロセスにおける残材由来活性炭を用いた吸着による排水処理三谷沙織

残材の熱処理生成物である木酢液を木材の防腐剤，活性炭を防腐剤の混入した排水を処理するための吸着剤として利用することにより，東南アジア等における本プロセスの改善を計る方法に対して，実用上重要である固定床型吸着装置の適用を試みた。ゴム木材おが屑を熱処理して得られた活性炭によるフェノールを含むモデル排水の吸着実験を攪拌槽，固定床吸着装置を用いて行い，吸着平衡と粒子内有効拡散係数を実測した。活性炭粒子内部の拡散係数が小さく，外部のそれは大きいことが推測された。実験結果に基づくシュミ
レーション計算より，木酢液は全て同時に得られた活性炭により処理できることが分かった。

In order to put this method to practical use, moredetailed studies of the individual operations in this process
should be carried out. For wastewater treatment, it isnecessary to study not only adsorption isotherms but also
kinetics.
This study aims to design the fixed bed adsorption
column for the wastewater treatment in this process.
Firstly, thermal treatments of RW sawdust were conductedto obtain the AC. With this AC, the adsorption of phenol, a
representative pollutant in PA, from the aqueous solution
with stirring vessel and fixed bed column were conducted
to measure the adsorption equilibrium and rate
parameters.

1. Introduction
In Southeast Asian countries, rubberwood (RW)
furniture manufacturing is an important industry.
Sawmilling provides the processed wood for the industry.
The current sawmilling process is shown in the black partof Fig. 1. Rubberwood logs are cut, preserved and dried,
and molded into products. The problems from the process
are to use toxic preservatives and to generate large amountof wood residues. The preservatives contain the harmful
compounds such as boric acid and mixture of borax
pentahydrate and the workers are exposed to the harmfulenvironment. In addition, the wastewater, containing thepreservatives, is discharged without any treatment, whichcauses soil and water contaminations. The large amount ofwood residues with the preservatives are dumped orthrown away in rural sites and burned illegally, causing
soil contamination, accidental forest burning andgeneration of hazardous gases.
To solve these problems in the sawmilling process, we
proposed the utilization of the products from thermal
treatment of RW residues. The proposed process is shownin Fig. 1, as well. A portion of residues from the cutting
and molding process are thermally treated under steam
atmosphere to produce the pyroligneous acid (PA),
activated carbon (AC) and off-gas. PA is used aspreservative, replacing the current purchased preservative
because it contains the components useful as preservatives.
Table 1 shows the some components in PA which we
identified. The concentrations of these toxic components
such as phenolic compounds are very low compared withcurrent harmful preservatives, but they need to be removed
from the wastewater. So, it is treated by adsorption with
the AC. The remaining wood residues and off-gas are used
for heat generation. We conducted the thermal treatment
of RW sawdust under various conditions, studied thecharacteristics of the products, and evaluate the feasibility
by simple calculation based on these experimental results.
These studies showed that the improvement of the processby the proposed method was feasible in terms of material
and heat balances [1].

Wastewater
treatment

Molding

Products

Steam

PA
AC
Off-gasHeat
generation 2

Wood
residues

Wastewater

Thermaltreatment

Wood residues

Heat
generation 1

Rubberwood

Molding/bandsawing

Steam

Preservation& Drying

Material flow
Heat flow
Current process
Proposed process

Fig. 1 Current and proposed process of sawmilling:material and heat flows
Table 1 Components in PA
Components
Mass fraction [%]Water
92
Acetic acid
0.89
Acetone0.22
Phenolic compounds
0.06
Methanol
0.02

85

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

apparatus for fixed bed adsorption. Principal conditions of
the experiment are shown in Table 4.

2. Experimental
2.1 Preparation of AC by thermal treatment of RW
sawdust
RW sawdust was thermally treated under steam
atmosphere in a stainless steel tube heated by an electric
furnace to obtain the AC. The experimental equipment isshown in Fig. 2, the same as previous study [1]. The tube
was heated from room temperature until the specified
treatment temperature, 873K, and the temperature was
kept for 1 hour, defined as holding time. Table 2 shows theprincipal conditions of thermal treatment. The watercontent in obtained crude PA was measured by Karl
Fischer Instruments (758KFD Titrino) from Metrohm
company.

Table 2 Principal conditions for thermal treatment
Feed
RW sawdust
Mass of feed[g]
25 ± 5
Atmosphere
H2O
Flow rate[m3∙h-1]< 473K
0.003(N2)> 473K
0.0012(N2)0.003(H2O)
Temperature[K]
873
Holding time[h]1

2.2 Adsorption with stirring vessel
AC was washed, dried, grounded and sieved before
adsorption experiments. The schematic diagram of thevessel is shown in Fig. 3. The experimental conditions areshown in Table 3. A specified amount of AC was mixed
with the model wastewater containing phenol in thestirring vessel. The stirring velocity was 500 rpm, which isreported to be high enough for external mass transfer
resistance to be negligible under such systems andconditions [2]. The temperature of the material system invessel was kept at 303 K in an isothermal bath. The
samples were intermittently collected until the systemreached adsorption equilibrium. The samples wereanalyzed by gas chromatograph (GC-14B with FID,
Shimadzu Co., Ltd.; ULBON HR-20M column I.D.
0.00053 m × 30 m).

(cm)
0.8
0.7
1.2

5

Table 3 Principal conditions for adsorption withstirring vessel
PhenolFeed solution
solution
Volume of feed solution [m3]500 × 10-6
Concentration of phenol in feed
100–400
solution,, C0 [g∙m−3]
AdsorbentAC0.21−0.85,
Particle radius AC, Rp × 103 [m]0.08−0.18
Concentration of adsorbent, CAC [g∙m−3] 200–2000
Stirring speed [rpm]500
Sampling size [m3]3 × 10-6
Contacting time [h]75, 200
Contacting temperature [K]303

Based on the results of batch adsorption in the stirring
vessel, fixed bed adsorption was conducted with the small
column which was made of acrylic resin. The column size
was 2.1 cm in I.D. and 4 cm in length. Pretreated AC wassoaked with deionized water under vacuum condition toremove air in the pores. Then it was packed in the column.
The model wastewater containing phenol was supplied tothe column in an upward flow direction at a desired flowrate by a delivery pump. Samples were taken from theeffluent on the top of the column and phenol concentration
was determined by gas chromatograph. Figure 4 shows the
T.I.

(1)
(4)
(7)

(5)

(3)

Non-condensable
gas

(6)
(7)

(2)

sampling

(8)

F.I.

(3)

(9)
N2

5

Fig. 3 Schematic diagram of stirring vessel

2.3 Adsorption with fixed bed

H2O

1

9

(4)

(10)

(1)

Fig. 2 Schematic diagram of thermal treatment apparatus:(1) tubular reactor; (2) electric tubular furnace; (3) sample
holder; (4) feed/solid product; (5) NiCr-Constantan
thermocouple; (6) micro-plunger pump; (7) valves; (8)condensers; (9) liquid product trap; (10) iced bath(275~277 K); F.I. flow indicator; T.I. temperature
indicator
86

(2)

(5)

Fig. 4 Schematic diagram of fixed bed apparatus:(1) Feed tank; (2) Solution delivery pump; (3) Heatingwater tube; (4) Fixed bed column; (5) Reservoir

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Rp × 103 [m] 0.43- 1.70 0.43- 1.70
C0/CAC [-]
0.42
1.08
C1/C0 [-]
0.69
0.83
Key

Table 4 Principal conditions for fixed bed adsorption
PhenolFeed solution
solution
Fluid velocity, u × 10 [m∙h−1]
2.6–5.4
Concentrationofphenol230–300
in feed solution, C0 [g∙m−3]
Adsorbent
AC
Particle radius of AC, Rp × 103 [m]0.08−0.18
Mass of adsorbent [g]2.5 ± 0.5
Sampling size [m3]3 × 10-6
Contacting temperature [K]303

0.15- 0.36
0.20
0.27

1

C/C0 [-]

0.8
0.6
0.4
Theoretical curve
13

0.2

2 -1

De×10 [m ·s ]De,i×1013[m2·s -1]
De,o×1013[m2·s -1]

3.8
0.5
20

0

10-3

3 Results and discussion
3.1 Preparation of AC by thermal treatment of RW
sawdust

10-1

100

101

102

103

Time [h]Fig. 5 Concentration change in stirring vessel

Thermal treatment of RW sawdust produced crude PA,
AC and off-gas. The color of crude PA was dark brown andwater content was 83 %. It was because it contained a lot oftar components, which we proposed to be removed by
distillation. All sawdust was evenly treated to AC. AC was
black in color and lighter in mass if compared with feed
sawdust. Off-gas was non-colored gas and had a bad smell.
Mass of obtained crude PA, AC and off-gas were about
30 %, 20 % and 50 % of the feed sawdust, respectively.
Mass of off-gas was derived based on material balance.

numerical solution of Eq. (1) with using the different
values of De for inner part of AC, De,i, and outer part of one,
De,o, are also drawn in Fig. 5(solid lines). They fitted theexperimental results better than former lines. Thissuggests that De of inner part of AC was smaller than outerpart of it and it controlled the overall adsorption rate inlater period of time.

3.3 Adsorption with fixed bed
Material balance equation in fixed bed is written as
Eq. (2)

3.2 Adsorption with stirring vessel
The basic equation of adsorption rate into the particle iswritten as Eq. (1)∂q/∂t = De∙(∂2q/∂r2 + (2/r) (∂q/∂r))10-2

u  C z ρb  qave   0

(1)

(2)

with boundary conditions
r = Rp； b  qave   kf av  C  Cs  ,with boundary conditions
r = Rp；  s  De  q t   VL S  C t  , q  f C  b  qave   De  a v   p  q r  , q  f C 

r = 0； q r  0
and initial conditions
q = 0 for 0 ≤ r ≤ Rp
and
C = C0 for t = 0, z = 0
where, u is flow rate, C is bulk concentration of phenol inthe bed, z is axial location in the column, ρb is bed density,
qave is average amount adsorbed, θ is time, ρp is particle
density, kfav is fluid phase mass transfer coefficient, and Cs
is concentration at interface. Eq. (1) and (2) can be solved
numerically to obtain the breakthrough curve.
Fixed bed adsorption runs were conducted under threedifferent conditions. u and C0 were varied. The
breakthrough curve was measured for each run shown in
Fig. 6. Breakthrough concentration, CB, was fixed 10% of
C0.
From the results, effluence concentration of phenol, C,
was 0 at first. This is because whole phenolic compound
was adsorbed by AC which was packed in the column
during the solution flowing in the column. Then, C/C0
gradually increased with time. Breakthrough time, tB,
showed increasing tendency with decreasing u.
The shapes of the curves spread widely at highconcentration field. Probably, the intraparticle masstransfer might dominate in this adsorption. Some
researchers reported that for rate control in the mobiler = 0； q r  0
and initial conditions
q = 0 for 0 ≤ r ≤ Rp
and
C = C0 at t = 0
where q is amount adsorbed, De is effective intraparticle
diffusivity, r is radial position in the particle, ρs is particle
density, VL is volume of solution, S is external surface area,
C is concentration of phenol in the solution. The measured
concentration change allows to estimate the value of De.
The time course of the dimensionless concentration,
C/C0, of phenol in the solution are presented in Fig. 5. The
concentration decreased over time, which shows that thephenol in the solution could be removed by AC.
C1/C0 is shown in table in Fig. 5, where C1 is the final
concentration of phenol. When C0 was low, C1/C0 was low.
When the mass fraction of AC in the solution, CAC was low,
C1/C0 was high.
The adsorption isotherm fitted Henry’s type well from
C1 of each run. Theoretical curves by analytical solution of
Eq. (1) are also drawn in Fig. 5 [3](dotted lines). De wasmeasured by fitting to theoretical curves. However, it wasdifficult to fit all data with this theoretical curve.
Compared with the curve, experimental concentration
decreased quickly in early stage and then gradually
decreased. Theoretical curves, which are obtained by87

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

fluid phase mass transfer coefficient [s-1], Cs: concentration atinterface [g∙m-3], av : specific surface area [m2], CB: breakthroughconcentration [g∙m-3], tB: breakthrough time [h], ξ : yield anddensity parameter [-](    PAYAC  bYPA ), L : column length [m], R:
mass of RW residues [kg∙h-1], YPA : yield of PA [-],YAC : yield of
AC [-],MAC,Column : amount of AC for column [kg], MAC,Exit :excess amount of AC [kg]phase, the wave “fronts” has a sharp tail; for rate control inthe stationary phase, the wave “tails” has a sharp front [4].
Theoretical breakthrough curves were estimated bysame method as before. Similarly to stirring vessel, modelof smaller De,i than De,o, which are shown by solid lines inFig. 6, fitted better to experimental results better thanmodel of uniform De values in the particle, which are
shown by dotted lines. Appropriate values of De,i was small
compared with that of stirring vessel. Possible reason was
difference of adsorption operation. For example,
channeling might occur in the column. If so, flow rate
became inconstant and it caused earlier increase ofconcentration. But decisive reason was not clear.
Then, simulation calculation for industrial adsorption
column operation was conducted. Figure 7 shows theprocess flow for simulation calculation. The yield andother characteristics of products were based on
experimental results. Toxic component in wastewater was
assumed to be only phenol and the concentration was
1500 ppm, which is sum of phenolic compounds in
obtained PA. CB was fixed 1 ppm, which is the permissiblelevel in Malaysia. Simulation was done to meet following
conditions, which all of PA is treated by tB within unit oftime and excess amount of AC is discharged. Then,
relations were obtained as follows to satisfy these
conditions.

References[1] Rizafizah Othaman, et al. J. Chem. Eng.Japan, Vol. 41, No.
12, pp. 1149-1158 (2008)[2] Suzuki Motoyuki, et.al. J.Chem. Eng. Japan, Vol. 8, No. 1,
pp. 79-81 (1975A)[3] Crank, “Mathematical diffusion”, Oxford (1956)[4] Friedrich G. Helfferich. J. Chromatography, 629, pp. 97-122,(1993)

Theoretical curveu× 10 [m∙h -1] 5.4
C0 [g∙m-3]
270
tB [h]
0.5
Key

2.6 2.9300 230
4.0 6.5010De,i×1016[m2·s-1]

6.0

De,o×1012 [m2 ·s-1]

2.0

De×1012[m2·s-1 ]

2.0

1.0

C/C0 [-]

0.8
0.6
0.4

u  t B   L  1, 1  tB

0.2

In this equation, ξ is yield and density parameter obtainedby experiment and L is column length. Table 5 shows theexample of simulation calculation. All of PA could betreated by AC and excess amount of AC could be treated.

0
20

30

Time [h]

Steam

R

PA

RYPA

Wastewater
(PA)

RYPA

AC

RYAC

Wastewater
treatment

RW
residues

Preservation

AC which posses the adsorption ability of phenol was
sufficiently obtained by thermal treatment of RW sawdust.
In adsorption with stirring vessel, it was found that masstransfer rate of inner part of AC particle was much smaller
than outer part.
In adsorption with fixed bed, mass transfer rate in innerpart of AC particle was small similarly to stirring vessel.
Based on the results, simulation calculation showed that
all of PA obtained by thermal treatment could be treated by
AC.
Therefore, it is concluded that application of fixed bed
operation for wastewater treatment in proposed
sawmilling process will be feasible.

Thermal treatment

Fig. 6 Breakthrough curves of phenol

4. Conclusion

Treated
water

RYPA

MAC,Exit

Fig. 7 Process flow for simulation calculation

Table 5 Example of simulation calculation
Mass of RW residues, R [kg∙h-1]
Mass of obtained PA, RYPA [kg∙h-1]
Mass of obtained AC, RYAC [kg∙h-1]
Volumetric flow rate of waste water
to column [kg∙h-1]
Column length, L [m]Fluid velocity, u [m∙h-1]
Breakthrough time, tB [h]Amount of AC for column, MAC,Column [kg]Excess amount of AC, MAC,Exit [kg]Nomenclature
C0: feed concentration [g∙m-3], Rp: particle radius [m], CAC:
concentration of AC [g∙m-3], u: velocity of the fluid [m∙h-1], De:
effective intraparticle diffusivity [m2∙s-1], q: amount adsorbed[g∙(g-AC)-1], t: time [s], r: radial position within a particle [m], ρs:particle density [g∙m-3], VL: volume of solution [m3], S: externalsurface area [m2], C: concentration in the fluid phase [g･m-3], C1:
final concentration [g∙m-3], De,o: effective intraparticle diffusivityof outer part in AC [m2∙s-1], De,i: effective intraparticle diffusivityof inner part in AC [m2∙s-1], z: axial location in the column [m], ρb:
bed density [g∙m-3], qave : average amount adsorbed [g∙(g-AC)-1],
θ: adjusted time [s](= t-ε∙z/u), ρp: particle density [g∙m-3], kfav :

88

10000
14000
2080
14
3.0
98
1.0
90
1990

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Quadratically Constrained Criterion based on Maximum a
Posteriori Estimation for Binary ClassifierStudent Number: 09M18114

Name: Tatsuya Yokota Supervisor: Yukihiko Yamashita

二値識別のための MAP 推定に基づく二次制約評価基準
横田 達也
本論文では，MAP 識別器を構成するための新しい二次制約評価基準を提案する．一般の MAP 識
別では，事後確率の推定を伴うが，本手法では事後確率の推定をせずに，直接，MAP 識別と同じ結果を与える識別関数を推定する．本評価基準に基づく識別器は，重み関数を選択することで，最小二乗回帰やサポートベクターマシンとして特徴づけることができる．さらに，計算機実験の結果，既存の従来手法と比べて有意な差を得ることができた．

1

3

Introduction

Maximum a posteriori (MAP) based methods play animportant role in machine learning and pattern recognition. In this paper I propose a new classification criterion based on the MAP estimation for a binary problem. In our method, I do not estimate the posteriori
probability; instead I construct a discriminant functionthat provides the same result.
I show that the least square regression (LSR) and the
support vector machine (SVM) can be derived from thecriterion. Furthermore I propose novel classifiers basedon the criterion and conduct experiments to demonstrate their advantages.

Proposed Method

In this section, I define a new criterion for the discriminant function D(x) based on MAP estimation and explain its properties.
Let a weighting function Q(x) satisfy
Z
Q(x)dx = 1, Q(z) > 0(5)
Dfor all z ∈ D, where D describes the data domain.
The new MAP classifier is called the quadratically constrained MAP (QCMAP) classifier and is defined as follows:Z
X
maximize
P (x, y) min(yD(x), 1)dx,y∈{+1,−1}2Model Formalization

And the classifier is given by(+1
D(x) > 0ŷ =−1
D(x) < 0,subject to
D

(2)

(7)

D

+ P (−1|x) min(−D(x), 1)}dx.

(8)

If the measure of {x|P (+1|x) = P (−1|x)} is zero (acondition that is satisfied in almost all classification
problems), then D(x) is given by(+1 if P (+1|x) > P (−1|x)D(x) =.
(9)−1 if P (+1|x) < P (−1|x)

MAP based Classifier

In the MAP classification, when an unlearned pattenx is given, its category ŷ is estimated by finding the
maximum a posteriori probability, P (y|x) :ŷ = argmax P (y|x).

Q(x)|D(x)|2 dx ≤ 1.

To optimize the criterion, I maximize the expectation ofmin(yD(x), 1). Since we have P (x, y) = P (x)P (y|x),
we can write the objective function in (6) as
ZP (x){P (+1|x) min(D(x), 1)(1)

where D(x) is a discriminant function. The goal of thisresearch is to optimize the discriminant function basedon the MAP estimation.

2.1

(6)

Z

Let y ∈ {+1, −1} be the category to be estimated froma pattern x ∈ RM . The training samples are given by{xn , yn }N
n=1 .

D

D(x) yields the same results as the MAP classifier.

(3)

3.1

y

Training method

I define D(x|w) using a linear model function as

In our approach [4], it is necessary and sufficient that(>0
P (+1|x) > P (−1|x)
(4)
D(x)
<0
P (−1|x) > P (+1|x).

D(x|w) :=

M
X
i=1

89

wi xi = hw, xi .

(10)Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Now let me assume that ||x||2 = 1 and the data
domain D is equal to the M − 1 dimensional hyperspherical surface S M −1 . This assumption holds in theGaussian kernel model. I choose the weighting function
to be
1
,
(22)
Q2 (x) :=SM −1

And the criterion of QCMAP estimation can be transformed by replacing the ensemble mean by the sample
mean, and substituting (10) as follows:NX

maximizemin (yn hw, xn i , 1)

(11)

n=1

subject towT Hw ≤ 1,

(12)

where SM −1 is the surface area of S M −1 . This constraint is given by

whereZ
H(i, j) :=Q(x)xi xj dx,||w||2 ≤ M.

(13)

D

We can see that the constraint matrix is defined as
1H2 := M
IM . In this case, QCMAP estimation is equivalent to the SVM method.
If the training samples are distributed normally,
P (x) should be similar to a normal distribution. In
this case the regularization of QCMAP estimation may
be achieved by choosing a normal distribution as theweighting function. So, I propose to use the weighting
function
T −1
11
p e− 2 (x−µ) Σ (x−µ) .
Q3 (x|µ, Σ) := √
M
( 2π)
|Σ|
(24)

Here objective function (11) is piecewise linear function
because of the min function. But it can be linearized
by using slack variables ξn :NX

minimize

ξn

(14)

n=1

subject toyn hw, xn i ≥ 1 − ξn(15)ξn ≥ 0, n = 1, . . . , N(16)wT Hw ≤ 1.

(17)

This optimization problem is a quadratically constrained linear programming (QCLP) problem. QCLP
is a second-order cone programming (SOCP) [2] problem and many solvers are available. QCMAP problems
can also be solved (i.e., trained) by the primal-dual interior point method. [6]3.2Then constraint matrix H3 is given by
Z
T −1
1
xi xj
p e− 2 (x−µ) Σ (x−µ) dx.
H3 (i, j) :=√M|Σ|
D ( 2π)(25)
QCMAP estimation with Eq. (25) provides a novel classifier, which we call the “Gaussian QCMAP” (GQCM)
classifier. If we use the Gaussian kernel model as basis
function and still use a normal distribution in the original space for Q(x), then I can calculate H3 analytically.
When I consider the robustness for outliers, theweighing function should have small value in massed
area and large value in sparse area, since the value controls the strength of regularization. So, I propose afollowing weighting function
λQ3 (x|µ, ν 2 Σ)Q4 (x) := 1 + M
ν −1
λ
− M
Q3 (x|µ, Σ),
(26)
ν −1

Construction of Constraints

In this section, I consider four types of constraint for
QCMAP estimation by choosing four weighting functions.
First, I choose P (x) as the weighting function
Q1 (x) := P (x),(18)

so it satisfies condition (5). Then, the matrix H1 can becalculated analytically by replacing the ensemble meanby the sample mean:Z

1 X (i) (j)xn xn =: H1 (i, j)
N n=1N

H(i, j) =DP (x)xi xj dx 'where 0 < λ < 1 and ν > 1. If we assume Σ is diagonal matrix, then this weighting function always satisfy
Eq.(5). Basically, λ should be nearly 1. Fig.1 depicts
examples of this weighting function. If ν increases, theweighting function becomes smooth and wider. Then,
we haveλλH4 = 1 + M
H30 − M
H3 ,
(27)
ν −1
ν −1
R
where H30 (i, j) := D Q3 (x|µ, ν 2 Σ)xi xj dx. It’s classifier can be characterized as a robust classifier for peripheral area, because the values of its weighting function is large in peripheral area. I call its classifier the“robust QCMAP” (RQCM) classifier.

(19)
(i)

where xn denotes the ith element of vector xn . So we
have

T
1
H1 = X T X, where X := x1 x2 · · · xN.
N
(20)This constraint is given by
wT X T Xw ≤ N.

(23)

(21)

In this case, QCMAP estimation is equivalent to LSR.

90

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

4.2
ν=2
ν=4
ν=9

0.2

In this experiment, I compared the GQCM and RQCM
approaches with existing typical classifiers using thirteen UCI data sets for binary problem. The parametersof weighting function Q3 (x|µ, Σ) are(Var[xi ] i = jµ(i) = E[xi ], Σ(i, j) =(34)
0i 6= j.

Q(x)

0.15

0.1

0.05

And I set λ = 0.99 and ν = 5 for Q4 (x).
Table 1 contains the results of this experiment. The
values in the table are the “average ± standard deviation” of the error rate for all realizations, andthe minimum values for all classifiers are underlined.
Columns S1 , A1 , S2 and A2 show the results of a significance test (t-test with α = 5%) for differences between GQCM/SVM, GQCM/ABR , RQCM/SVM and
RQCM/ABR , respectively. A “+” means that the
QCMAP error is significantly smaller while a “−”means the QCMAP error is significantly larger. The
penultimate line for “Mean%”, is computed by using
the average values of Table 1 as follows. First I normalize the error rates by(particular value)− 1 × 100[%](35)(minimum value)

0
-4

-3

-2

-1

0
x

1

2

3

4

Figure 1: Q4 (x)3.3

Construction of Classifiers

Actually, I define a discriminant function as
D(x|α, β) =NX

αn k(xn , x) + β,

(28)

n=1

where k(x, y) = exp(−γ||x − y||2 ). In other words,
D(x|w) = hw, φ(x)i ,

(29)

where


T
w = α1 · · · αN β ,φ(x) = k(x1 , x) · · · k(xN , x)

(30)
T
.

(31)

maximize

1 X
min (yn hw, φ(xn )i , 1)
N n=1

(32)

subject towT Hw ≤ 1,

(33)

1

for each data set. Next the “average” values are computed for each classifier. This evaluation method istaken from [5]. The last line shows the average of pvalue between “particular” and “minimum” (i.e., minimum p-value is 50%).

GQCM and RQCM are trained byN

5where if H = H3 then the classifier is GQCM, and if
H = H4 then the classifier is RQCM.

4

Discussions

I have shown that the QCMAP approach includes both
LSR and the SVM, and two novel classifiers GQCM and
RQCM. GQCM is characterized not only as regularized LSR, but also as an intermediate classifier between
LSR and SVM. If we make the variance parameter Σ inQ3 (x|µ, Σ) very large, the weighting function becomes
uniform and the classifier tends towards the SVM. On
the other hand, when training samples are distributed
normally, if we set µ and Σ to be their maximum likelihood estimators, the GQCM classifier becomes similar
to LSR.
RQCM is characterized as a robust classifier for peripheral area. According to Mean% and p-value, the
RQCM classifier is the best of all the classifiers considered.
It should be noted that the QCMAP classifiers produce excellent results despite not having a regularization parameter. In general, the QCMAP constraint canbe regarded as a special case of wT Hw < ρ, with regularization parameter ρ. If I adjust ρ to optimize, itsclassifier could be better. However, in terms of the clarity of the theory, a classifier without a regularization

Experiments

The experiments with respect to two classifiers of
GQCM and RQCM were done with following sample
sets:• Artificial samples,• 13th UCI Data sets.

4.1

Performance with UCI data sets

Artificial samples

Fig.2(a) shows the discriminant functions of GQCM,
LSR and SVM. If I focus on the two outliers of (-5,1)
and (5,-1), LSR fits the outliers and the SVM ignores
them. The GQCM approach occupies an intermediate position. Fig.2(b) shows the discriminant functionsof RQCM and GQCM. When the value of weighting
function is large, constraint is strong. Then it is contemplated that RQCM becomes robust for peripheral
area.

91

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

GQCM
LSR
SVM
+1
-1

2

1
D(x)

1
D(x)

RQCM
GQCM
+1
-1

2

0

0

-1

-1

-2

-2
-10

-5

0
x

5

10

-10

(a) LSR, SVM, and GQCM

-5

0
x

5

10

(b) GQCM and RQCM

Figure 2: Artificial experiments: the lines depict discriminant functions of each classifiers. ‘+’ and black
triangles depict the training samples of each categories +1 and −1, respectively
Table 1: Experimental result
BananaB.Cancer
Diabetes
F.SolarGermanHeart
Image
Ringnorm
SpliceThyroid
Titanic
Twonorm
Waveform
Mean%
P-value

GQCM
10.5 ± 0.5
25.6 ± 4.1
23.0 ± 2.0
33.5 ± 1.5
23.9 ± 2.3
15.5 ± 2.8
3.4 ± 0.7
1.9 ± 0.1
10.4 ± 0.7
4.2 ± 2.4
22.1 ± 1.0
2.6 ± 0.2
9.7 ± 0.4
6.0
80.9

S1 A 1
RQCM+ + 10.4 ± 0.4
24.6 ± 4.3+ + 22.7 ± 2.1− + 33.3 ± 1.8
23.9 ± 2.1+ 15.1 ± 3.1− − 3.1 ± 0.5− − 1.7 ± 0.2+ − 10.5 ± 0.5+
4.1 ± 2.1
+ + 22.1 ± 0.9+ + 2.6 ± 0.2+
9.8 ± 0.4
3.4
71.3

S2 A2
+ +
+ +
+ +
− +
+
−
+
+
+
+

+
−
−
−
+
+

LSSVM
10.6 ± 0.5
26.7 ± 4.7
23.3 ± 1.7
34.2 ± 1.7
23.6 ± 2.2
16.6 ± 3.6
3.0 ± 1.6
1.6 ± 0.2
11.0 ± 1.6
4.7 ± 2.3
22.5 ± 0.9
2.8 ± 0.2
9.8 ± 0.5
6.7
93.6

KFD
10.8 ± 0.5
25.8 ± 4.6
23.2 ± 1.6
33.2 ± 1.7
23.7 ± 2.2
16.1 ± 3.4
4.8 ± 0.6
1.5 ± 0.1
10.5 ± 0.6
4.2 ± 2.1
23.2 ± 2.0
2.6 ± 0.2
9.9 ± 0.4
9.1
86.0

AB
12.3 ± 0.4
30.4 ± 4.7
26.5 ± 2.3
35.7 ± 1.8
27.5 ± 2.5
20.3 ± 3.4
2.7 ± 0.7
1.9 ± 0.3
10.1 ± 0.5
4.4 ± 2.2
22.6 ± 1.2
3.0 ± 0.3
10.8 ± 0.6
14.5
94.8

ABR
10.9 ± 0.4
26.5 ± 4.5
23.8 ± 1.8
34.2 ± 2.2
24.3 ± 2.1
16.5 ± 3.5
2.7 ± 0.6
1.6 ± 0.1
9.5 ± 0.7
4.6 ± 2.2
22.6 ± 1.2
2.7 ± 0.2
9.8 ± 0.8
4.7
90.8

regularisation of the hyper-parameters. J. Mach.
Learn. Res., 8:841–861, May 2007.

parameter that has theoretical grounding is superior toa classifier with an unknown regularization parameter.

6

SVM
11.5 ± 0.7
26.0 ± 4.7
23.5 ± 1.7
32.4 ± 1.8
23.6 ± 2.1
16.0 ± 3.3
3.0 ± 0.6
1.7 ± 0.1
10.9 ± 0.7
4.8 ± 2.2
22.4 ± 1.0
3.0 ± 0.2
9.9 ± 0.4
7.8
91.1

[2] M. Lobo, L. Vandenberghe, S. Boyd, and H. Lebret. Applications of second-order cone programming, Nov. 1998.

Conclusions[3] S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and
K.R. Mullers. Fisher discriminant analysis with kernels. In Neural Networks for Signal Processing IX,
1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop, pages 41 –48, August 1999.

In this research, I have proposed a “quadratically constrained maximum a posteriori” (QCMAP) estimation
procedure and proven that it reduces to LSR and the
SVM upon choosing appropriate weighting functions.
Furthermore, I have proposed two novel classifiers“Gaussian QCMAP” (GQCM) classifier and “robust
QCMAP” (RQCM) classifier. As the experimental results, the usefulness and significance of QCMAP classifiers have been demonstrated.

[4] Nopriadi and Y. Yamashita. Maximum a posteriori
based kernel classifier trained by linear programming. LNCS, 6218:493–502, 2010.
[5] G. Rätsch, T. Onoda, and K.R. Müller. Soft margins for adaboost. Tech. Rep. NC-TR-1998-021,
Royal Holloway College, University of London, UK,
42(3):287–320, 1998.

References[6] 小島政和, 水野真治, 土谷隆, and 矢部博. 内点法. 朝
倉書店, 2001.

[1] Gavin C. Cawley and Nicola L. C. Talbot. Preventing over-fitting during model selection via Bayesian92Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Selective catalytic reduction of NO over TiO2-ZrO2
supported metal catalysts
Student Number: 09M18137

Name: Yang WANG

Supervisor: Hirofumi HINODE

TiO2-ZrO2 担持金属触媒による NO 選択還元王 楊
本研究では、TiO2-ZrO2 担持金属触媒を用い、過剰酸素存在下でプロペンを還元剤とする NO の選択還元反応の活性について検討した。TiO2-ZrO2 へ Mn2O3 を機械的に混合したところ、活性が向上し、250℃付近で NO を N2 への転化率が 71%であった。反応系に水蒸気が存在する条件で同様な実験を行ったところ、水蒸気存在下での失活は観測されなかった。そこで、NO が生成した NO2 とプロペンが反応して N2 を生じる反応機構が推定できる。

1. Introduction
Based on these reports, Mn2O3 was selected as metal oxidethat would be added to the catalyst, and the activity ofdeveloped catalyst was tested.
Therefore, in this study, the catalytic activity ofNb-supported TiO2-ZrO2 (Nb/TiO2-ZrO2) catalyst andMn-supported TiO2-ZrO2 (Mn/TiO2-ZrO2) for the reductionof NO with propene in the presence of O2 was investigated.

Nitrogen oxides (NO, NO2 and N2O) are the major
sources of air pollution and can cause photochemical smog,
acid rain and ozone depletion. About 50% of these nitrogenoxides are emitted from automobiles [1]. In automobiles,
three-way catalysts are used to elimate HC, CO and NOx
simultaneously at stoichiometric condition. However, it
requires specific air-fuel ratio and cannot work in lean-burncondition. Therefore, it is necessary to develop new
catalysts to reduce nitrogen oxides in lean-burn condition.
Among all treatment methods, selective catalytic reduction
by unburned hydrocarbons existing in exhaust gas(HC-SCR) is gaining attention, because of its potential
ability to remove NO from oxygen-rich exhaust. However,there is still no practical catalyst that has been discovered so
far.
TiO2-ZrO2 is known of its high BET surface area andstrong mechanical strength, and is widely used in many
reactions as catalyst [2]. A study on the performance ofCu-supported TiO2-ZrO2 in HC-SCR reported catalytic
activity [3]. On the other hand, Nb-supported TiO2 andMn-supported TiO2 were also investigated [4] [5] [6].
It was also suggested that NO reduction is carried out by
the reaction to generate NO2 followed by the reaction of
NO2 with hydrocarbons [4].Metal oxide was thought toaccelerate the formation of NO2 [5][6][7].Therefore,
researchers are aiming at increasing activity of the catalyst
further by adding metal oxide to catalyst. M. Misono et al.
showed that addition of Mn2O3 to Ce-ZSM-5 was enhanced
the oxidation of NO to NO2 and oxidative decomposition oforganic intermediates containing nitrogen and oxygen[8].

2. Experimental
2.1 Materials
First, TiO2-ZrO2 composite support was prepared byco-precipitation
method
[9].
And
then
M/TiO2-ZrO2(M=Nb,Mn)) was prepared by impregnation
method using the following procedures:
The NH4[NbO(C2O4)2(H2O)2](H2O)7 or MnCO3 ・H2O
was added into 300 mL of ion-exchanged water and thenthe TiO2-ZrO2 was added to the mixture followed bystirring at room temperature for 24 hours. After that, themixture was dried up at about 100°C for 24 hours, andcalcined at 550°C for 4 hours under air flow.
(Mn+Nb)/TiO2-ZrO2 was prepared by co-impregnation
method by impregnating TiO2-ZrO2 with Nb and Mn
simultaneously. (Mn-Nb)/TiO2-ZrO2 was prepared byconsecutive impregnation method from TiO2-ZrO2 with
Nb/TiO2-ZrO2 prepared firstly followed by impregnation
with Mn using MnCO3・H2O as precursor.
Mn2O3+(TiO2-ZrO2) was prepared by mixing Mn2O3 andTiO2-ZrO2 mechanically. Mn2O3 was prepared by93

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

calcination of MnCO3・H2O at 600°C for 3 hours under air
flow. The mechanically mixed sample was then calcined at
550 °C for 4 hours under air flow.
Finally, all catalysts were pelletized, crushed and sieved
into particles with diameter between 0.71~1.00 mm.

were
also
investigatedforco-impregnated(Mn+Nb)/TiO2-ZrO2 and consecutively impregnated(Mn-Nb)/TiO2-ZrO2 catalysts. The results were shown in
Figure 3 .The loading levels of Nb and Mn were 15wt.%
and 1wt.%,respectively.The catalytic activities of both
catalysts decreased. For consecutively impregnated catalyst,
it is possible that Mn covered Nb active site byimpregnation and oxidized C3H6 to CO2 before C3H6 could
function as reducing agent. This could lead to the decreaseof catalytic activity.

2.2 Catalytic Activity Measurement
The catalytic activity measurement was carried out in a
fixed-bed flow reactor. The reactant gas composition was asfollowed:1500 ppm of NO, 1500 ppm of C3H6, 10% of O2,
and helium as a balance gas with a total space velocity of
13000 h-1 over 0.8 g of catalyst. The reaction temperature
was increased stepwise at intervals of 50°C starting from
150°C.
The concentrations of NO and NO2 were measured by
NOx analyzer (NOA-7000, Shimadzu Corp.). N2O and
CO2 were analyzed by gas chromatographs (GC-8A,
Shimadzu Corp. and GC-390, GL Science CO.)respectively. Catalysts were characterized by TG-DTA,
XRD and BET.
The influence of water vapor on the activity of catalyst
was investigated by introducing 10 vol. % of water vapor
during the reaction. Gas composition and analysis apparatus
were the same as stated above.

Fig.1 Influence of Nb loading levels on Nb/TiO2-ZrO2for the
reduction of NO to N2.

3. Results and Discussion
3.1 Catalytic Activity of M/TiO2-ZrO2(M=Nb, Mn)
Figure 1 shows the conversion of NO to N2 overNb/TiO2-ZrO2 catalysts with Nb loading levels from
0-20wt.%.Every Nb loaded catalyst showed lower catalytic
activity than that of bare TiO2-ZrO2.It can be thought thatNb participated in the NO reduction and the optimalloading level of Nb was 15wt.%.
Figure 2 shows the effect of Mn loading levels on the
catalyst activity of Mn/TiO2-ZrO2. The loading level of Mn
was 0-15wt.%.The conversion of each Mn loaded catalyst
decreased. However, the peak temperature of each Mn
loaded catalyst shifted to lower temperature. The optimalloading level of Mn was 1wt.%.
3.2 Catalytic Activity of (Mn+Nb)/TiO2-ZrO2 and(Mn-Nb)/TiO2-ZrO2

Fig.2 Influence of Mn loading levels on Mn/TiO2-ZrO2 for the
reduction of NO to N2.

Influence of Mn loading levels on the catalytic activities

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The conversion of NO to N2, NO2 and N2O, and oxidationof C3H6 to CO2 over mechanically mixed
Mn2O3+(TiO2-ZrO2)(1:9) catalyst are shown in Figure 5.
The activities of NO conversion to N2 and C3H6 conversion
to CO2 were improved at low temperature . Small amountof N2O was produced in the low temperature region below
350C. NO2 formation decreased in the temperature region
where the conversion of NO to N2 was high, and increased
again in the temperature region where the conversion of
NO to N2 decreased. This result confirmed the reaction
mechanism that NO was oxidized to NO2 before it was
subsequently reduced to N2. NO2 formation was accelerated
at higher temperature because of the oxidation. This
indicated that addition of Mn2O3 enhanced formation of
NO2 and activation of propene. Up to 250°C, theconversion of C3H6 to CO2 showed similar tendency withthe conversion of NO to N2 in which the conversion
increased with increasing temperature. It means C3H6 wasused as reducing agent to produce N2. At temperature
higher than 250°C, the conversion of C3H6 to CO2
increased until it reached at 100%.In this region, C3H6 wasprobably just oxidized by reaction with O2.
Catalyst should be resistant to water vapor for practical use
because exhaust gas normally contains water vapor. Figure
6 shows comparison of mechanically mixed
Mn2O3+(TiO2-ZrO2)(1:9) catalyst activity with or without
the presence of water vapor. Because of the water vapor, theconversion of NO to N2 shifted to higher temperatureslightly although no activity inhibition was observed. The
reason is considered that the high catalytic activation underhigh temperature condition. Another is that the active sites
for the NO reduction to N2 are activated by water vapor.
On the other hand, in this experiment, the water vapor did
not show obvious effect on the C3H6 combustion. NO
conversions to N2O and NO2 profile (not shown in the
figure) were similar regardless of the existence of water
vapor.

Fig.3 Comparision of (Mn+Nb)/TiO2-ZrO2,(Mn-Nb)/TiO2-ZrO2 and Nb catalyst activities

3.3 Catalytic Activity of Mn2O3+(TiO2-ZrO2)
Figure 4 shows the conversion of NO to N2 overmechanically mixed Mn2O3+(TiO2-ZrO2) catalyst using
C3H6 as a reducing agent .For all mechanically mixed
catalysts, the maximum conversion of each catalyst shiftedto lower temperature. Mechanically mixed Mn2O3 +(TiO2-ZrO2) with ratio of (1:4) and (1:9) showed higher
catalytic activity than that of TiO2-ZrO2. The conversion of
NO to N2 over Mn2O3+ (TiO2-ZrO2)(1:9)catalysts showedmaximum conversion of NO to N2 reached 71% at 250°C.

Fig.4

Influence of Mn2O3:(TiO2-ZrO2) ratio on
mechanically mixed Mn2O3+(TiO2-ZrO2) catalyst

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The surface area of TiO2-ZrO2 catalyst after calcination
was 252.6m2/g. After impregnation with Nb or Mn, thesurface area decreased. It was suggested that no obvious
relationship between BET and NO activity by many
researchers [3] [5] [6].And this was also confirmed in thisexperiment.

4. Conclusion
Enhancements of C3H6-SCR activity were observed when
TiO2-ZrO2 was mechanically mixed with Mn2O3.The
maximum conversion of NO to N2 was 71% at 250°C.
The effect of Mn2O3 addition to Nb/TiO2-ZrO2 catalyst
depends on the preparation method. Mn2O3 added byimpregnation was thought to cover the active site ofNb/TiO2-ZrO2.
Mechanically mixed Mn2O3+(TiO2-ZrO2)(1:9) catalyst did
not show the deactivation caused by water vapor. As for thereaction mechanism, it is proposed that NO is oxidized toNO2 and then reacts with C3H6 to N2.

Fig.5 Catalytic activity of Mn2O3+(TiO2-ZrO2)(1:9)

References[1] H.Schneider, U.Scharf, A.Wokaun, A.Baiker,
J.Catal.147, 545 (1994)[2] B.M.Reddy, P.M.Sreekanth, Y .Yamada, Q.Xu.
T.Kobayashi, Appl.Catal. A 228(2002) 269-278.
[3]
N.Sakata: Master Thesis, Tokyo Institute ofTechnology (2003)[4]
H.Kawai: Master Thesis, Tokyo Institute ofTechnology (2004)[5]
C.Tang: Master Thesis, Tokyo Institute ofTechnology(2006)[6]
M.Makii: Master Thesis, Tokyo Institute ofTechnology(2006)[7]
H.Hamada, Y.Kintaichi, M.Sasaki and T.Ito and
M.Tabata, Appl. Catal,70,L15(1991)[8]
M.Misono, et al,Catal.Today,38 (1997)157[9]Kazuhiro Ito, Shuichi Kakino, Keita Ikeue, Masato
Machida, “NO adsorption/desorption property of
TiO2-ZrO2 having tolerance to SO2 poisioning,”Appl. Catal.
B 74 pp.137-143, February 2007

Fig.6 Catalytic activity of
Mn2O3+ (TiO2-ZrO2) (1:9) with water vapor.

3.4 Characterization of Catalyst
The BET surface areas of Nb catalysts and Mn catalysts
are summarized in Table 1.
Catalysts(After calcination)
TiO2-ZrO2Specific Surface
Area [m2/g]
252.6

Mn1%/(TiO2-ZrO2)

45.6

Nb15%/(TiO2-ZrO2)

67.4

Mn2O3+(TiO2-ZrO2)(1:9)

42.0(Mn1%+Nb15%)/(TiO2-ZrO2)

50.1

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Applicable condition of linear approximation for boost switching regulator〜For charging tiny electric energy to capacitor〜
Student Number: 09M18143

Name: HUANG Shu

Supervisor: TAKAHASHI Kunio

昇圧スイッチングレギュレータ回路に対する線形近似解の適用可能条件〜些末電力のキャパシタに対する蓄積のための研究〜
黄舒

スイッチングレギュレータの昇圧回路方式を用いて，キャパシタにエネルギーを蓄積するシステムを設計するため，解析解が有用である．本研究では，解析解を求めるために，スイッチングレギュレー
タの昇圧回路の線形近似モデルを構築し，そのモデルに対する解析解の適用可能条件を理論的および実験的に検討した．

1

Introduction

2

TheoryThough there is a lot of tiny electric energy around 2.1 Linear approximation model
us such as light, winds, water ﬂow and so on, the enFigure 1 shows the switching regulator.
ergy has not been used yet. If it is possible to charge
the renewable energy around us, the anxiety to the future of humanity can be reduced. But it is necessary
to design an eﬃcient charge system to use it, because
the renewable energy is so tiny. At present, in the useof renewable energy, the provide of steady energy has
been required [1]. But it is worth designing from theFig. 1: switching regulator

In the switching regulator, the inductor, FET andviewpoint of charge the renewable with a high energy diode are nonlinear, it is diﬃcult to obtain analyticaleﬃciency, even if the provide of steady energy is un- solution by using the nonlinear parameters. Therefore,
steady.
considering the internal resistance of the inductor, FET
As a charge device, compared with storage bat- and diode, a linear approximation model is showed astery,capacitor is used for high energy eﬃciency. For Figure 2.
charging tiny electric energy, a booster is needed beThen, as the input voltage from the tiny electric encause the capacitor can eﬃciently charge energy with ergy, there are a variety of power supplies. In order tohigh voltage.The switching regulator is used for boost- design the optimum parameters of the switching reging the voltage [2].
ulator,the input voltage Vin is assumed to be a simFor charging the energy with high eﬃciency with ple power supply, a constant-voltage DC power supply,
switching regulator,it is necessary to design a switching showed as Figure 2.
regulator and analytical solution is important. Therefore, a linear approximation model is necessary to obtain analytical solution and it is useful to examine theapplicable condition of the approximation model.
Therefore, In order to design an eﬃcient charge system for charging TEE to capacitor, it is necessary to

Fig. 2: Linear approximation model-1

In the model, L is the inductance of the inductor,
introduce the linear approximation model of switchinductor, FET and diode are approximated as the reing regulator and verify the applicable condition of thesistances rL , rD , rS for considering the energy loss of
linear approximation model theoretically and experithe inductor, FET, diode. T1 is the time interval whenmentally.
the switch on 1, T2 is the time interval when the switch

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on 2, T is the switching period, T = T1 + T2 , D is the(7)

duty ratio, D = TT2 .
Where,
L, rL , rD , rS are the circuit parameters, T and D are
R1 T1 ˜R1 T2Voutthe control parameters. For charging the tiny electric
T˜1 =, T2 =, ã =L
L
Vin
energy, it is necessary to determine the control paramR2
T˜2
eters.
, T̃ = T˜1 + T˜2 , r̃ =
D=
R
T̃
1
Then, in order to determine the control parameters,2
the voltage of capacitor is assumed to be a constant r̃ = R
R1 is approximated as 1, because the internal revoltage, for assuming the capacitor with large enough sistance of inductor is larger than FET and diode.
The electric charge can be expressed by normalized
capacity as the voltage of the capacitor does not change
parameters as eq.(8).
in one period.

Vin
q=
R1

[

ã (1 − e−(1−D)T̃ )(1 − e−DT̃ )− (ã − 1)DT̃(1 − e−T̃ )](8)Fig. 3: Linear approximation model-2

From ã = VVout, ã is larger than 1. Therefore, (ã −in

i1 is the inductor current when the switch on 1 and 1)D is positive. When f (D, T̃ ) as eq.(9) is large, The
i2 is the inductor current when the switch on 2. The electric charge will be large.
positive direction of iL is showed in Figure 2.
When the switch is on 1,
f (D, T̃ ) =di1
Vin = (rL + rS )i1 + L
dtWhen the switch is on 2,(1)

1 (1 − e−(1−D)T̃ )(1 − e−DT̃ )T̃
(1 − e−T̃ )(9)Figure 4 shows the relation between T̃ and f (D, T̃ ).
The horizontal axis is T̃ , the vertical axis is f (D, T̃ ).

di2(2) From ﬁgure 4, when T̃ is small, f (D, T̃ ) will be large.
dt
R T
From the condition of continuity of current, showed From T̃ = L1 , the inductance of the inductor should
be large.
as eq.(3) and eq.(4).
Vin − Vout = (rL + rD )i2 + L

i1 (t = 0) = i2 (t = T2 )(3)

i1 (t = T1 ) = i2 (t = 0)

(4)

The inductor current i1 and i2 can be expressed aseq.(5) and eq.(6).
i1 =
i2 =

(1 − e(1 − e−R2 T2L

1−e−R1 T1
L

1−e

−Vout
)( Vin R− VRin1 )2−(R1 T1 +R2 T2 )L)( Vin − Vin −Vout )R1
R2−(R1 T1 +R2 T2 )L

−R1

e L t+−R2

e L t+Vin(5)
R1

Vin − Vout
R2
(6)

Fig. 4: The relation between T̃ and f (D, T̃ )Where,

When T̃ is small, the electric charge per a unit of
1 2
1) + 4ã], whenthe capacitor approaches VRin1 [−ã(D − 2ã1
D = 2ã .

R1 = rL + rS , R2 = rL + rD , T = T1 + T2

The electric charge per a unit time of the capacitor
Therefore, as control parameters, the duty ratio can
1can be expressed as eq.(7).
be controlled by D = 2ã= 2VVin.
out∫ T2
i 2 dtQ= 0
q=
2.2 Applicable condition of linear apTT−R1 T1−Vout
proximation(1 − e L )( VRin1 − Vin R)−R2 T2
1
2
L
=
[L
(1
−
e
)
−(R1 T1 +R2 T2 )
R2 T
L
1−e
In the linear approximation model, The diode is ap+ (Vin − Vout )T2 ]proximated to resistance. The reverse current might98

Technical Report of International Development Engineering

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ﬂow to the inductor when the voltage of the capaci-

At T̃ = 3.67, the experiment is done at A11 , D =tor grows. In the experiment, the diode is used, the 0.48, ã = 0.7, A12 , D = 0.48, ã = 1.04 and A13 , D =reverse current can not ﬂow to the inductor. The in- 0.48, ã = 1.28.
ductor current in the model should be positive.
In the same way, at T̃ = 1.83, the experiment is done
When t = nT (n > 0),
at A21 , D = 0.37, ã = 0.96, A22 , D = 0.37, ã = 1.35 andA23 , D = 0.37, ã = 1.7. AtT̃ = 1.22，the experiment is−R2 T2−Vout(1 − e L )( Vin R− VRin1 ) −R1 t Vin
2i1 => 0 done at A31 , D = 0.21, ã = 1.9, A32 D = 0.21, ã = 2.36
e L +−(R1 T1 +R2 T2 )
R
1
L
1−e
and A D = 0.21, ã = 2.74.
33

(10)
−T̃

D<−

ln(1 − 1−eãThe experiment result of 9 points showed as ﬁgure
6.

)

(11)

T̃
R1 T
Vout
T̃ =
, ã =
LVin

1, the applicable condition
From eq.(11) and D = 2ã
of linear approximation can be showed by ﬁgure 5.

From ﬁgure 5, whenVin = 0.5V, Vout = 500V, the input voltage is boosted to 1000 times, the duty ratio can
1
be controlled by D = 2ãwhen T̃ = 0.5, 1. But whenT̃ = 2, 4, 6, the reverse current ﬂows to the inductor.
When the reverse current ﬂows to the inductor, theFig. 6: Applicable condition of linear approximation atT̃ = 3.67, 1.83, 1.22

1duty ratio can not be controlled by D = 2ã
.

In order to measure the inductor current, a 10Ω resistance is placed between power supply and inductor.
The voltages are measured by oscilloscope. In the experiment, the voltage of the resistance and capacitor,
and the switching frequency are measured as ﬁgure 7.

Fig. 5: Applicable condition of linear approximation
1
D = 2ã=−−T̃ln(1− 1−eãT̃)Fig. 7: Experimental method

is the boundary conditionof linear approximation.
1
Then, the duty ratio D can be controlled by D = 2ã
from ã = 0 to the boundary condition.

3

Experiments

3.1

Experimental method

3.2

Experimental equipments

In the experiment, in order to examine the applicable condition of the linear approximation, the inductor
current and the booster voltage are measured at 3 kindsof conditions, in the range of applicable condition, theboundary condition of linear approximation and out ofFig. 8: Experimental equipments
According to section 2.1, the circuit parameters havebeen determined, FET is N channel MOSFET, K2232,
the applicable condition..
A function generator is used to control the switching diode is CMS01, inductor is SLF10145, capacitor is
SMG series. The internal resistances of inductor, FET

frequency of the FET.
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Technical Report of International Development Engineering

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are 12.1Ω, 0.05Ω and the internal resistance of diode isulator, the applicable condition of linear approxima-changed between 5Ω and 15Ω because it is nonlinear. tion has been examined in the linear approximation
The inductance of inductor is 3300µH and the capacity model theoretically and experimentally and the appliof capacitor is 22000µF.
cable condition of linear approximation has been clarFigure8 shows the experiment equipments, by the iﬁed.
inductor, FET, diode and capacitor.
0.025

Inductor current iL (A)Experimental results

In the experiment, the inductor current and thebooster voltage at 9 points are measured. Here, theexperimental results of points A21 , A22 , A23 are showed

0.020

0.4

0.015
0.2
0.010

Booster voltage V (V)

3.3

〜

〜

when T̃ = 1.83.

0.005

T=12.1T/0.0033
T=0.0005
0.0000T=1.83
D=0.37a=0.96〜0.0005Time t (s)0.0010

0.0

Fig. 10: The inductor current and the booster voltage
at A21

0.6

0.4
0.01
0.2

Booster voltage V (V)Inductor current iL (A)

0.02

〜

〜

0.00

Fig. 9: Booster voltage when T̃ = 1.83, D = 0.37

T=12.1T/0.0033
T=0.0005
0.0000T=1.83
D=0.37〜a=1.350.0005Time t (s)0.0010

0.0

Figure 9 shows the booster voltage when T̃ =Fig. 11: The inductor current and the booster voltage
1.83, D = 0.37.
at A22
Figure 10, Figure 11, Figure 12 shows the inductor
current and the booster voltage at the point A21 , A22 ,A23 . The horizontal axis is time, the left of the vertical

0.8
0.02
inductor current iL (A)ages agree with the theoretical booster voltage, v =0.48V, v = 0.63V, v = 0.86V , are almost constant.

0.6
0.01
0.4

Booster voltage V (V)axis is the inductor current and the right of the verticalaxis is the booster voltage.
From the results, at A21 , A22 , A23 , the booster volt-

0.00〜

T=12.1T/0.0033
T=0.0005〜

T=1.83
D=0.37
〜
a=1.7

0.2

0.0
-0.01
0.00000.00050.0010
The theory and the experiment of inductor currents
Time t (s)almost agree atA21 , A22 . At A22 the inductor current is
0A, when t = 0.0005s. But the inductor current does Fig. 12: The inductor current and the booster voltage
not agree at A because of the diode. The experi- at A23

23

mental result shows that the linear approximation isapplicable at point A21 , the point A22 is the boundary Referencescondition of linear approximation and the point A23 is[1] Jonathan W.Kimball, Theresa L.Flowers,Patrick
the condition can not be linear approximated.
L.Chapman, Low-Input-Voltage,Low-Power Boost
From the results, the applicable condition of theConverter Design Issues, IEEE POWER ELECmodel has been examined experimentally.
TRONICS LETTERS, Vol. 2,NO. 3,(2004)

4

Conclusion[2] Otome Keisuke, Eﬀects of conditions of use on theenergy eﬃciency of boost switching regulator, mas-

To design the system to charge the tiny electric energy in the capacitor by using the boost switching reg100

ter thesis, (2010)

Technical Report of International Development Engineering

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Decomposition of nitrous oxide overrutile structure titania supported metal catalysts
Student Number：09M18185

Name：Peng GAO

Supervisor：Hirofumi HINODE

ルチル型チタニア担持金属触媒による N2O 分解
高鵬

本研究では、３種類のルチル型チタニア担持金属触媒（鉄、コバルト、ニッケル、バリウム）の N2O 分解活性について検討した。ルチル型チタニア(602X03)担持コバルト(10%)触媒は最大活性を示し、酸素がない場合で 550℃において N2O を完全に分解した。10%存在下では N2Oの 100％分解温度は高温側にシフトし、触媒活性を抑制することを確認した。触媒のキャラク
タリゼーションは触媒のキャラクタリゼーションはＸ線回折に結晶構造解析、ＢＥＴ比表面積測定などにより行った。
1. Introduction
Nitrous oxide (N2O) is a greenhouse gasthat greatly contributes to the green houseeffect and severely destructs the ozone layer
in the stratosphere. The major stationary
sources of nitrous oxide are the adipic acidand HNO3 processing units, which areresponsible for about a quarter of worldwide
emissions. Agriculture and fluidized bed
combustion processes also contribute to N2Oemission. Vehicles equipped with three-way
catalysts contribute as the mobile emission
sources when the three-way catalysts
deteriorate due to prolonged use. With theincrease in concern for environmental
protection, efforts have been made to convert
N2O into N2 and O2 before it is released into
the atmosphere.
Previous study showed that rutile
structure titania had better activity thananatase structure on N2O decomposition
during activity test using Cu/TiO2 catalysts[1].
In this study, the decomposition of N2O
over rutile structure TiO2 supported Fe, Co,
Ni and Ba catalysts was investigated.

were calcined at 600℃ for 5 hours under air
flow. In order to reduce pressure drop, thecatalysts were pelletized, crushed and sieved
to between 0.71mm to 1.00mm. The
following nomenclatures for the catalyst
samples are used: Fe(x wt%)/TiO2 (y) wherex means Fe loading levels, and y means
TiO2.
The sources of Co, Ni and Ba were(99.5%,Wako
Co.),
Co(NO3)26H2O
Ni(NO3)2 ・ 6H2O (99.9%, Wako Co.) andBa(NO3)2 (99.9%, Wako Co.), respectively.

2.2 Catalytic activity experiment
The catalytic reaction was carried out in a
fixed-bed flow reactor under atmosphericpressure, and the reactant gas was preparedby mixing N2O, O2 and He as a balance gas.
Then the mixed gas of 1000ppm N2O,
0%/10% O2 and He was fed to the catalyst atflow rate of 5.0-6.0ml/s, which correspond to16000h-1 of space velocity.
Gas chromatography (GC323w; GL
Science Co., with Porapak N, Porapak Q and
Molecular Sieve 13X column) was used toanalyze N2O, N2 and O2.

2. Experimental
2.1 Catalyst preparation
M/TiO2 (M=Fe, Co, Ni, Ba) catalysts wereprepared by impregnation method. TiO2
(602X03, 602X05, 603X03; TohoTitanium
co.,LTD.) were used as supports.
In the preparation of Fe/TiO2(example),
TiO2 was impregnated in an aqueous

2.3 Catalyst characterization
Characterizations of catalysts were
performed by TG(TG8120), XRD(MultiFlex),
BET(SA3100)
and SEM(JSM-5310LV)-EDS(JED-2140).
3. Result and discussion
3.1 The activity for the decomposition of N2O
over M/TiO2(602X03) (M = Fe, Co, Ni, Ba)
Figure 1 shows the catalytic activity of
M/TiO2(602X03) (M= Fe, Co, Ni, Ba)
catalysts for N2O decomposition. The axis issolution of Fe(NO3)39H2O (99.9%, Wako Co.).
Each of the solution was stirred at room
temperature for one day followed by 12hours drying up at 70℃. Then the catalyst

101

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the reaction temperature. The shift to lower
temperature region means high activity. The
loading level for each sample here wasselected from the loading level of the sample
with the best performance result.
NO and NO2 were not detected in allcondition.

Fe/TiO2 for N2O decomposition preparedfrom TiO2 (602X03). Fe loading levels(1wt%,
5wt%, 10wt%) of Fe/TiO2(602X03) showedalmost the same activities for N2O
decomposition. Therefore the optimum Fe
loading levels of Fe/TiO2(602X03) was 1wt%.

Fig.2 The catalytic activity for the decomposition
Fig.1 Comparison of the catalytic activity for thedecomposition of N2O over each catalyst without oxygenof N2O over Fe/TiO2(602X03)

Figures 3 showed the catalytic activity of
Co/TiO2 for N2O decomposition preparedfrom TiO2 (602X03). The optimum Co
loading levels of Co/TiO2(602X03) was
10wt%.

Parent TiO2(602X03) calcined at 600 ℃
decomposed only 58% of N2O to N2 even at
700℃. When iron or cobalt was supported on
TiO2, the catalytic activity was promoted.
When Co loading level was 10wt%, thehighestcatalytic
activityforthedecomposition of N2O was achieved.
Decomposition of N2O to N2 overCo(10wt%)/TiO2(602X03) catalysts began at
350 ℃ , and the conversion of N2O to N2reached 100% at 550 ℃ without O2.
completely
Fe(1wt%)/TiO2(602X03)
decomposed N2O at 600℃ without O2.
Ni(10wt%)/TiO2(602X03) decomposed 60%
of
N2O
at
700
℃
,
and
Ba(10wt%)/TiO2(602X03) decomposed only
approximately 30% of N2O even at 700℃.
According to XRD results, there was only
NiTiO3 or BaTi2O5 peaks in the catalyst after
calcinations while no NiO or BaO peaks
appeared. The reason was that the precursor
Ni(NO3)2 and Ba(NO3)2 reacted with TiO2
during 600 ℃ calcinations, and catalyst
activity was inhibited.

Fig.3 The catalytic activity for the decompositionof N2O over Co/TiO2(602X03)

Table 1 shows the relationship between
the BET specific surface area of TiO2 and the
optimummetalloading
levelofM/TiO2(M=Fe, Co) from the catalytic activity
experiments for N2O decomposition andgeometrical calculation from MxOy andM(NO3)x projected area.

3.2 The optimum Metal loading level and theeffect of TiO2 as support
Figures 2 showed the catalytic activity of102

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Table 1 Relationship between specific surface area of TiO2 and optimum Fe and Co
loading level for the decomposition of N2O
Support(metal)Specific surface

Optimum Metal

Optimum Metal loading

area (m2/g)

loading level

level from geometricallevel from geometricalbefore/after

from experimentcalculation of active site

calculation of precursor

2.89wt%0.93wt%（Fe2O3）（Fe(NO3)3）

12.56wt%

7.71wt%（Co3O4）（Co(NO3)2）

602X03(Fe)1wt%

73.80/32.85
602X03(Co)

10wt%

OptimumMetalloading

temperatures 400℃ and 600℃ showed thesame activity, but 700℃ showed the worse
activity than the others. The reason is thatCoTiO3 was appeared in the catalyst at
higher calcination temperatures. On theNi(10wt%)/TiO2 and Ba(10wt%)/TiO2, even ifon the lower calcination temperature 400℃,
the better activity was not showed.

The results from Table 1 indicate that the
optimum Fe and Co loading level obtained
from the catalytic activity experiments was
agreed well with on the calculated value
fromtheprojected
area
of
and
Co(NO3)2).
precursor(Fe(NO3)3
Therefore, when the TiO2 surface wascovered with Fe(NO3)3 or Co(NO3)2 molecule
completely, the catalytic activity for thedecomposition of N2O reached the highest
value. If the amount of Fe(NO3)3 or Co(NO3)2
loaded exceeded the optimum loading, thecatalytic activity remained the same or
declined because of sintering of Fe or Co.
Table.2 The optimum metal loading level of M/ TiO2
Catalyst

Optimum Metalloading level

T100(℃)

Fe/TiO2(602X05)

1wt%

600

Fe/TiO2(603X03)

1wt%

600

Co/TiO2(602X05)

5wt%

600

5wt%
600
*T100 means the temperature of 100% N2O
conversion.
Co/TiO2(603X03)

Fig.4 The catalytic activity for the decomposition of N2Oover

The optimum metal loading level of theother M/TiO2 were shown in Table 2. The
difference of the optimum Co loading level
was considered to be related with thedifference of the specific surface area of each
TiO2.

each

catalyst

withdifferent

calcination

temperatures.

3.4 The influence of oxygen
Figures 5 shows the catalytic activity ofFe(1wt%)/TiO2 and Co(10wt%)/TiO2 for N2O
decomposition prepared from 602X03 withoxygen(10%). The decomposition of N2O
shifted to the higher temperature by the
introduction of oxygen (10%). This result
indicated that conversion of N2O wasinhibited by the existence of O2. The reason
was that O2 compete with N2O in the processof adsorption on active site[2].

3.3 The effect of calcination temperature
Figures 4 show the catalytic activity ofCo(10wt%)/TiO2,
Ni(10wt%)/TiO2
andBa(10wt%)/TiO2 for N2O decomposition
prepared from 602X03 with different
calcination
temperatures.
On
the
the
calcination
Co(10wt%)/TiO2,

103

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

different calcination temperatures, show thesame XRD result.

Fig.5 The catalytic activity for the decomposition
of

N2O

over

Fe(1wt%)/TiO2(602X03)

andCo(10wt%)/TiO2(602X03) with oxygen(10%)

3.5 Characterization of catalysts
Figure 6 shows the results of XRD
patterns of each catalyst after calcinations.
In Fe(1wt%)/TiO2 and Co(10wt%)/TiO2,
Fe2O3 and Co3O4 peaks ware appeared, thatwas confirmed the activity site. NiTiO3 andBaTi2O5 peaks in the Ni(10wt%)/TiO2 andBa(10wt%)/TiO2, were not the activity site
for N2O decomposition.
Fig.7 XRD patterns of each catalyst with different
calcination temperatures

4. Conclusion
Fe/TiO2 and Co/TiO2 exhibited thecatalytic activity for N2O decomposition.
Co(10wt%)/TiO2(602X03) showed the best
catalytic activity, N2O was decomposedcompletely at 550℃ without O2. Ni/TiO2 andBa/TiO2 did not show the activity, because
during the calcinations the precursors
reacted with TiO2, and catalyst activity
was inhibited.
N2O decomposition was inhibited by theexistence of O2. In Co(10wt%)/TiO2(602X03),
the 100% N2O conversion was showed at
600℃.
Reference[1] K.Yanagida,
Masterthesis,“The
decomposition of nitrous oxide overtitania supported metal catalysts” Tokyo
Institute of Technology (2006).
[2] S. Suarez, M. Yates, A.L. Petre, J.A.
Martin, P. Avila and J. Blanco,“Development
ofanew
Rh/TiO2–sepiolite monolithic catalyst
for N2O decomposition” Appl. Catal. B
64 (2006), p. 302.

Fig.6 XRD patterns of each catalyst

Figure 7 shows the results of XRD
patterns of each catalyst with different
calcination
temperatures.
In
Co(10wt%)/TiO2 with 700 ℃ calcination
temperatures, CoTiO3 peaks was appeared,
and the activity was inhibited. In theNi(10wt%)/TiO2 and Ba(10wt%)/TiO2 with104

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Heavy Metal Adsorption by Zeolite from
Lake Sludge as Raw Material
Student Number: 09M18172

Name: Yan Shao

Supervisor: Hirofumi Hinode co-supervisor: Nakasaki Kiyohiko

湖沼汚泥を原料とした合成ゼオライトによる重金属吸着邵

妍

閉鎖水域である湖沼に汚染物質が汚泥とともに容易に蓄積され、悪臭や富栄養化等の様々な環境問題を引き起こす。一般的な処理方法として汚泥の浚渫が行われる。しかし浚渫汚泥の埋め立て処理は軟弱地盤の形成や高コスト等の問題点が伴っているため、汚泥の再資源化技術の開発が必要である。本研究では、シリカおよびアルミナに富んだ浚渫汚泥を吸着剤として利用できるゼオライトに転換する技術の開発を行った。種々の反応条件（アルカリ濃度、反応時間、反応温度、Si/Al のモル比およびアルカリ水溶液/固体汚泥質量比）
におけるアルカリ水熱合成によってゼオライト Na-P1 やゼオライト Analcime-C 等が生成できた。合成したゼオライトの吸着能力を比較するために陽イオン交換容量(CEC)の測定および重金属吸着実験を行った。
1. Introduction
could be synthesized from lake sludge usinghydrothermal treatment in sodium hydroxide
solution medium3).
In this study, the hydrothermal syntheses ofzeolite from lake sludge in alkali solutions at
various Si/Al ratios were carried out and theeffect of reaction conditions were investigated.
Furthermore, the application of the synthesizedzeolites was also investigated.

Nowadays, water pollution is becoming aserious environmental problem. Some of thewater sources are river, lake and groundwater.
Here, we focused on lake because it is a closed
water system where pollutants could beaccumulated easily. Generally, the sludge in thebottom of a lake is dredged to remove thepollutants and then it is disposed by landfilling.
However, there are some problems such as theformation of soft-ground and the shortage oflandfilling site. Therefore, an effective use of
the dredged lake sludge must be considered.
The main components of lake sludge aresilica and alumina, which are the same as themain components of zeolites. Previous studies
reported that coal fly ash containing silica andalumina could be converted into zeolites1-2).
Other studies also reported that zeolites

2. Experimental
2.1 Synthesis of zeolite
Lake sludge from Kasumigaura was used inthis study. After being crushed to particle size
less than 150 μm, the sludge was dried ato
105 C for 24 hours and sieved. Prior tohydrothermal treatment, lake sludge was mixed
with sodium hydroxide (NaOH) solutions with105

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

different concentrations and was shaken for 24hours in a 23 ml Teflon reaction vessel. Molar
ratio of silicon to aluminum (Si/Al) was
adjusted by adding Na2SiO3 solution and/or
Al(OH)3. The vessel was then put inside an
autoclave and incubated at a temperature of 90oto 150 C in an oven for the hydrothermal
treatment. After the reaction, the slurry wasfiltered and the solid phase was washed several
times with distilled water to eliminate theexcess sodium hydroxide solution.

3. Results and discussion
3.1 XRD patterns of zeolite products
Figure 1 shows XRD patterns of zeolite
products obtained at different concentrations of
NaOH solutions. Si/Al was 1.9 which is themolar ratio of Si to Al in the raw lake sludge(no adjusting by Na2SiO3 or Al(OH)3) andoheating temperature (T) was 120 C. At NaOH
concentrations of 1 to 2 M, zeolite NaP1 phase
was observed. Quartz phase that is the main
component of sludge was still present. At
NaOH concentration of 4 M, Sodium
Aluminum Silicate Hydrate phase was detected.
Hydrothermal treatments in alkali mediums
with different NaOH concentrations could yield
different types of zeolite phases.

2.2 Characterization
The identifications of crystalline materials in
sludge and synthesized zeolite products werecarried out by X-ray diffraction analysis (XRD).
The surface structure was observed by scanningelectron microscopy, energy dispersive X-ray
spectroscopy (SEM-EDS).
2.3 Cation exchange capacity (CEC)
measurement
The cation exchange capacity of zeolite
product was determined by sodium acetate
method. The sample was saturated with NaOAc
solution, and then the Na+ was extracted by
NH4OAc solution. The amounts of released
Na+ were measured using Inductively Coupled
Plasma – Atomic Emission Spectorometry(ICP-AES).

Figure 1 XRD patterns of products prepared withdifferent NaOH concentrations: Sodium Aluminum Silicate Hydrate(1.08Na2O!Al2O3!1.68SiO2!1.8H2O);:Quartz (SiO2); :Zeolite Na-P1 (Na6 Al6Si10O32.12H2O)

2.4 Heavy metal adsorption
The synthesized zeolites were evaluated on
their capability to remove heavy metal inaqueous solutions. 50ml of solution containing
different concentrations of Pb2+ or Cd2+ werepoured into a glass bottle with 0.5g sample andwere shaken at 150rpm. After equilibration
period, solid and liquid were separated bycentrifugation and filtration (0.45μm membranefilter). The concentrations of Pb2+ or Cd2+ insolution were determined by ICP.

Figure 2 XRD patterns of products prepared at different
heating temperatures (T): Zeolite Analcime-C (Na(Si2Al)O6.H2O)

106

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

different treatment temperatures.

XRD patterns of the products synthesized byhydrothermal treatments at different heating
temperatures were shown in Figure 2. The
amount of quartz was reduced with increasing
temperature which indicated that Si was
dissolve at higher treatment temperature. The
dissolved elements (Si and Al) then formedozeolite Na-P1 type phase at 90-120 C andotransformed into zeolite Analcime-C at 150 C.

CE C [me q/ 1 0 0 g]

400

Si/Al=1.5
Si/Al=1.9
Si/Al=2.5
Si/Al=3.0

300
200
100
0

The liquid to solid ratio (L/S), which was theratio of the volume of NaOH solution to theweight of raw sludge in Teflon reaction vessel,
was changed from 2 to 8 ml/g. The XRD
patterns of the products were shown in Figure
3. Only zeolite Na-P1 and quartz could beobserved at all L/S values. Because the shapeof all graphs almost the same, so the reaction
temperature is still low could be considered.
The L/S ratio showed no effect on the type ofzeolite formed.

60

90

120
Temperature [℃]

150

180

Figure 4 CEC of products obtained on various Si/Al
ratios and temperatures

CEC of products reached optimum values ato
120 C as the treatment temperature wasoincreased from 90 to 150 C. The change in
Si/Al ratios from 1.5 to 3.0 also showed an
optimum at Si/Al=2.5. From XRD patterns of
the products, it was thought that decrease of theo
CEC values from 120-150 C was due to thetransformation of zeolite Na-P1 into
Analcime-C. By hydrothermal treatment, raw
sludge (CEC value=19 meq/100g) could be
converted into zeolites with high potential use
as ion exchange materials for heavy metals
contaminants in aqueous solution.
Table 1. CEC and major phases observed by XRD at
various Si/Al ratios and different temperature
Si/Al

1.5

Figure 3 XRD patterns of products prepared withdifferent liquid to solid ratio (L/S)
2.5

3.2 Cation Exchange Capacity (CEC) of
zeolite products

3.0

Figure 4 shows the CEC values of zeolite
products obtained at various Si/Al ratios with

107

T(℃)

Major phase

CEC(meq/100g)

90

Na-P1

313

120

Unnamed Zeolite

328

150

Na-P1,
Unnamed Zeolite

158

90

Na-P1

350

120

Na-P1,Species-P

362

150
90

Analcime-C
Na-P1

223
304

120

Analcime-C ,Species-P

288

150

Analcime-C

190

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

4. Conclusions

3.3 Heavy metal adsorption
The adsorption capacities of Pb2+ and Cd2+
were measured at various conditions. Theozeolite product synthesized at 120 C,
Si/Al=2.5 (Z-1) showed Pb2+ adsorption of 55.3
mg/g and Cd2+ adsorption of 40.9 mg/g. Theozeolite product synthesized at 150 C,
Si/Al=1.5 (Z-3) showed Pb2+ adsorption of 37.3
mg/g and Cd2+ adsorption of 24.0 mg/g.

X[mg/g]From lake s ludge, s evera l t yp es ofzeolites were successfully synthesized byhydrothermal reaction in alkali solution.
Soluble elements in sludge such as Si and Al
tend to dissolve at higher temperature and form
zeolites. Zeolite Na-P1 and zeolite Species-P
which were synthesized at Si/Al ratio of 2.5o
and reaction temperature of 120 C showed thehighest value of CEC (362meq/100g). The L/Sratio showed no effect on the type of zeolite
formed. Obtained zeolites also have adsorptive
capability toward Pb2+ (Xm=55.3mg/g) andCd2+ (Xm=40.9mg/g) ions. These values werehigher than those reported on previous study.
(Activated carbon Xm(Pb2+)=35.7mg/g andartificial zeolite Xm(Cd2+)=38.55mg/g). The
zeolite synthesized from lake sludge in thisstudy could be applied for several applications
such as soil improvement or water purification.

C[ppm]

Figure 5 Langmuir adsorption isotherm of the Pb2+ ion

X[mg/g]References
1) X. Querol et al., “Synthesis of zeolites fromcoal fly ash: a overview”, Int. J. Miner.
Process, p.413-423, May 2002
2) M. Inada et al., “Synthesis of zeolite fromcoal fly ashes with different silica–alumina
composition”, Int. J. Miner. Process,p.299-304, 2005
3) Y. Shao et al., “Synthesis of zeolites fromlake sludge”, Water and Environment Tech.
Conference, 25-1B-06, June 2010

C[ppm]

Figure 6 Langmuir adsorption isotherm of the Cd2+ ion

Table 2. CEC and adsorption capacity of Pb2+ and Cd2+ forvarious synthesis conditions.

Zeoliteproduct
Z-1
Z-2
Z-3

Synthesis condition
Si/Al=2.5, 120℃,
L/S=5ml/g, 2M NaOH
Si/Al=1.5, 90℃,
L/S=5ml/g, 2M NaOH
Si/Al=1.5, 150℃,
L/S=5ml/g, 2M NaOH

CEC

Xm(mg/g)
2+

Xm(mg/g)for Cd2+

Major phase(meq/100g)

for Pb

362

55.3

40.9

313

41.1

35.2

NaP1

158

37.3

24.0

Analcime-C

108

NaP1,
Species-P

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Sulfate resistance of cementitious materials
with blast furnace slag, lime stone powder and anhydrite
Student Number: 09M18189

Name: Jeong-Gook Jang

Supervisor: Nobuaki Otsuki

高炉スラグ微粉末，石灰石微粉末および無水石膏を混和した
セメント系材料の耐硫酸塩性
張正菊
本研究は，1．セメント系材料おける硫酸マグネシウム劣化機構を明らかにすること，2．高炉スラグ微粉末，石灰
石微粉末および無水石膏のそれぞれの材料が耐硫酸塩性に与える影響を明らかにし，耐硫酸塩性を向上させるための材料設計を提案することを目的とした．
その結果，1．セメント系材料の硫酸マグネシウム劣化機構は，劣化初期に収縮し，その後，膨張することが明らかになった．初期の収縮は，浸漬初期に水酸化カルシウム量が減少して水酸化マグネシウム量が増加したことに起因し，その後の膨張は，二水石膏またはエトリンガイト量が大きく増加したことに起因したものと結論付けた．また，
2．高炉スラグ微粉末の混和率を高めることで水和初期の水酸化カルシウム量を減らすこと，無水石膏の混和率を高めることで水和初期に多量のエトリンガイトを生成させモノサルフェートの生成を抑制すること，石灰石微粉末の混和率を高めることで水和初期にモノカーボネートを生成させモノサルフェートの生成を抑制することが耐硫酸塩性の向上に有効であることが明らかになった．
1. Introduction

2. Experimental procedureSulfate attack is one of the major deterioration
problems for cement based materials, such as concrete,mortars and cement paste. This has been reported in
Middle East Asia, North America and worldwide [1], [2].
Our research group has been conducting experiments
improve the sulfate resistance by mixing admixtures
such as blast furnace slag (BFS), limestone powder(LSP), and anhydrite (AH) to ordinary Portland
cement (OPC) [3]. However, the previous researches
considered only Na2SO4 attack conforming to
ASTM-C1012. In the Middle East and so on where
there is a lot of MgSO4 in soil, it is expected that thedeterioration mechanism is different from that of
Na2SO4 attack. Also, MgSO4 deterioration mechanism
has not been clarified and material design to improve
MgSO4 resistance has not been conducted. Therefore,
this research focuses on the deterioration of
cementitious materials with OPC-BFS-AH-LSP due to
MgSO4. Also, the purpose of this study is to clarify
MgSO4 deterioration mechanism and to propose
material design to improve MgSO4 resistance using
BFS-LSP-AH.

2.1 Specimens
The materials used in this study were OPC, BFS, AH
and LSP. Table.1 shows chemical composition andphysical properties of the materials used. Mix
proportion of each materials are OPC:BFS=60:40,
45:55, 30:70% and AH=0, 3, 5, 7, 10% and LSP=0, 5,
10, 20%. Specimens were prepared with water-binder
ratio w/b=0.5 and dimension of paste specimens is
1×1×8cm. Specimens were cured in the mold for 24hours. And then, specimens were cured in water for 6
days. After water curing, the specimens were
submerged in 5 mass% MgSO4 solution at 23℃.
2.2 Method
Sulfate resistance was evaluated based on length andmass change tests conforming to the ASTM standards.
Also, all specimens were photographed with the use ofdigital camera. Compressive strength were measured
based on JIS R 5201.
In order to clarify the importance of understanding
MgSO4 deterioration and MgSO4 resistance of
cementitious materials with OPC-BFS-LSP-AH,

Table.1 Chemical composition and physical properties of materials used
Chemical composition (mass%)

Material

Ig.loss(mass%)

SiO2

Al2O3

Fe2O3

CaO

MgO

SO3

Specific surfacearea(cm2/g)

OPC
BFS
LSP
AH

1.07
0.49
43.20
0.85

20.37
33.60
0.33
1.00

5.57
14.26
0.10
0.30

3.38
0.41
0.05
0.10

63.17
43.13
55.80
40.50

2.04
6.05
0.19
0.10

2.48
56.80

3300
4550
7610
3990

109

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

hydration products of the cement pastes were
examined using X-ray diffraction test (XRD), thermal
analysis (TG/DTA), and scanning electron microscope(SEM).

3.2 Change of hydration products
Fig.3 shows the change of cement hydration products
after exposure to 5 mass% Na2SO4 solutions. Fig.4
shows the change of cement hydration products after
exposure to 5 mass% MgSO4 solutions. The amountof calcium hydroxide decreased at each BFS
replacement ratio after exposure to 5 mass% Na2SO4
and MgSO4 solutions. As for the decrease in amountof calcium hydroxide, the case of MgSO4 solution
exposure was larger than in the case of Na2SO4
solution exposure. In addition, in MgSO4 solution
exposure, the amount of calcium hydroxide decreased
greatly in early period of exposure, and the generationof the magnesium hydroxide was confirmed.
The amounts of ettringite and gypsum have increasedin each BFS replacement ratio after exposure to 5
mass% Na2SO4 and MgSO4 solutions. And in case of
MgSO4 solution exposure, the amount of increase in
ettringite and gypsum has grown as time passes. From
this result, the mechanism of MgSO4 deterioration
could be considered that;

3. MgSO4 deterioration mechanism
3.1 Change of length and external appearance
Fig.1 shows the experimental result of length change
test and Fig.2 shows the external appearance ofspecimens after exposure to 5 mass% Na2SO4 andMgSO4 solutions. In the case of (a) Na2SO4 solution
exposure, specimens expanded from early ages andsome specimens got destroyed. On the other hand, inthe case of (b) MgSO4 solution exposure, specimens
shrank in early ages and then it expanded. Moreover,in the case of (b) MgSO4 solution exposure,disintegration of specimen surface was observed.

2

2

BFS40%-AH0%-LSP0%
BFS40%-AH5%-LSP0%

Mg2+ + Ca(OH)2 → Mg(OH)2 + Ca2+

BFS40%-AH0%-LSP10%

1

SO42- + Ca(OH)2 +2H2O → CaSO4・2H2O + 2(OH)-

BFS55%-AH0%-LSP0%

C3A・CaSO4・12H2O monosulfate + 2( CaSO4・2H2O) gypsum+ 16H2O → C3A・3CaSO4・32H2O ettringite

BFS55%-AH5%-LSP0%0
BFS55%-AH0%-LSP10%BFS55%-AH5%-LSP10%

In other words, shrinkage occurs due to the reactionthat generates magnesium hydroxide as magnesium
ion reacts with calcium hydroxide. Expansion occurs
due to reaction that generates ettringite as sulfate ion
reacts with calcium hydroxide and monosulfate.

BFS70%-AH5%-LSP10%

-2
0

4

8

12 16 20 24

0

4

8

12 16 20 24

Exposure period (weeks)

Exposure period (weeks)(a) Na2SO4 solution(b) MgSO4 solution

7.0

Content of CH (mass%)-2

Fig.1 Length change after exposure to 5 mass%
Na2SO4 and MgSO4 solutions
BFS40%-AH0%-LSP0%
BFS40%-AH5%-LSP0%

300

7.0
6.0

1.0
0.0

0

6.0

CH
ettringite
gypsum

5.0
4.0
3.0
2.0

0
4
8
12
Exposure period (weeks)

BFS40%-AH0%-LSP10%

300

7.0
6.0

1.0
0.0

0

CH
ettringite
gypsum

5.0
4.0
3.0
2.0

CH
ettringite
gypsum

5.0
4.0
3.0
2.0
1.0

300

Peak intensity of ettringite
and gypsum (cps)

BFS70%-AH0%-LSP10%

Content of CH (mass%)

BFS70%-AH5%-LSP0%

Peak intensity of ettringite
and gypsum (cps)

BFS70%-AH0%-LSP0%-1-1Content of CH (mass%)

0

BFS40%-AH5%-LSP10%Peak intensity of ettringite
and gypsum (cps)Length change ratio (%)

Length change ratio (%)

1

0
4
8
12
Exposure period (weeks)

0
0
4
8
12
Exposure period (weeks)(b) BFS55%(c) BFS 70%(a) BFS40%

0.0

BFS40%-AH5%-LSP10%

Fig.3 Change of cement hydration products after
exposure to 5 mass% Na2SO4 solutions

BFS55%-AH0%-LSP0%

BFS70%-AH0%-LSP10%

4.0
3.0
2.0
1.0
0.0

BFS70%-AH5%-LSP10%(a) Na2SO4 solution

ettringite

5.0

gypsum004812Exposure period (weeks)(b) MgSO4 solution(a) BFS40%

Fig.2 External appearance of specimens after exposure
to 5 mass% Na2SO4 and MgSO4 solutions

7.0

CH

6.0

MH

300

1.0
0.0

0

ettringite

5.0
4.0
3.0
2.0

gypsum

7.0
6.0

5.0

CH
MH
ettringite
gypsum

4.0
3.0
2.0
1.0

0
4
8
12
Exposure period (weeks)

0.0

0
0
4
8
12
Exposure period (weeks)(b) BFS55%(c) BFS 70%

Fig.4 Change of cement hydration products after
exposure to 5 mass% MgSO4 solutions

110

300

Peak intensity of ettringite
and gypsum (cps)

BFS70%-AH5%-LSP0%

300

MH

Content of CH and MH (mass%)

BFS70%-AH0%-LSP0%

CH

6.0

Peak intensity of ettringite
and gypsum (cps)

BFS55%-AH5%-LSP10%

7.0

Content of CH and MH (mass%)

Content of CH and MH (mass%)

BFS55%-AH0%-LSP10%Peak intensity of ettringite
and gypsum (cps)

BFS55%-AH5%-LSP0%

Length change ratio (%)

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

2

2

2

1.5

1.5

1.5

1

1

1

0.5

0.5

0.5

0

0

0

-0.5

-0.5

-0.5

-1

-1

-1

-1.5

-1.5

-1.5

-2

-2

0

4

8 12 16 20 24(a) BFS40%-2
0 4 8 12 16 20 24
Exposure period (weeks)

0 4 8 12 16 20 24(b) BFS55%(b) BFS70%

AH0%-LSP0%
AH0%-LSP5%
AH0%-LSP10%
AH0%-LSP20%
AH3%-LSP0%
AH3%-LSP5%
AH3%-LSP10%
AH3%-LSP20%
AH5%-LSP0%
AH5%-LSP5%
AH5%-LSP10%
AH5%-LSP20%
AH7%-LSP0%
AH7%-LSP5%
AH7%-LSP10%
AH7%-LSP20%
AH10%-LSP0%
AH10%-LSP5%
AH10%-LSP10%
AH10%-LSP20%

Fig.5 Length change after exposure to 5 mass% MgSO4 solutions
Multiplication length change =|maximum amount ofshrinkage| + |maximum amount of expansion|

4. Material design to improve MgSO4 resistance
4.1 Sulfate resistance evaluated from length change
Fig.5 shows the experiment result of length change
test. In the case of AH0%-LSP0%, specimens were
deteriorated within 12 weeks regardless of BFS
replacement ratio. Except of these, shrinkage andexpansion is greatly controlled by BFS replacement
ratio. In case of (a) BFS40%, shrinkage ratio wasaround 0.3% and expansion ratio was 0.5 - 3%. In thecase of (b) BFS55%, shrinkage ratio was around 1%
and expansion ratio was 0.3 - 1%. In case of (c)BFS70%, shrinkage ratio was around 0.5% andexpansion ratio was around 0.5%. Also, the amount ofexpansion ratio decreased with increasing AH and
LSP replacement ratios.
From these results, increasing replacement ratio of
BFS, AH and LSP showed increased MgSO4
resistance. In this experiment, BFS40%-AH3~7%LSP5~10% and BFS70%-AH5~10%-LSP5~20%
showed superior MgSO4 resistance. Superior MgSO4
resistance is defined with 0±0.5% of length change
ratio.

As a result of Fig.7, it could be thought that therelationship between multiplication length change andcalcium hydroxide have almost parallel correlation.
And, it was thought that multiplication length change
has decreased by decreasing the content of calcium
hydroxide in case of BFS70%.

Content of CH (mass%)

7.0
BFS40%

6.0

BFS55%

5.0

BFS70%

4.0
3.0

2.0
1.0
0.0

0

0.2

0.4

0.6

0.8

OPC/p (mass/mass)

Fig.6 Relationship between OPC/p andcalcium hydroxide (before exposure)

4.2 Relationship between sulfate resistance andcalcium hydroxide
Multiplication length change (%)

Fig.6 shows the relationship between OPC/p andcalcium hydroxide (before exposure). OPC/p means
mixture ratio of OPC to all powder (OPC+BFS+AH+
LSP). The content of calcium hydroxide wassignificantly controlled by OPC/p. In other words,
replacement ratio of BFS increased, the content ofcalcium hydroxide decreased significantly. As a result,in the case of BFS70%, the content of calcium
hydroxide decreased to around 1% regardless of
replacement ratio of AH and LSP.
Fig.7 shows the relationship between multiplication
length change and calcium hydroxide (before
exposure). Multiplication length change means;

3.0
BFS40%
2.5
BFS55%
2.0
BFS70%
1.5
1.0
0.5
0.0
0.0

2.0
4.0
6.0
Content of CH (mass%)

8.0

Fig.7 Relationship between multiplication lengthchange and calcium hydroxide (before exposure)111

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

hydrated cement paste only expanded in case of
Na2SO4 deterioration.

4.3 Relationship between sulfate resistance andaluminate hydration products

2) In case of MgSO4 deterioration, shrinkage occurred
due to reaction that generates magnesium hydroxide
as magnesium ion reacts with calcium hydroxide.
Expansion occurred due to reaction that generates
ettringite as sulfate ion reacts with calcium hydroxideand monosulfate. Therefore, it could be consideredthat control of calcium hydroxide content is mostimportant to improve sulfate resistance, comparedwith Na2SO4 deterioration.

Ettringite

400

300

monosulfate

300

200
100
0

Peak intensity (cps)

400

100

【References】

0400

Ettringite

400

300

monosulfate

300

100
40% 55% 70%
Replacement ratio of BFS(c) AH5%-LSP0%[1] Rasheeduzzafar et al., J.ACI, Proc., Vol.81, No.1,
pp.13-20 (1984)[2] B.P.Bellport, In performance of concrete, ed.
EG.Swenson, University of Toronto Press, Toronto,
pp.77-92 (1968)[3] Yun sub Lee et al., Influence on replacement ofanhydrite gypsum and lime stone powder at blast
furnace slag cement on sulfate attack, Journal ofmaterial science, Vol.59, No.10, pp.737-742 (2010)

100(b) AH0%-LSP10%05) MgSO4 resistance improved with increasing the
LSP replacement ratio because monocarbonate content
could increase and monosulfate content could
decrease in early age.

100(a) AH0%-LSP0%1004) MgSO4 resistance improved with increasing the AH
replacement ratio because a large amount of ettringite
could generate and monosulfate content could
decrease in early age.

200

40% 55% 70%
Replacement ratio of BFS

2003) MgSO4 resistance improved with increasing the
BFS replacement ratio because calcium hydroxide
content in early age, which causes shrinkage andexpansion, could decrease.

Ettringite
monocarbonate
monosulfate

40% 55% 70%
Replacement ratio of BFS

Peak intensity (cps)Peak intensity (cps)Peak intensity (cps)Fig.8 shows the peak intensity of aluminate
hydration products before exposure. In the case of (a)AH0%-LSP0%, the amount of ettringite was few
regardless of BFS replacement ratio, and a large
amount of monosulfate was confirmed. In case of (b)AH0%-LSP10%, monocarbonate was mainly
generated regardless of BFS replacement ratio, andmonosulfate was few. In case of (c) AH5%-LSP0%,
ettringite was mainly generated regardless of BFS
replacement ratio, and monosulfate was confirmed. Incase of (d) AH5%-LSP10%, the amount of ettringite
and monocarbonate were the largest, monosulfate wasfew. Therefore it could be considered that MgSO4
resistance improved with increasing the AH
replacement ratio because a large amount of ettringite
could generate and monosulfate content could
decrease in early age. Also, MgSO4 resistance
improved with increasing the LSP replacement ratio
because monocarbonate content could increase andmonosulfate content could decrease in early age.

Ettringite
monocarbonate
monosulfate

200
100
0
100
40% 55% 70%
Replacement ratio of BFS(d) AH5%-LSP10%

Fig.8 Peak intensity of aluminate hydration products
before exposure
5. Conclusions
The purpose of this research is to clarify MgSO4
deterioration mechanism and to propose material
design to improve MgSO4 resistance using
BFS-LSP-AH. The experimental results could beconcluded as follow.
1) Hydrated cement paste shrank in early ages andthen it expanded in case of MgSO4 deterioration while112

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Evaluating Infrastructure Funds for Transportation
Infrastructure Projects
Student number: 09M18195 Name: Lei ZHANG

Supervisor: Shinya HANAOKA

交通社会資本整備事業を対象としたインフラファンドの評価
張磊
本研究では、世界各地域における交通社会資本整備事業を対象としたインフラファンドの投資現状について、投資方針と実際の投資状況の両者について分析し、特徴を把握した。また、リターン、リスク、
シャープレシオ、ジェンセンアルファなどの評価指標を用いて、インフラファンドのパフォーマンス評価を行った。その結果、インフラファンドが安定した資金ソースになる可能性は今のところ高いとは言えないという結果を得た。
i. Compare and analyze the current statement ofinvestment of the infrastructure funds based on theinvestment plan, and the ones based on practicalinvestment, by region, investment sectors and phases.
Further find out the characteristics of infrastructure
funds.
ii. Evaluate the listed infrastructure funds invested in thetransportation projects, through return, risk, Sharpe
ratio and Jensen’s alpha. Further conduct evaluationcomparison with infrastructure funds invested in othersectors, transportation-related stocks and REIT. Findout whether the infrastructure funds will become astable source of funding.

1. Introduction
At present, the need for additional transportation
infrastructure is increasing, as national economy andsocial development is developing rapidly in China, India
and other developing countries. On the other hand themaintenance of the transportation infrastructure should bedone as quickly as possible, since the infrastructure isgetting old in the developed countries. The
implementation and maintenance of transportation
projects requires large initial investment and a long
payback period. Therefore, it is almost difficult for thegovernment to afford this construction and operation costsolely due to the limitation of funding. To solve theproblem, most of countries in the world have actively
introduced private funds through PPP (Public–Private
Partnership), in order to implement and maintain thetransportation projects.
There is an alternative financing technique that exists
in the market known as “infrastructure funds”. The
institutional investors in Australia and some otherdeveloped countries have been actively investing into
infrastructure projects in Europe and United States
through infrastructure funds. Recently, new funds in India
have been set up. The movement to invest into
infrastructure projects in Asia has been seen. Therefore
infrastructure funds could be expected. (Infrastructure
fund is a fund doing investment to the infrastructure
projects.
In fact, the infrastructure funds only started recently.
So the existing studies about the infrastructure fund forthe transportation only contain the basic information of
the funds development. There are few researches that
focus on the analysis of worldwide infrastructure funds
development and the evaluation of the infrastructure
funds’ performance.

3. Analysis of current statement of theinvestment in infrastructure fund worldwide
3.1 Research methodology
1) Research target
i. The analysis of current statement of investment baseson the investment plan of fund manager. This researchis aiming to analyze and indentify the characteristicsof the infrastructure funds that used in thetransportation project, transportation(roads,railways, airport, seaport), energy(energy, renewableenergy, natural resources), utilities (utilities, water,water management), as well as telecoms.
ii. While analyzing the current statement of investment
based on practical investment, the infrastructure fundin transportation projects is analyzed as researchtarget to understand its characteristics.

2) Data
The data comes from [The 2010 Preqin Infrastructure
Review], which was issued by Preqin Ltd. And it
represents the most comprehensive edition, with details
for over 270infrastructure firms and 450funds.

2. Research purpose
The evaluation of the possibility of the infrastructure
funds for transportation projects as a stable source offunding will be conducted in this thesis. The discussion
will follow the two steps, shown as below:

3) Methodology
i. About analysis based on the investment plan, analysis
was conducted in the following four steps:A) Extract the data from a region-based database.

113

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TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

B) Break down the date that has been extracted in thestep A) of [i], by the investment sector and project
phase.
C) Organize the data that has been from step A) andB) of [i], by cross table.
D) Analyze and discuss the results.
ii. About analysis based on the practical investment,
analysis was conducted in the following steps:A) From the database of step A) of [i], extract the dataof infrastructure funds based on practicalinvestment, through checking fund’s homepage
and investment business site.
B) Break down the data from database extracted instep A) of [ii], by the investment sector and project
phase.
C) Organize the data received from step A) and stepB) of [ii], by cross table.
D) Analyze and discuss the results.

Investment phase Greenfield Brownfield
Investment sector
Transportation
Road
Transportation Railway
Seaport
AirportEnergyEnergyRenewable energy
Natural energy
Utilities
Utilities
Water
Water management
Telecom
Telecom

1
1
1
1
4
12
1
1
1

19
10
8
9
6
12
8
1
13
11
7
8

Investment sector
Road
Railway
Seaport
Airport

48
30
25
23
16
45
61
7
33
29
24
16

Investment sector
Road
Railway
Seaport
AirportInvestment sector
Road
Railway
Seaport
Airport

1
7
2
6

61

55
14

4
2
1
1

3

2

9
2
6
1

5

18

3
2
1
1

Greenfield andTotal (sector) Total
Brownfield
1

3

4
2
4
1

11

142

According to the analysis of the investment plan, andcomparison among other sectors, it has been found outthat infrastructure funds prefer investing into thetransportation projects through the number of
infrastructure funds (Table.1, Table.4).From the analysis
of practical investment, it shows that the infrastructure
funds mainly invested in road and railways, due toincreasing needs. Through investigating the data andwebsite, the fact that mix-type infrastructure funds
existing in large numbers has been identified. About theinvestment destination, the infrastructure funds are
expanding worldwide, but still, most of them only
focused on a few countries like UK and India (from thecomparison betweenTable.2 and Table.3, as well as
Table.5 and Table.6). About the investment phase, theinfrastructure funds are investing into the Greenfield in
the developing countries and into the Brownfield in thedeveloped countries. From the comparison investment
plan and practical investment, the number of practicallyinvested funds is much smaller than the one based on theinvestment plan. It has been found out that the
infrastructure funds did not carry out as planned.

113

86
16

Greenfield andTotal (sector) Total
Brownfield

Greenfield andTotal (sector) Total
Brownfield
1
7
2
6

89

Table.6 Current statement of investment by infrastructure funds
based on practical investment (India)

Investment sector
Road
1
5
2
8
Railway91
1032
Seaport8
8
Airport
6
6
Table.2 Current statement of investment by infrastructure funds
based on practical investment (Europe)

Investment phase Greenfield Brownfield

27
25
10
15
12
33
21
7
21
23
11
14

Greenfield andTotal (sector) Total
Brownfield

Investment phase Greenfield Brownfield

Table.1 Current statement of investment by infrastructure funds
based on investment plan of fund manager (Europe)
Investment phase Greenfield Brownfield

18
17
6
10
6
21
13
5
14
18
9
8

Table.5 Current statement of investment by infrastructure funds
based on practical investment (Asia)

Greenfield andTotal (sector) Total
Brownfield
28
19
16
13
10
29
41
6
19
18
16
7

1

Investment phase Greenfield Brownfield

Table.1, Table.2 (Europe), Table.4, Table.5 (Asia) are
the cross tables of infrastructure funds that based oninvestment plan and practical investment in Europe andAsia. Table.3, Table.6 shows the current statement of thepractical investment in UK and India. Besides, the data inthe other region, such as North America, Africa, Latin
America, has also been broken down as below.

Investment sector
Transportation
Road
Transportation Railway
Seaport
AirportEnergyEnergyRenewable energy
Natural energy
Utilities
Utilities
Water
Water management
Telecom
Telecom

2

6
5
3
4
5
8
4
2
5
5
2
5

Table.4 Current situation of investment by infrastructure funds
based on investment plan of fund manager (Asia)

3.2 Result

Investment phase Greenfield Brownfield

3
3
1
1
1
4
4

Greenfield andTotal (sector) Total
Brownfield

16

Table.3 Current statement of investment by infrastructure funds
based on practical investment (UK)114

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

indicates that a risk-free asset would perform better
than the financial product being analyzed.
iv. Jensen’s alpha:
The Jensen’s alpha is used to determine theabnormal return of a security or over the theoretical
expected return, which is predicted by a market model.
The Jensen’s alpha formula is:(4.3)
Rp – Rf = α + β ( Rm - R f ) + ε

4. Evaluation of the performance ofinfrastructure funds in transportation
projects
4.1 Research methodology
1) Research target
To evaluate the performance of listed infrastructure
funds, the research focused the ones used intransportation projects, that were listed in the stock
exchanges (Australia, London, New York, Singapore,
South Korea).Then compare with other products such as
infrastructure funds used in energy, transportation-related
stock and REIT, which are similar with infrastructure
funds.

Where: Rp : return, Rf : risk free interest rate, Rm : market return,
ε: independent normally distributed random variables

This formula is calculated by regression equation.
When alpha is significant, if the value is positive, thetarget product is earning excess returns rather thanmarket. On the other hand, if the value is negative,then the product is earning less return rather thanmarket. When alpha is insignificant, alpha could beconsidered as 0, which means that the product isearning the proper return regarding for the market.

2) Data
In Australian stock exchange, 4 infrastructure funds
used into transportation projects, 4 funds used into energy
projects, 2 transportation-related stocks, 4 REIT (Real
Estate Investment Trust) companies, and Australian stock
exchange index are abstracted. The monthly price of eachtarget is collected from Yahoo! Finance between Dec.
2005 and 2010, and Australian government bond 10 year
yield is used as risk free interest rate here (the theoreticalrate of return of an investment with zero risk). Because of
the global financial crisis in 2008, the observing period isseparated to 2005~2008 and 2008~2010.Furthermore, thesimilar ways are used to collect the data in the other stock
exchange like the one used in the Australian stock
exchange.

4.2 Result
As the examples, There are results about infrastructure
funds in the Australian stock exchange, with observing
period 2005~2008 and 2008~2010, shown in Table.7 andTable.8. The infrastructure funds in the other stock
exchanges were calculated as below.

FT1(Brownfield)
FT2(Greenfield)
FT3(Brownfield)
FT4(Greenfield)
FE1
FE2
FE3
FE4
FT-Average
FE-Average
Average(total)
ST1
ST2
ST - Average
REIT1
REIT2
REIT3
REIT4
REIT - Average
ASX3) Methodology
There are four measurement methods used to evaluate
infrastructure funds and other products.
i. Return:R = (P1-P0) / P0
(4.1)
Where: P1 : price in the current month, P0 : price in the last month

If return has positive value, the higher value is better.
If it has negative value, it means that it could not
generate profit.

ReturnRisk

1.118-1.403-0.294-0.926-9.234-0.997-1.717-0.445-0.376-3.098-1.737-0.181-10.21-5.198-4.784-7.833-1.503-0.356-3.619-0.370

6.426
7.966
7.541
8.319
22.468
7.731
8.704
4.633
7.563
10.884
9.224
7.934
11.3439.638
12.19223.135
6.719
8.723
12.692
4.790

Sharpe ratio-0.740-0.914-0.818-0.818-0.672-0.889-0.872-1.364-0.822-0.949-0.886-0.763-1.418-1.091-0.874-0.592-1.098-0.714-0.820-1.304

Jensen’salpha-6.0700-4.42100
00-4.880-2.622-1.220-1.92100000-3.4670-0.8660

Table.7 Australian stock exchange (2005~2008)

ii. Risk:
The risk “σ” is a standard deviation of the return.
Here, the smaller value is better.

FT1(Brownfield)
FT2(Greenfield)
FT3(Brownfield)
FT4(Greenfield)
FE1
FE2
FE3
FE4
FT-Average
FE-Average
Average(total)
ST1
ST2
ST-Average
REIT1
REIT2
REIT3
REIT4
REIT-AverageASXiii. Sharp ratio:
The Sharpe ratio is a measurement method of theexcess return (or risk premium) per unit of risk in an
investment asset or a trading strategy. The Sharpe
ratio formula is:
Sp = (Rp – Rf) / σ(4.2)

Where:Rp : return, Rf : risk free interest rate, σ : standard deviation of
returnThe Sharpe ratio is used to characterize how well
the return of an asset compensates the investor for therisk taken. The higher the Sharpe ratio number is thebetter it is. When comparing more than two assets
with the return Rp against the same benchmark withreturn Rf, the asset with the higher Sharpe ratio gives
more return for the same risk. A negative Sharpe ratio

RetureRisk

Sharpe ratio-0.4270.032
0.088-1.491
3.198
1.082-0.991-0.627-0.449
0.665
0.107
0.367
-5.383-2.5081.685
5.640-1.362
1.242
1.801
0.860

5.378
14.6337.579
9.994
31.86620.96512.1767.265
9.396
18.06813.7325.397
28.02016.70820.11727.44016.6739.877
18.5264.512-1.031-0.347-0.663-0.661-0.060-0.192-0.501-0.791-0.676-0.386-0.531-0.880-0.374-0.627-0.1700.018-0.388-0.392-0.233-0.943

Table.8 Australian stock exchange (2008~2010)

115

Jensen’salpha-4.576000
00-0.576-3.940-1.144-1.129-1.13600
0013.128003.282
0Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

FT: Infrastructure Fund (transportation), FE: Infrastructure Fund(energy), ST: Transportation-related stock, REIT: A-REIT
ASX: Australian stock exchange

ratio, the fund has a negative value, and the Jensen’salpha is 0 or during the observing period.
In the Singapore stock exchange, return of the funds
and others is going up, as Singapore stock exchange isrecovered through the comparison with two observing
period. The mix type infrastructure funds (transportation+ energy) have high risk, furthermore the value of Sharp
ratio is negative, and the Jensen’ alpha is 0 or during theobserving period.
In the South Korea stock exchange, return of the funds
and others is going up, as South Korea stock exchange isrecovered through the comparison with two observing
period. According to the results of Sharp ratio, the fundhas a negative value, and the Jensen’s alpha is 0 or
negative value during the observing period.
According to the analysis of the performance ofinfrastructure funds in transportation projects and mix
investment sectors in the each stock exchange, the risk of
infrastructure funds invested in the transportation projects
is almost the smallest among all of the financial products
being selected. On the other hand, mix type infrastructure
funds for transportation have a high risk. Funds invested
in the Greenfield have higher risk rather than the one
invested in the Brownfield. And funds invested in thedeveloping countries have higher risk rather than the one
invested in the developed and developing countries.
According to the result of sharp ratio, value of
infrastructure funds is negative in these five stock
exchanges. It means that the risk-free asset (government
bonds) would perform better than the infrastructure funds.
On the other hand, from the result of Jensen’s alpha, mostof the infrastructure funds’ value is negative. It meansthat the infrastructure funds analyzed in this thesis receive
less return than the stock market.

From 2005 to 2008, in the Australian stock exchange,
all of the targets have a negative value of return. It meansthat they could not generate returns. According to theresults about risk, it shows that the average of risk value
for funds invested in transportation projects is 7.563. It issmaller than the one of other products, which means thatthe funds invested in the transportation projects, has alower risk than others. The fund invested in theGreenfield projects has higher risk rather than the one
invested in the Brownfield projects, among the funds. On
the other hand, according to the results of Sharpe ratio, allof the targets have negative value and the value of thefund invested in the transportation is -0.822. It means thata risk-free asset would perform better than the funds andothers. Therefore it couldn’t attract the investors.
According to the results of the Jensen’s alpha, fundinvested in transportation is -2.622, transportation-related
stock is 0 (when it was considered as insignificant
evaluation by t-value), A-REIT is -0.866. It means that allof them are earning the less return regarding for themarket risk level. It couldn’t attract the investors.
From 2008 to 2010, return of the funds and others aregoing up, as Australian stock exchange is recovered. But
the fund invested in the transportation still had a negativevalue, -0.449 and it couldn’t generate returns during thisobserving period. From the results of the risk value, it
shows that funds are still having lower risk than theothers. The fund invested in the Greenfield projects also
has higher risk compared with the one invested in the
Brownfield projects. On the other hand, all of the targets
have a negative value on Sharpe ratio and the value of thefund invested in the transportation is -0.676. It means thata risk-free asset would perform better than the funds andothers. But the one of A-REITs called “REIT2” has apositive value on Sharpe ratio. According the results of
the Jensen’ alpha, fund has a negative value -1.144, but
A-REIT has a positive value 3.282. Therefore, the fund is
not an attractive asset for the investors. The A-REIT
could be considered as a high-risk high-return asset in theAustralian stock exchange.
The similar analysis of the other stock exchanges isconducted like the one in the Australian stock exchange,
being described above.
In the London stock exchange, return of the funds andothers are going up, as London stock market is recovered
through the comparison with two observing period. The
mix type infrastructure funds (transportation + energy)
have high risk. Among the funds, the fund invested inboth developed and developing countries has less riskrather than the one, which just invested in the developing
countries. According to the results of Sharp ratio, the fundhas a negative value, and the Jensen’ alpha is 0 or
negative value during the observing period. It is not an
attractive asset for the investors.
In the New York stock exchange, the mix typeinfrastructure funds (transportation + energy) haverelatively small risk. According to the results of Sharp

5. Conclusion
Since 2004, the infrastructure funds have beendeveloping rapidly. It could become a new source offunding. But in this thesis, according to the analysis ofcurrent investment by infrastructure funds, it has beenfound out that the infrastructure funds did not carry out asplanned. In the future, it is hard to tell whether
infrastructure funds would keep up their interest in thetransportation projects. However, during the observing
period, the evaluation of performance of the infrastructure
funds tells us that it is not attractive for the investors.
Thus, whether the investors would stay with theinfrastructure funds is still a question.

References[1] Jensen, M. C. [1968], “The Performance of Mutual
Funds in the Period 1945-1964,” Journal of Finance,
Vol23, pp.389-416.
[2] Sharpe, W.F.[1964], “Capital Asset Prices: A Theoryof Market Equilibrium under Condition of Risk,” Journalof Finance, Vol 19(9), pp.425-442.
[3] Preqin Ltd[2010], “The 2010 Preqin Infrastructure
Review,” Preqin Ltd.

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PREPARATION OF SOLID ACID CATALYSTS FROM BAGASSE FLY ASH
Student Number: 09M18203

Name: Jiaxing BIAN Supervisor: Hirofumi HINODE

バガスフライアッシュを用いた固体酸触媒の調製
卞佳興
本研究では、砂糖黍工業からの廃棄物であるバガスフライアッシュ(BFA)を原料とした固体酸触媒の開発を行った。調製した固体酸触媒の性能はイソアミルアルコールと酢酸のエステル化反応を用いて評価した。BFAはスルホン化処理によって強い固体酸触媒に変えた。また、キャラクタリゼーション方法としてN2吸着による比表面積測定、元素分析、フーリエ変換赤外分光分析(FTIR)およびpHzpc 測定を用いた。調製した固体酸触媒はエステ
ル化反応において、2 時間、70℃で酢酸イソアミル 60%以上の生成率に達して、市販の酸性触媒(Amberlite IR-120 plus)と同等な性能が確認できた。

could also be enhanced. In this study, BFA
was used to synthesize acid catalyst throughsulfonation method. The performances ofsynthesized acid catalysts were evaluatedusing esterification reaction of isoamyl alcoholand acetic acid in a batch reactor. A
commercial acidic resin, Amberlite IR-120
plus was also used as catalyst for comparison.

1. Introduction
Sulfuric acid is one of the most popularacid catalysts for practical chemical processes
and is widely used in the production ofindustrially important chemicals. However, thedevelopment of environmentally sustainablechemical processes has stimulated the use ofsolid acid catalysts, because solid acids can beeasily separated from the reaction matrix by
simple filtration or decantation for repeateduse.

2. Experimental
2.1 Material
BFA was obtained from PT Madukismo,a sugar company in Indonesia. H2SO4 (98%),
acetic acid (99.9%) and isoamyl alcohol(98%) were purchased from Wako chemical
while Amberlite IR-120 plus was from MP
Biomedicals.

Bagasse is the fibrous residue fromsugarcane juice extraction, that is valuable asenergy resource due to its high calorific value[1]. However, the ash from the combustion
process of bagasse in the form of fly ash andbottom ash have not yet been commercially
utilized and is causing disposal problems.

2.2. Preparation of solid acid catalysts
Solid acid catalysts were prepared bysulfonation of BFA using the following
procedures. 0.2g of activated carbon or BFA
was mixed with 10 ml concentrated H2SO4(98%) inside a Teflon-lined stainless steel
vessel. The mixture was heated in an oven at
150 ℃ for 24 hours. After treatment, themixture was cooled to room temperature. The
solid product was washed with deionized

The solid acid catalysts available
commercially are mainly prepared fromexpensive precursors such as pure glucose [2],cellulose [3] and so on. Therefore, by usingbagasse fly ash (BFA) as low-cost precursor insynthesizing solid acid catalyst, the disposal
problem of BFA could be solved and theeconomic potential of sugarcane industries

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water until constant pH. The solid product wasthen dried in an oven at 100 ℃ for 24 hours.
The nomenclature of solid acid catalysts
samples was showed in Table 1. The sample
ID, B-C-S means that BFA was activated by
CO2 and then sulfonated. B-S-S means that
BFA was activated by steam and thensulfonated. BFA-S means that BFA was
directly sulfonated without activation.

acetate from isoamyl alcohol and acetic acidwas shown as (2.4.1).
CH3COOH + C5H12O

C7H14O2 + H2O(2.4.1)

3. Results and discussion
3.1. Solid acid catalysts characterization
The porosity of specific surface area of
BFA and solid acid catalysts was showed inTable 2. After sulfonation, the porosity andspecific surface area of all the samples
increased.

Table 1 Nomenclature of solid acid catalysts
samples by sulfonation method
Sample ID Activated Activation Sulfonation
agent
(S)
B-C-S
yes
CO2
Yes
B-S-S
yes
steam
Yes
BFA-S
no
Yes

Table 2 Porosity and specific surface area of BFA
and solid acid catalysts
Porosity
Sample ID
Vmic
Vmes
SBET
(cc/g)(cc/g)(m2/g)

2.3. Solid acid catalysts characterization
The samples textural properties werecharacterized using nitrogen adsorption at 77
K by Autosorb 1 from Quantachrome. The
chemical property analyses were done bymeasuring zero point charge pHZPC and also by
analyzing the surface functional groups using
FTIR spectra analysis.

B-C-S
B-S-S
BFA-S

0.407
0.642
0.358
0.207

BFA

0.102
0.114
0.084
0.018

785
1,231
824
475

The compositions of C, H, N and S insamples were analyzed. The results of ultimate
analysis were showed in Table 3. BFA-S
sample had the highest S content.

The ash content of sample wasdetermined by thermogravimetry (Thermo
Plus TG8120, Rigaku). Elemental analyses ofC, H, N and S were performed on a LECOCHNS932 elemental analysis instrument.

Table 3 Ultimate analysis (C, H, N, S) of solid acidcatalysts
SampleID

2.4.Esterification method
Batch esterification reactions were carriedout in a simple batch system at 70℃ for 2
hours. There was no attempt of eliminatingwater or shifting the equilibrium to enhance
the reaction conversion. Each run used a
mixture of 0.3 mol acetic acid (99.9%, Wako)
and 0.3 mol isoamyl alcohol (98%, Wako)
with 1.0, 1.5, 2.0 or 2.5wt % of solid catalyst.
After reaction, the conversion of the ester was
analyzed by gas chromatography-mass
spectrometry (GC-MS). The esterificaton
reaction equation for synthesizing isoamyl

Ultimate analysis(wt %)

B-S-S

C
65.00

H
2.15

N
0.27

S
0.60

O*
22.28

B-C-S

73.38

0.21

0.50

0.73

11.00

BFA-S

68.07

0.78

0.30

3.98

26.81

O* content was calculate by difference

The functional groups on solid acidcatalysts were analyzed by FTIR. The result ofsynthesized solid acid catalysts B-C-S was
showed in Figure 1. After sulfonaton, thefunction group as indicated by peak intensity
at 1600 cm-1 is carboxylic (-COOH), the peaksof phenolic group (-OH) is from 900 to 1200
cm-1. Meanwhile, for sulfonate group (-SO3H)
the peaks are at 1,040 and 1,195 cm-1.

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70

-SO3H

-SO3H

-COOH

Conversion, %-COOH :1,600 cm-1
-OH : 1,200-900cm-1-SO3H : 1,195 and 1,040 cm-1

Absorbance

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

60

pHzpc =1.87
SBET=1,000

1.75
824

50
40

2.71
785

30

-OH

20

2.96
1,231

10
2000

1800

1600

1400

1200

1000

0

800

Amberlite

BFA‐S

B‐C‐S

B‐S‐S

Wavenumber [cm-1]

Fig.2 Conversion of isoamyl alcohol by several
catalysts

Fig. 1 FTIR spectra of B-C-S

3.2. Solid acid catalysts activity
The acid catalytic performance of BFA-S,
B-C-S and B-S-S samples was evaluatedthrough the esterification reaction of isoamyl
alcohol with acetic acid. The commercial
material (Amberlite) was used for acomparison. Figure 2 showed conversion ofesterification reaction by several catalysts. The
performance of BFA-S sample was higher
than that of Amberlite. This result showed thatthe catalytic activity was affected by thepHzpc.value.
The esterification reaction
mechanism was showed in Figure 3 [4]. The SO3H released H+ in the esterification reaction.
Therefore, the -SO3H group had sufficient
acidity to contribute significantly to reaction.
Catalyst with higher performance inesterification with higher -SO3H group had thelower pHzpc (stronger acid characteristic).

Fig.3 The esterification reaction mechanism
Multiple esterification reaction cycles
were carried out to examine the deactivation of
BFA-S catalyst. The result of catalystic
activity during five reaction cycles of theesterification was showed in Figure 4. The
result showed that no significant deactivation
was observed for BFA-S during fiveconsecutive esterificaton cycles of acetic acidwith isoamyl alcohol at 70 ℃ for 2 h. The
result of pHzpc measurement also showed
negligible change in the pHzpc value of BFA-S
after five cycles.

From the result, it can be suggested thatthe catalytic activity was independent ofspecific surface area.

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Conclusion
Conversion, %60

Solid acid catalysts have beensuccessfully prepared from BFA by simple
sulfonation method. The high performance ofsynthesized catalyst was evaluated using
esterification reaction of isoamyl alcohol andacetic acid at 70 ℃ for 2 hours in a batch
system with conversion of isoamyl alcohol
into isoamyl acetate reaching above 60% forcatalyst prepared from BFA. The performanceof synthesized catalysts was comparable to thecommercial acid catalyst (Amberlite IR-120plus).

50
40
30
20
10
0
1

2

3

4

5

Cycle

Fig.4 BFA-S activity during five reaction
cycles of the esterification
Effect of catalyst to BFA-150 mass ratioon conversion of esterification was showed in
Figure 5. From the results, The BFA-S mass
ration increased from 1.0 wt % to 2.5 wt %,however, the conversion was not significantly
changed. The conversion reached 63.5% with
H2SO4 catalyst. It could be possible that thereaction reached the equilibrium.

References[1]V.S. Batra, S. Urbonaite and G. Svensson,‘Characterization of unburned carbon inbagasse fly ash’, Fuel, vol. 87, (2008) 2972.
[2]A. Takagaki, M. Toda, M. Okamura, J.N.
Kondo, S. Hayashi, K. Domen and M. Hara,‘Esterification of higher faty acids by a novel
strong solid acid’, Catalysis Today, vol. 116,
(2006) 157-161.

H2SO4 catalyst[3]S. Suganuma, K. Nakajima, M. Kitano, D.
Yamaguchi, H, Kato, S. Hayashi and M. Hara,‘Synthesis and acid catalysis of cellulosederived carbon-based solid acid’, Solid State
Sciences, vol. 12, (2010) 1029-1034.

70

Conversion, %

60
50
40

[4]M. Kitano, K. Arai, A. Kodama, T.
Kousaka,K. Nakajima, S. Hayashi, M. Hara,‘Preparation of a sulfonated porous carbon
catalyst with high specific surface area’, Catal.
Lette. , vol. 131, (2009) 242-249.

30
20
10
0

1.0wt %

1.5wt%

2.0wt%

2.5wt%

Fig.5 Effect of catalyst to BFA-S mass ratio on
conversion of isoamyl alcohol120

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Estimation of CO2 Emission with Development of Rail-basedIntermodal Freight Transportation in Inland China
Student Number: 09M18210

Name: Xiaolin FANG

Supervisor: Shinya HANAOKA

中国内陸部におけるインターモーダル貨物輸送による CO2 排出量の推定
房小琳

本研究では、中国鉄道コンテナ輸送の現状、課題および方向性を明らかにした上で、今後の中国の経済発展と産業構造の変化に基づき、2020 年から 2050 年までの中国内陸部のコンテナ貨物輸送における鉄道インターモーダル輸送による CO2 排出量を推定した。
1. Introduction:
With the development of China economy, CO2 emission
volume is getting bigger. Now the CO2 emission by China
is 20.2% of that by whole world, as No. 2 following the
US. The CO2 emission by China transportation sector isabout 20% of that by all sectors in China. In recent years,
in China, the share of road transportation was getting
higher; however the share of rail transportation was
getting lower. From environment view, for per ton-km
freight, the CO2 emission by road is about 10 times of thatby rail. CO2 emission for per ton-km freight can be
reduced by shifting from road based transportation torail-based intermodal transport system.
The research focuses on the rail container freighttransportation of the inland China. There are 2 research
purposes. Firstly, clarify the current status, the current
issues and the future developing direction of the rail
container freight transportation in China. Secondly, each
10 year from 2020 to 2050, estimation of the containertransportation volume in China inland, then estimation of
CO2 emission volume, based on the modal shift from roadtransportation to rail transportation.

3.1 The current status
From 1999 to 2007, both the share of road container freight
and the share of water-container-freight increase apparently,
however the share of railway container freight decreases
year by year, from 26% in 1999 to 10% in 2007.Any ofChina road-container-freight, and the total container freightincreases well proportionally with GDP, however thegrowth rate of the rail container freight is much slower thanthat of the China GDP especially since 2003.
3.2 The issues of China railway container freight
The issues of China railway container freight are due to thefollowing 4 reasons:a. The shortage of capacity of railway-container
The transportation capability of railway is not enough dueto the not-enough investment in the past years. Secondly,
government orders that the railway should transport thenatural resources such as charcoal, steel, grain and so on
with highest priority, which is already above 65% of thetotal rail capability.
Furthermore, the busy container lines, through which morethan 35% container freight are transported, also work as
passenger transportation. These lines connect Chinese
biggest cities such as Beijing, Shanghai, Guangzhou and soon, between which the passenger transport are becoming
heavier and heavier in recent years. Especially during
Chinese New Year, in order to make sure that thepassengers can go home on time, the freight has to be
stopped or reduced frequency of transport.
b.
The shortage of facilities related torailway-container transportation
Firstly, the facilities in stations, which can receive/send
container freight, are out-dated. The following example isabout the status of all stations (609) in 2005: Most of them
have no specific workspace for container freight, justsharing workspace with common freight; some of them
have no specialized machines to load or unload thecontainer. Most of them have no advanced IT system tomanage the freight or the inter-station communication.
Secondly, the shortage of containers and the shortage ofcontainer carrying trains make containerization lower.
Based on Ministry of Railways, the container freight isabout 10% of rail freight, however due to the above reason,
only 2% of rail freight is transported by container.
c. The shortage of Railway-Sea Intermodal FreightTransport
After China joined in WTO in 2002，with the trade
increase，the freight volume of marine container increases
apparently. However, Railway-Sea Intermodal FreightTransport is only 2% of all marine containers，and more

2. The current status and issues of Chinese rail freightThe share of rail freight, measured by ton freight volume
and ton-km freight, decreases as compared to road freightin recent years, although rail freight is more suitable toChinese large land. The growth rate of the Chinese rail
freight is slightly slower than that of the China GDP,
while any of China road freight and the total freightincreases well proportionally with GDP. The reason is theshortage of rail freight’s capacity. With the GDP rapid
increase after China joined in WTO in 2002, the shortageof rail freight’s capacity is getting worse. In 2008 the rail
freight’s capacity can only meet 40% of the request
received by the China railway companies from the market.
So the market demand goes to road freight, althoughrailway freight is cheaper especially in the case of middle
or long distance freight. The reason of capacity shortage isthe shortage of railway construction. In China, railway
construction is mainly by invested by government. In thecase that the budget of government for railway
construction is not enough, the investment of railway
construction is not enough. During 2000~2008, thegrowth rate of operating-km of railway is only 2% eachyear, while the increase of highway is about 10%~30%.
Thus, the freight share of railway decreases in recent
years.
3. The current status and issues of China railway
container freight121Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

R: freight method, i.e. railway freight and road freight

than 80% marine containers depends on roadtransportation. The reason is that most of railway freightstations are far from seaport.
d. The shortage of door to door service
The shipper has to go to the rail freight station by oneself
in order to send or take the freight, because the railtransport companies only transport freight between rail
freight stations, which increases both the cost and thedelivery time.

The dependence of freight volume increment in the factors
such as GDP growth, change in production structure, andchange in demand is taken into consideration. By
considering the above factors, different approaches are used
to predict the container freight volume for 2020 and foryears beyond 2030, with 2030 inclusively. Besides that, thecontainer freight volume is emphasized in the calculation as
it is the main factor that decides the estimation of the CO2
emission.

4

The future development of rail containertransportation
The ‘medium-and-long-term plan for railway network’
central government established in 2007, which explicitly
draw the target of infrastructure construction until 2020and supplies 5 trillion budgets.
The large-scale railway construction will affect containertransportation in the following points:
1. The freight transportation will be separated from thepassenger transportation in busy container line. In recent
years, the passenger volume increases heavily. For linetransporting both freights and passengers, it is difficult toimprove the capacity of freight. With the construction ofhigh speed railway, the current railway will be usedmainly for freight, which will improve the capacity of railtransportation.
2. To construct the centre of container logistics. China
government plans to, before 2012, complete theconstruction of 18 the centers of container logistics which
can cover the whole national transportation network. The
main functionalities of these centers are:a. Can load/unload one whole train at one time: Eachcentre has this kind of advanced machines, which can both
improve the transportation efficiency and save energy.
b. Can handle the international containers: In the center,
there are customs and CIQ (China Entry-Exit Inspection
and Quarantine Bureau).
c. Advanced IT management system
3. The enlargement of Railway-Sea Intermodal FreightTransport. In April 2007, Ministry of Railways adjusted
the lines of container transportation. After the adjustment,
56 of 90 container lines connect the harbor. 6 of 18
container logistics centre are near the harbor. With thedevelopment of railway infrastructure, more and morelines will connect the harbor.
4. Introduce double-stack train: The double-stack train
was introduced into the Beijing-Shanghai line. Compared
to traditional line, the transportation capacity is enhanced,
the energy consumption per unit freight volume is saved
1/3, and the transportation fee also is saved 25%-40%.
After Beijing-Shanghai line, China government plans toconstruct a transportation network with 4 horizontal and 4
vertical lines. When the plan is finished at 2020, the Chinatransportation capacity will be much better than now.

5.1 Estimation of CO2 emission in 2020
5.1.1 Estimation of the container freight volume in 2020
The estimation of railway container freight volume for 2020is based on the current growth stage of China freightindustry. In this current stage, the increment in the freight
demand is largely contributed by the growth in economy.
Among all, the increment rate in GDP is the mostremarkable factor. The GDP growth rate in China towards
2020 is expected to continue the fast growing pace with thesimilar growth rate as in year 1999 to year 2008. Hence, byreferring to the relation between GDP and container freight
volume in the past decade, the railway container freight
volume and road container freight volume for 2020 can be
estimated.
(1) Estimation of the railway container freight volume
From year 2003 onward, the increment rate in the railway
container freight has been slowed down to a nearly flat
trend. Therefore, the railway container freight volume for2020 is calculated based on the estimated railway freight
volume instead of railway container freight volume. The
railway container freight volume is therefore can beobtained by replacing the percentage of container freight
volume over railway freight volume.
The linear relationship between railway freight volume and
GDP from year 1999 to year 2008
Y=136711.5+0.0487 X (5.1.1)
Y: railway freight volume (ten thousands ton)
X: GDP (million dollars)
Estimated GDP value: 8000000 million dollar (National
Bureau of Statistics of China, 2005)
Percentage of container freight volume over railway freight
volume: 9.7％ (Shenwan Research Institute，2009)
Hence, the railway freight volume for 2020 is 526204 ten
thousands ton.
the railway container freight volume for 2020 is 51042 ten
thousands ton (＝526204＊9.7％).
(2) Estimation of the road container freight volume
As for the estimation of road container freight volume for2020, it is known that the increment of road container
freight volume is linear proportional to the increment of the
GDP from year 1999 to year 2008.
Y=0.0185X-12606.6 (5.1.2)
Y: road container freight volume (ten thousands ton)
X: GDP (million dollars)
The estimated result of road container freight volume for2020 is 135563 ten thousands ton.

5 Estimation of CO2 emission from 2020 to 2050
The following formula 5.1 is used to calculate the CO 2
emission volume for intermodal container freight in China
Inland, from 2020 to 2050, for each decade.
(5.1)
E: CO2 emission volume (ton)
MR: container freight volume of freight method R (ton)
LR: average container freight distance of freight method R(km)
EFR: CO2 emission volume per ton-km freight for freight
method R (ton-CO2/ton-km-freight)

5.1.2 Estimation of the average container freight
distance(1) Estimation of the average railway container freight
distance122

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The OD (origin- destination) of railway container volume
for 2020 is estimated based on the OD data of railway
container freight for year 2005. The following formula5.2.1 is used to calculate the average railway container
freight distance.

Table 5.1 CO2 emission of container freight of Inland China
RailwayRoad
ton-km freight7939.94 1152.29（a hundred million ton-km）
CO2 emission per ton-km freight
174.65
1940.55（kg/10^4tn-km）
1386.71 2236.07
CO2 emission (ten thousands ton)
3622.78i, j: 29 province of China
1. Estimation of railway container freight between OD
Setp1: By using the average increment rate of GRP ineach region for 4 years, which is from year 2005 to year
2008, the GRP increment rate of each region for 2020 isestimated.
Step2: By assuming the increment rate of container
freight between OD is the same as the average GRP
increment rate of O and D, the railway container freight
volume between OD for 2020 is estimated.
Step3: The calculation result is corrected by using theestimated railway container volume for 2020.
2. Estimation of railway container freight distance
between OD. If there exists a plan for a certain route, thedistance is calculated using GPS while the other routes arecalculated based on the actual distance of the existing
railways. Hence, the average railway container freight
distance: 1555.6 km

5.2 Estimation of CO2 emission from 2030 to 2050
5.2.1 Estimation of railway container freight volumes
The railway container freight volumes of 2030 to 2050 are
estimated using the estimated data of domestic goods
categories of volume of trade in China from 2005 to 2050,
and the actual data of applicable freight volume forrailway container freight by items in year 2005.
Step1: from the estimated data of domestic volume oftrade up to 2050, to select 5 goods categories, which canbe applied to container transportation are as followed:
Metal and machinery; Fishing; Foods; Other
manufactures; Agriculture.
Step2: the actual data of applicable container freight byitems for 2005 is categorized into the matching selected
goods categories.
Step3: assuming the increment rate of freight volume isequal to the increment rate of volume of trade, theincrement rate of the freight volume for 2020 to 2050 ishence calculated.
Step4: the freight volume for 2020 to 2050 is obtained byreferring the actual data of applicable container freight by
categories for 2005.
The reasons for the recalculation of the applicablecontainer freight volume for 2020 are to confirm thereliability of the estimated result in part 5.1.1, and to makeassumption for the containerization ratio for 2030 andbeyond.
The comparison of the estimated result of railway
container freight volume for 2020
Estimated container freight volume from 5.1.1: 51042 (tenthousands ton)
Estimated result of applicable container freight volume inthis part: 59155 (ten thousands ton)
Assuming both estimated applicable container freight
volumes have to be in same value, the following
containerization ratio for 2020 is obtained.
Containerization ratio for 2020: 86.2% (=51042/59155)
The Ministry of Railway of China has aimed to increase
the containerization ratio to 50% in 2010 and targetingfull capacity coverage of transportation for applicablecontainer freight in 2020. This result is hence consistent
with the target set by the Ministry of Railway of China.
Therefore, the estimated result of railway container freight
volume above is considered reliable.
Finally the containerization ratio for 2030 and beyond isassumed.
As the containerization ratio for 2020 is 86.2%, thecontainerization ratio for 2030 and beyond are expected tobe more than 86.2%. Currently the containerization ratioof Japan is nearly 100% while other developed countries
are also having containerization ratio of more than 90%.
By referring to this numerical value, the containerization
ratio of China for 2050 is estimated to approach 100%. It
is assumed that there will be an increment of 4% for each
decade for 2030 and beyond. Therefore, the following(2) Estimation of the average road container freight
distance. Without the OD data of road container freight,
the road freight distance for 2020 is estimated using the
average freight distance from 1995 to 2007. Throughanalysis of the average freight distance of road freightfrom year 1995 to year 2007, the increment of averageroad freight distance is slow with 16 km in 13 years.
Based on this increment pace, the average freight distancefor 2020 is estimated as 85km.
5.1.3 Emission of CO2 emission per ton-km freight(1)CO2 emission per ton-km freight of railway freight
The CO2 emission is declined with the revolution ofengine and the increment of average load per railway
carriage. However, as there is no sufficient evidence tosupport these factors in 2020, it is hard to predict therelated situation in 2020. As for this, the CO2 emission per
ton-km freight is estimated according to the China
government’s target in CO2 emission reduction. Its target
in CO2 emission reduction in 2020, it is estimated that theCO2 emission is to be reduced by 35% as compared toCO2 emission in year 2005. Hence, the estimated result of
the CO2 emission per ton-km freight of railway freight for2020 is 174.65 kg/10^4tn-km.
(2) CO2 emission per ton-km freight of road freight
Same as the railway, the CO2 emission of road freight iscalculated according to the CO2 reduction target ofChinese government. China have a target in CO2 emission
reduction in 2020, it is estimated that the CO2 emission is
to be reduced by 10% as compared to CO2 emission inyear 2005(2156.17 kg/10^4tn-km).This result is 1940.55
kg/10^4tn-km in 2020
By composing the above calculated results, the CO2
emission of container freight of China Inland for 2020 isestimated.

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containerization ratio is assumed: 2030: 90.2%; 2040:
94.2%; 2050: 98.2%
The estimated result of railway container freight volume is98489 for 2030, 177792 for 2040, and 274322 for 2050
(Unit: ten thousands ton).

5.2.5 Estimation of CO2 emission per ton-km freight(1) CO2 emission per ton-km freight of railway freight
The calculation is based on the double stack train insteadof the common container transportation method. The
double stack container transportation method is only
suitable for the countries with wide area. Currently, thetransportation volume of double stack train is about 50%of the total container freight volume in Australia, while
there is 60% to 70% for North America. As the double
stack container transportation line has not been developedin China now. Therefore, by referring to the current
condition of the developed countries and future
development of rail-container transportation (part 4), it isassumed that China could have 50% container freight
volume as double stack train, and double stack train could
reduces 1/3 of its current CO2 emission per ton-km
freight.
The CO2 emission per ton-km freight of the rail freighttransportation: 145.54 (kg-CO2/10^4tn-km).

5.2.2 Estimation of road container freight volumes
Road container freight volume = domestic container
freight volume + marine container freight volume
Domestic container freight volume refers to the container
freight volume with both origin and destination locatedwithin the country while marine container freight volume
refers to the container freight volume covering import andexport.
(1) Estimation of domestic container freight volume
According to the statistic of the Ministry of Railway ofChina, about 80% of domestic container freight volume ishandled by railway in recent years. By assuming the same
trend in the future, the domestic container freight volume:2030: 123111; 2040: 222240; 2050: 342903 (Unit: ten
thousands ton).

(2)CO2 emission per ton-km freight of road freight
The future related information of CO2 emission per ton-km
freight cannot be obtained. From 2030 onwards, as China is
expected to have economy status approaching developed
countries, the CO2 emission per ton-km freight is hence
assumed to be similar to the developed countries.
By referring to the CO2 emission per unit freight volume(1350kg-CO2/10^4tn-km) of business-used truck in Japan
in 2008, the CO2 emission per ton-km freight in China up to
2050 is estimated to approach 1350kg-CO2/10^4tn-km. It is
assumed that there will be a decline of 10% for each decadefrom 2020 onwards. The result is as below: 2030: 1746;
2040: 1571; 2050: 1414(Unit: kg-CO2/10^4tn-km).

(2) Estimation of marine container freight volume
The marine container freight volume and the volume oftrade data of product import and export for 1999 to 2007
Y=4623.36+0.00585X(5.2.1)
X: volume of trade for product import and export (million
dollars)
Y: marine container freight volume (ten thousands ton)
By using the volume of trade data for product import andexport of China that is extracted from the estimated dataof worldwide trade matrix up to 2050, the marine
container freight volume for 2030 and beyond isestimated: 2030: 33,867; 2040: 49,703; 2050 64,705(Unit: ten thousands ton). Hence, the road container
freight volume is as below:
Table 5.2.1: Estimated results of road/railway container
freight volume (Unit: ten thousands ton)
2030
2040
2050
Road
Railway

156978105262271943
187733

The CO2 emission of container freight of China Inland for2030 to 2050 is estimated: 2030: 4713; 2040: 7882; 2050:
11403 (Unit: ten thousands ton)
6. Conclusion
As the wide area of China land is beneficial to thedevelopment of rail container freight, the share of rail
freight is expected to be increased dramatically following
the construction of railway infrastructure and otherpromoting polities. Therefore, by implementing therail-based intermodal, the average CO2 emission per ton-km
freight in total will be reduced.

4076082872635.2.3 Correction of railway container freight volume
for 2030 to 2050
The estimation of railway container freight volume inpart5.2.1, has not considered about the increment ofrailway-sea intermodal freight volume. It is assumed thatthe railway-sea intermodal freight volume could reach
20% of marine container freight volume following thefuture railway infrastructure development and also by
referring to the current condition of the developed
countries. The estimated result of railway container freightis showed in table5.2.1.

Reference:
Regmi, M. B. and Hanaoka, S. (2010). A framework to evaluate carbon
emissions from freight transport and policies to reduce CO2 emissions
through mode shift in Asia, The 3rd International Conference on
Transportation and Logistics (T-LOG 2010), Fukuoka.
2005, 2006, 2009 Year Book Of China Transportation And
Communications, Year Book House Of China Transportation And
Communications.
The Ministry of Railway of China, 2000-2008 Statistics Bulletin Of China
Railway.
The Ministry of Transport of China, 2000-2008 Statistics Bulletin Of Road
Transportation And Water Transportation.
National Bureau of Statistics Of China, 2006-2009 China Statistical
Yearbook.
National Bureau of Statistics Of China, 2008 China Energy Statistical
Yearbook.
Shenwan Research Institute, (2009). Investment strategy of railway
container transportation

5.2.4 Estimation of average transport distance ofrailway and road container freight for 2030 to 2050
The average transport distance of railway and roadcontainer freight for 2030 to 2050 is obtained from theaverage transport distance of 2020. After 2020, the speedof economy growth of China will be slowed down, andbasically complete its major traffic infrastructure building
by 2020, so it is estimated that there would not have majorchange in the transportation distance between OD for2030 and beyond.

124Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Pattern Recognition by Kernel Wiener Filter
Student Number: 09M18226

Name: Rui LI

Supervisor: Yukihiko YAMASHITA

ウィナーフィルタを用いたパターン認識李睿
逆問題のための代表的な手法であるウィナーフィルタ (WF) は，その評価基準が原信号と復元信号の差を原信号空間
で直接比較するものであり，構成も容易であるという利点から，極めて広い範囲で応用されている。カーネルウィー
ナフィルタ (KWF) は，WF にカーネル法を適用したものであり，非線形処理が可能になる。本論文では，KWF を用いたパターン認識に着目する。まず，2 クラス認識における，原信号特徴空間のカーネル関数値と認識率との関係を調べる。そして，交差検定 (CV) の考え方を適用し，復元作用の素ための標本点と評価のための標本点を分ける，交差検定 KWF(CVKWF) を提案する。さらに，計算機実験によって，CVKWF の評価を行う。

1 Introduction
An inverse problem is a task to estimate an original signal
from its observed data. The inverse problem often occurs inmany fields of science and engineering. The restoration ofdeteriorated images is an application example of the inverse
problem.
Let Hs be an original signal space and Ho be an observed
signal space. An original signal and its observed signal aredenoted by f and g, respectively. The linear estimation
model is given as the following equation:fˆ = Bg,(1)

where B is a bounded linear operator from Ho to Hs , andfˆ is the estimated siganl. This model is illustrated in Fig.1.
The linear inverse problem is to find the best estimated sig-

product in feature space just by their low dimensional original signals. It is called kernel trick.
In this paper, I explain KWF and its application to pattern recognition. To acquire higher recognition results, I
analysis the effect on recognition result brought from modfying original category vectors. I propose an effective way
to modify the original category vectors and conducted experiments to prove it.
I propose subspace KWF (SKWF) of which filter model
is constructed by a different set of samples from that for validation. By applying the cross validation method to SKWF,
I propose the cross validated KWF (CVKWF). I show its
advatage by experimental results.

2

Kernel Wiener Filter

The signals in a space are mapped by a nonlinear function
Φ, which is derived from a Mercer kernel, into a reproducing kernel Hilbert Space (RKHS). This space is called thefeature space. Generally speaking the feature space has ahigher dimension than the original space. In this paper I
use the following two kernel functions .
Fig. 1: Inverse problem
nal fˆ from the observed signal g.
The Wiene filter (WF) is an representive solution for the
inverse problem. It providers the best estimated signal withrespect to the squared error averaged over the original andthe observed signals among linear operators. WF directlycompares f and fˆ in Hs . Moreover, the criterion and realization of WF are so simple that it can be widely applied tomany fileds.
According to the Mercer kernel theory, I can obtain an
mapping operator by which low dimensional input siganals
can mapped into a high diemnsional space (feature space).
Nonlinear processing in the original space is realized by linear processing in the feature space. And the Mercer kernel theory provides us a way to calculate the value of inner125

ks (f x , f y ) =< Φs (f x ), Φs (f y ) >,(2)

ko (g x , g y ) =< Φo (g x ), Φo (g y ) > .

(3)

The model of KWF is shown in Fig2. X is a restoration
operator between Fs and Fo . Its estimated signal is given
by XΦo (g). fˆ is provided by using XΦo (g).
KWFX minimizes the sum of squared errors between
Φo (f i ) and its restored signal XΦo (g i ). Then X minimizes the following criterion JKW F .
1X
kΦs (f i ) − XΦo (g i )k2 .
L i=1
L

JKW F =

(4)

I assume X is given by
X=

L X
L
X
i=1 j=1

hij Φs (f i )Φo (g j )T .

(5)Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

3

Modifying Kernel Function ValueTo explain briefly,I assume that Φo (g) is a linear combination of Φo (g i ) (i = 1, . . . , L) :Φo (g) =LX

αi Φo (g i ).

(11)

i=1

I define vector ĥĥ = 2[α1 , . . . , αL ].

(12)

and assume that K is regular. Then the discriminant function dt (g) can be described asKs (f †t , f 1 )..
dt (g) = Ks (f †t , f †t ) − ĥ .
.

Fig. 2: Inverse problem between Fs and Fo
For calculation, I define the following (L × L) matrixes,
H,Kg ,Ks .

h11 h12 . . . h1L h21 h22 . . . h2L 
H=
(6)
..
.. 
..
 ..
,
.
.
. 
 .
hL1

...

...

(13)

Ks (f †t , f L )It’s clear I don’t have to calculate the kernel function in
Hs but just need its value. Let an angle between Φs (f †i )and Φs (f †j ) in Fs be θij , the inner product is obtained as
the following eq.(14) by deciding on the value of kΦs (f †t )kand θij .

hLLks (f †i , f †j ) = Φs (f †i )T Φs (f †j )= kΦs (f †i )k · kΦs (f †j )k cos θij . (14)ko (g 1 , g L )
ko (g 2 , g L ) 
To simply the problem, I just analysis 2-class recognition.
 , (7)..
+1 and −1 represent categories. And I suppose the fisrt.

L1 training data are included in category +1, the others areko (g L , g L )included in category −1. Since Ko is fixed, the discriminantfunction is described byks (f 1 , f 1 ) ks (f 1 , f 2 ) . . . ks (f 1 , f L )Ks (f †+1 , f †t )
 ks (f 2 , f 1 ) ks (f 2 , f 2 ) . . . ks (f 2 , f L ) 
Ks = 
..
..
..
..
..
 . (8)


.
.
.
.
.




K (f † , f † )ks (f L , f 1 )...
. . . ks (f L , f L ) s +1 t † 2
dt (g) = kf t k − ĥ (15)Ks (f †−1 , f †t )

and the following solution.
..




.
†
T
†
†
H = Ko (Ko Ko ) .
(9)
Ks (f −1 , f t )ko (g 1 , g 1 ) ko (g 1 , g 2 ) . . .
 ko (g 2 , g 1 ) ko (g 2 , g 2 ) . . .
Kg = 
..
..
..

.
.
.

ko (g L , g 1 )...
...

Training data are given as pairs (f i , g i ) (i = 1, . . . , L),
where f i represents the category of g i . Let f †t indicate thecategory t, g be the unkonwn pattern. To simply the calculation, I substract kXΦo (g)k which does not depend on
the category from the squared error, then, the discriminantfunction dt (g) is given bydt (g)≡=kΦs (f †t ) − XΦo (g)k2 − kXΦo (g)k
Ks (f †t , f †t )
− 2[ko (g 1 , g), . . . , ko (g L , g)]H T 

3.1

Modifying angles with the same norms

Suppose kf †+1 k = kf †−1 k, then the differece between 2
discriminant values can be represented as following.
d+1 (g) − d−1 (g) = −ĥkf †+1 k2 (1 − cos θ)µ,

(16)

where


Ks (f †t , f 1 )
..
.


.

Ks (f †t , f L )
(10)

126

µ = [1, . . . , 1, −1, . . . , −1]T(17)

When θ doesn’t equal to 0, the value of (1 − cos θ) isalways positive. In such case, modifying θ can’t change
the recogniton result. Only when θ the sign of the differecevalue chages, the recogniton result will change.

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

3.2 Modifying norms with cos θ = 0
When cos θ = 0, the difference between 2 discriminantvalues can be discribed as following.
d(g) =

denote the later data as (f 2n , g 2n ) n = 1, . . . , L. Then, thecriterion of SKWF XS is given as

d+1 (g) − d−1 (g)= kf †+1 k2 − kf †−1 k2 − kf †+1 k2

L1
X

where

αi

XS =

i=1

+ kf †−1 k2

L
X

1 X
kΦs (f 1m ) − XS Φo (g 1m )k2 ,M m=1
M

JSKW F =L2
c X
Xhij Φs (f †i )Φo (g 2j )T .

(23)

(24)

i=1 j=1

αj .

(18)

j=L1 +1(19)
I denote the value of d(g) by do (g) when kf †+1 k = kf †−1 k =1. I can haveLX

d0 (g) = d+1 (g) − d−1 (g) =

αj −

L1
X

αi , (20)i=1

j=L1 +1

When kf †+1 k is changed, I have,L1
Xd(g) = d0 (g) + (kf †+1 k2 − 1) 1 −
αi .

(21)

4.2

CVKWF

I propose to design SKWF by applying the cross validation method. I call the filter the cross validated KWF(CVKWF). Learning data is divided into A folds. Of the A
folds, B fold is used for the filter model, and the remaining
A − B folds are used for the validation of the filter model.
The cross-validation process is then repeated A times. The
A results for the filter model are averaged (or otherwise
combined) to produce a single estimation. The usage ofdata for CVKWF(A = N, B = 1) is shown as Fig.3.

i=1

PL1
If i=1αi < 1 and d0 (g) < 0, the sign of d(g) can chagefrom minus to plus by increasing kf †+1 k. In this case, modifying norm is an effective way for increasing recognition
PL1
rate of the −1 category. If i=1
αi > 1 and d0 (g) < 0,increasing kf †+1 k will not chage the sign of d(g).

3.3 Modifying cos θ with fixed norms

Fig. 3: Usage of data for CVKWF+
When kf ++1 k and kf −1 k are fixed, I haved(g)= d+1 (g) − d−1 (g)= kf †+1 k2 − kf †−1 k2+(kf †+1 kkf †−1 k cos θ − kf †+1 k2 )

L1
X

5
αi

i=1

+

(kf †−1 k2 − kf †+1 kkf †−1 k cos θ)

L
X

αj .

j=L1 +1(22)+
When modifying cos θ, the differece of kf ++1 k and kf −1 kcan be control d(g).

4 Cross-validation KWF(CVKWF)
4.1 Subspace KWF(SKWF)
I propose the subspace KWF (SKWF) where data to construct the filter model are different from data for validation.
I denote the former data as (f 1n , g 1n ) n = 1, . . . , M , and127

Experiment result

In the experiment, I used the CTG data set and Image
Segementation data set from UCI Machine Learning Repository. CTG and Image Segementation are both multiclassrecogniton data. And I extract class1 and class2 data from
CTG and Foliage and Window data from Image Segmentation to conduct 2-class recognition experiment.

5.1

Modifying the original signal

Table 1. shows the experiment results of modifying norms
with cos θ = 0. From the result, with increasing the norm of
one category, the miss-recognition from the other category
to the category becomes larger, and the miss-recognition
from the category to the other category becomes fewer.
Table 2. shows the experiment result of modifying cos θwith kf †−1 k = 1, kf †+1 k = 1.1. From the result, modifying

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

Table 1: KWF FOLIAGE(class +1) WINDOW(class -1)
cos θ+1−1 =0 modifing norms
Norm

Norm

misrecog-size

misrecog-size

†
kf+1
k

†
kf−1
k

miss

+1 → −1missmis−1 → +1

rate

1
1
1
1
1.1
1.5
100

1
1.1
1.5
100
1
1
1

48
41
36
33
72
77
93

35
40
49
59
32
30
26

13.83
13.50
14.17
15.33
17.33
17.83
19.83

Table 3: KWF FOLIAGE(class +1) WINDOW(class -1)
modifing cross result††cos θ+1−1
kf−1
k(kf+1
k = 1)
1
1.1
1.5
2
0.4
0
-0.1
-0.5

13.83
13.83
13.83
13.83

cos θ+1−1

miss

miss

+1 → −1−1 → +1

mis rate

33
38
41
41
41
42

56
48
41
40
36
38

14.83
14.33
13.67
13.50
13.17
13.33

0.9
0.7
0.4
0
-0.1
-0.5

the value of cos θ can control the difference between the
miss-recognitions of two categories.
Table 3. shows experiment results of modifying both
norms and cos θ. From the experiment result, after modifying kf †+1 k and kf †−1 k, modifying θ+1−1 is an effective
way to improve the recognition rate.

14.33
14.17
14.33
14.67

14.67
14.50
14.67
14.33

Table 4: CVKWF,CTG(c1,c2),best result
KWF CVKWF(4-3) SVM
mis rate

Table 2: KWF FOLIAGE(class +1) WINDOW(class 1)modifing θ††
kf−1
k = 1.1
k = 1, kf+1

13.67
13.50
13.17
13.33

17.56

16.83

18.78

Table 5: CVKWF,CTG(c1,c2),experiment result detailmiss rate
CVKWF(2-1)
CVKWF(3-1)
CVKWF(3-2)
CVKWF(4-1)
CVKWF(4-3)
CVKWF(5-1)
CVKWF(5-4)

17.80
17.32
17.07
18.29
16.83
18.05
18.05

KWF with subspace constraint SKWF. By combining crossvalidation method to SKWF, I proposed CVKWF. I showed
the advantages of proposed methods by experiments of pattern recognition.

References[1] Y. Washizawa and Y. Yamashita, Kernel Wiener filter, JIEICE 6 workshop in computer information-theoretic learningtheory, Nov. 2003, pp.143-148.

5.2 CVKWFIn the Table 4. and Table 5., CVKWF (A-B) represents
that I divide training data into A folds and use B folds toconstruct the filter model. Table 4. shows the least mis
recogniton rate of KWF, CVKWF, and SVM. CVKWF(43) is the best recognition rate. Table 5. shows the detailof every CVKWF experiment. I found that I can get betterrecongnition rate by using A-1 folds to construct the filter
model than by using only 1 fold to construct the filter model.

[2] Y. Washizawa and Y. Yamashita, Kernel Wiener filter, JIEICE magazine, vol.103, no.733, 2003, pp.73-78.

6 Conclusion[5] Hirokazu Yoshino, Chen Dong, Yoshikazu Washizawa, and[3] Y. Washizawa and Y. Yamashita, Non-linear Wiener filter in
reproducing kernel Hilbert space, 18th International Conference on Pattern Recognition(ICPR’06), 2006, pp.967970.
[4] Young-Chul SONG and Doo-Hyun CHOI, Kernel Adjusted
Wiener Filter for Image Enhancement, Japanese Journalof Applied Physics., Part 2 Letters, vol.44, issue.6A, 2005,
pp3996-3999.

Yukihiko Yamashita Kernel Wiener Filter and its Applica-

In this paper, I investigated the pattern recognition by
KWF. I clarified the effect of changing values of a kernel
function in the original vectors on recognition. I proposed128

tion to Pattern Recognition, IEEE Trans. on Neural Networks, vol. 21, no. 11, pp.1719–1730, Nov., 2010.

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

ASSESSMENT OF OPERATIONAL EFFICIENCY AND SPACE ALLOCATION OF
LOW-COST TERMINAL IN AIRPORTS
Student Number: 09M51608

Name: Batari SARASWATISupervisor: Shinya HANAOKA

This thesis aims to propose models and solutions toward LCT’s operational efficiency and space allocation issues
so that LCT could become a sustainable concept that can be adopted by many airports. An optimization model isconducted to find the best terminal site and configuration for LCT that minimize the distance travelled by passengers
and aircrafts according to the number of aircraft gates desired. Furthermore, an optimization model is also developed to
calculate concession space in departure airside and landside in order to maximize potential non-aeronautical revenue.

Introduction

The low-cost carrier (LCC) industry is growing rapidly forthe past decade resulting in a new trend in aviationindustry. Airport is one of the elements that influenced
heavily by the growth of LCCs [1]. Some have developed
the concept of dedicated LCT to accommodate therequirements of LCCs, the concept of which emphasizes
on cost and time reduction.
LCT has its own capability to process flights andpassengers using a simplified terminal building design. It
uses straightforward design without any aesthetic orprestigious value, for instance, LCT do not provide airbridges and eliminates luxury lounges.
LCT could be redeveloped from existing facilities(old passenger terminal or old cargo terminal) or could bebuilt as a brand- new dedicated terminal. Airports with
LCT may benefit from additional traffic from LCC
services. Since airport faces large fixed infrastructure
costs, each airport with excess capacity can benefit from
LCCs services following the fact that airports can do so at
low marginal cost per passenger [2].
Despite all the advantages that can be offered by LCT
development in airport, the sustainability of this concept
is still in question [3]. The fundamental question that theauthor intended to address is how to ensure thesustainability of LCT despite all the difficulties that it
potentially face. There are two potential issues that havebeen identified: operational efficiency and profitability.
Operational efficiency discussed in this thesis is a
response to LCT characteristic as an additional terminal
in airport that subject to space limitation problem. It
raises a necessity to effectively determine LCT locationand configuration. Profitability issue discussed in thisthesis is a response to an increasing pressure from LCCs
to lower the level of aeronautical fees. Airport authority
has to seek new ways of generating revenues mostly bygiving greater attention to commercial (non-aeronautical)
revenue.
There are two main objectives of this thesis. The firstobjective is to determine the effective location andconfiguration of LCT inside an airport by taking into
account passenger walking distance and aircraft taxiing
distance. The second objective is to determine space
allocation of commercial area inside the LCT building toensure non-aeronautical revenue maximization. Further
elaboration about each objective and its optimization
model are provided accordingly.

2.

Operational Efficiency of LCT

2.1

Model Formulation

To determine the location of configuration of LCT inside
an airport, there are parameters that need to be considered.
The configuration of LCT is determined by passenger
walking distance parameter. Meanwhile, the location of
LCT toward other facilities inside an airport is very muchdetermined by aircraft taxiing distance parameter. Eachof this parameter has its own importance to affectaircraft’s turnaround time thus influence operational
efficiency at the terminal.
There are 4 terminal configurations discussed: (1)linear, (2) single pier, (3) T-shaped pier, and (4) Y-shaped
pier. The average walking distance is calculated as total
walking distance required to travel from end point of
waiting area to each gates (from the most distant and theclosest gate) divided by the number of gates. Notify that in
LCC business process, transfer passengers cannot directly
travel from one gate to another since most LCCs serve
point-to-point flights.
w

d

Gate

Linear2yw2

End Point of Waiting Area

d

y

y

w2

y

0.5d

0.5d

d

d

1.

w

w1

Single Pier

T-Shaped Pierw1

Y-Shaped Pier

Figure 2-1. LCT configuration

Mathematical model is developed to solve the problemof terminal site and terminal configuration for LCT more
systematically. The mathematical model has two
objectives. Objective (2.1) minimizes average passenger
walking distance (DW) from waiting point to aircraft
gates. Objective (2.2) minimizes average aircraft taxiing
distance (DT) required from runways to apron area. The
role of zij is to assure the choice of one site and one
configuration for the new terminal. The constraints (2.3)
and (2.4) guarantee that the longest length g(xij) andwidth h(xij) of the gross area needed to build terminalbased on the number of aircraft gates desired in new129

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

terminal site i with configuration j do not exceed thelength and width of the new terminal site, Li and Wi.
Min 𝐷𝑊 =Min 𝐷𝑇 =

1
𝑝

𝑖

𝑖 𝑧𝑖𝑗

𝑗 𝑧𝑖𝑗 𝑓(𝑥𝑖𝑗 )Six zij excluded from the solution set (z11, z12, z14, z21,
z31, and z41) because they do not satisfy constraint (2.3)
and (2.4) in the model formulation. As a result, we haveten possible solutions where two of them are non-inferior
solutions and achieve Pareto optimality (z13 and z22). After
applying the single objective function with the determinedweights, the optimal solution is obtained. A new terminal
should be located in site 2 with a single pier configuration(z22), with objective function equal with 14,763.01.
Sensitivity analysis is performed to see the effect ofdifferent passenger and aircraft value of time to the model
result. We change the value of w2 which represents theratio of aircraft to passenger time value per unit distance($/meter). When ratio of aircraft to passenger time value
per unit distance achieves 3.974, the solution will shift
from z22 to z13. In addition to that, the solution will shift
from z22 to z13 if the walking distance in z13 is reduced
20.6% into 165 meter.

(2.1)

𝑘 𝑑𝑖𝑘 + 𝑑𝑘𝑖(2.2)

Subject to:𝑔(𝑥𝑖𝑗 ) ≤ 𝐿𝑖

∀ 𝑖, 𝑗 (2.3)

𝑕(𝑥𝑖𝑗 ) ≤ 𝑊𝑖

∀ 𝑖, 𝑗 (2.4)

𝑖𝑗 𝑧𝑖𝑗 = 1

𝑧𝑖 , 𝑧𝑗 𝜖 0,1(2.5)∀ 𝑖, 𝑗

The weighted sum of the objective method can beapplied to solve bi-objective optimization of terminal
location and configuration problem. It entails selecting
scalar weights (wi) and minimizing the composite
objective function. The weight is determined by paired
comparison method. In order to obtain the weights (w1 andw2), passenger value of time per unit distance will becompared to aircraft value of time per unit distance.
Passenger time value is $0.00539/m, while the aircraft
time value is $0.01207/m [4], thus w1 = 1 and w2 = 2.24.

Z13
Optimum Solution

2.2

3.974

Hypothetical Example

To demonstrate how the model works and to verify its
usefulness, the hypothetical problem regarding locationand configuration of LCT in airport is presented.

Z22

0

1

2

3

4

5

6

w2
Table 2-1. Input data for hypothetical example
Site (i)Areaa (m2)Site 1Site 2Site 3Site 417,50024,00023,00025,000

155 x
112

210 x
114

200 x
115

200 x
125

Taxiing
distance(m)

Runway1(3000,
4050)(4000,
3700)(4500,
4500)(3000,
4300)(dik, dki)

Runway2(2400,
3600)(2000,
3400)(3500,
4500)(3800,
2700)

Figure 2-3. Sensitivity analysis for terminal location and configuration

9000

Avearge Taxiing Distance (m)Z32

Z34 Z33

8000
7500
7000
6500

Z42

Z43 Z44

Z22

Z23
Z24
Z13

6000

95

145

195

Space Allocation of LCT

3.1

Model Formulation

Non-aeronautical (commercial) revenue is very importantfor LCT [5]. Airport authorities need to seize the business
opportunity from LCC passengers so that they cancompensate the lost in aeronautical revenue. This issue isaccommodated in this thesis through further discussion in
optimizing the allocation of commercial space inside
LCT building to ensure revenue maximization. The basic
concept of the optimization model was adapted from Hsu
and Chao (2005) [6] with several customizations to suit
LCT characteristics.
The very nature of simple design of LCTs might
results in less extensive range of facilities compared withordinary terminal. This study categorizes the store variety
in LCT into four main types: (1) Food and beverage store,(2) Travel convenience store, (3) Specialized store, (4)Duty-free Store.
The idea of the optimization model is to allocate
space for commercial activity in LCT that ensuresrevenue maximization. This study focus on departure area
since departure area is counted as more reliable source ofcommercial revenue compared to arrival area.

There are 4 alternative sites that are suitable for LCT
construction in an airport. There are 4 choices of terminal
configurations: linear (j = 1), single pier (j = 2), T-shaped
pier (j = 3) and Y-shaped pier (j = 4).

8500

3.

245

Average Walking Distance (m)Figure 2-2. Solution graph (zij = new terminal site i with configuration j)

130Technical Report of International Development Engineering

𝑀𝑎𝑥 𝑇𝑅 =

𝑙
𝑘=1

𝑚
𝑗 =1

𝑛
𝑖=1 𝑅𝑖𝑗𝑘 𝑋𝑖𝑗𝑘

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

the accumulation of passenger shopping time, initialvalue of T = 0.

(3.1)

Subject to:𝑛
𝑖=1 𝑋𝑖𝑗𝑘 ≤ 𝐴𝑗𝑘∀𝑘 = 1, … , 𝑙; 𝑗 = 1, … , 𝑚 (3.2)

𝑙
𝑘=1 𝑋𝑖𝑗𝑘 ≤ 𝑆𝑖𝑗∀𝑖 = 1, … , 𝑛; 𝑗 = 1, … , 𝑚 (3.3)(2)

Number of potential buyers in landside. Departurelandside is defined as the area before the passport
control which can be accessed by anyone(passengers and well-wishers/visitors) since it has
no entry barriers. Let 𝑡𝑝𝑓 denotes the landsidedwelling time of departure passenger p who takeflight f, where shortly 𝑡𝑝𝑓 is expressed asprobability distribution function 𝐹 𝑡 𝑑𝑡 .Variabletapf indicates arrival time in terminal, r denotes
ratio of well-wishers to departure passengers, wik
denotes probability of well-wishers to choose
concessions.

TR represents the total concession revenue in the
LCT. Xijk acts as the variable decision, represents square
meter area for concession type i in location j (departure
airside/landside) and location k (<10 m, 10-50 m, >50 mfrom entrance point). Xijk has to be in the range between
Sij (minimum area required to meet terminal design
standard) and Ajk (commercial space available).
𝑅𝑖𝑗𝑘 = 𝐶𝑖𝑗𝑘 𝐴𝑆𝑖 + 𝐴𝐶𝑖𝑗𝑘 𝐹𝑖 𝑃𝐵𝑖𝑗𝑘 (3.4)

Concession revenue is calculated based on charge
per square meter area (Cijk), in addition to certain
percentage of sales (Fi), where PBijk denotes number ofpotential buyers for each concession type i in location(j,k).
To define number of potential buyers, LCC
passenger dwelling time and LCC passenger preferences
toward concessions are incorporated into the model.
Potential buyer means individual that can reach certain
concession Xijk and remain there longer than the shortest
duration required for consumption activities (TOi),
defined as follows:(1) Number of potential buyers in airside. Departure
airside is defined as the secured area beyond
passport control that can only be accessed bydeparture passengers who have valid ticket andhave completed check-in, security and immigration
processes. Let 𝜃𝑝𝑓 denotes the airside dwelling timeof departure passenger p who take flight f, where
shortly 𝜃𝑝𝑓 is expressed as probability distributionfunction 𝐹 𝜃 𝑑𝜃.
𝜃𝑝𝑓 = 𝑡𝑏𝑓 − 𝑡𝑕𝑝𝑓 − (𝑡𝑐 + 𝑡𝑖 + 𝑡𝑠 +𝑃𝐵𝑖1𝑘 =𝑓𝑔

𝑣 𝑃𝐵𝑖𝑣𝑔𝑘 𝑃𝑓 𝑒𝑣

𝐷𝑓𝑣

𝑃𝐵𝑖1𝑘 =

𝑓𝑣 𝑃𝐵𝑖𝑣𝑔𝑘 𝑃𝑓 𝑒𝑣 +𝑔

𝑓 𝑃𝑓 𝑟𝑤𝑖𝑘(3.9)

𝑃𝐵𝑖𝑣𝑔𝑘 =
𝑏𝑔
𝐷
𝐹
𝑡
𝑑𝑡
;
if
𝑏
−
𝑇
≥
𝑇𝑂
where
𝑇 = 𝑇 + 𝑇𝑂𝑖𝑣𝑔 + 𝑘
𝑔
𝑖𝑣𝑔
𝑎𝑔
𝑉
0; otherwise

3.2(3.10)

Case Study

This study chooses Terminal 3 Soekarno-Hatta
International Airport as an example to demonstrate thefeasibility and usefulness of the constructed models forterminal commercial space allocation. Soekarno-Hatta
International Airport is located in Jakarta, Indonesia.
There are 2 LCCs operate in Terminal 3, AirAsia andMandala Air.
An online survey was conducted on December 23rd2010 – January 24th 2011 to reveal passenger statedpreference toward concessions. The total number ofcollected data is 296, with 222 of them are passengers
and 74 of them are well-wishers.

) (3.5)(3.6)

𝑃𝐵𝑖𝑣𝑔𝑘 =
𝑏𝑔
𝐷
𝐹
𝜃
𝑑𝜃
;
if
𝑏
−
𝑇
≥
𝑇𝑂
where
𝑇 = 𝑇 + 𝑇𝑂𝑖𝑣𝑔 + 𝑘
𝑔
𝑖𝑣𝑔
𝑎𝑔
𝑉
0; otherwise

𝑡𝑝𝑓 = 𝑡𝑕𝑓 − 𝑡𝑎𝑝𝑓 (3.8)

Preferences toward Concessions

160%
140%
120%

(3.7)

58.7%

100%

Equation 3.7 calculates number of potential buyers
in every concession as the sum of those who havedwell time range more than minimum time required
to undertake consumption activity (𝑏g − 𝑇 ≥ 𝑇𝑂ivg ).
Index v denotes index for number of passenger
preference types toward concessions (n type ofconcessions in LCT results in n! passenger
preferences). Boarding time of flight f is denoted by
tbf, while thpf denotes check-in time of departure
passenger p who takes flight f. Time required forpassenger to complete check-in, security,
immigration processes are denoted by tc, ts, ti
respectively. Df indicates the total walking distanceof passenger flight f to complete all compulsory
processes. Pf denotes number of passengers in flightf. Variable T acts as temporary variable that denotes

131

80%
60%

33.0%
12.0%

40%
20%
0%

9.3%
48.4%

11.7%
14.3%

16.0%
21.3%
19.1%
16.5%

8.8%

1.3%
7.4%
7.7%

2.7%
7.4%
4.4%

FB≻CS≻SS FB≻SS≻CS CS≻FB≻SS CS≻SS≻FB SS≻FB≻CS SS≻CS≻FB

Passenger Landside

Passenger Airside

Well-wisher Landside

Figure 3-1. Terminal user preferences toward concessions

Passenger dwelling time distribution is also observed.
The dwelling time observed in this study is a result ofobservation towards passenger arrival time, check-in timeand flight boarding time. Beta distribution is found as themost fitted probability density function for passenger

Technical Report of International Development Engineering

TRIDE-2011-01, February 15, 2011 (ISSN 1880-8468)

dwelling times in Terminal 3. At average, AirAsia andMandala Airlines passengers comes 1.5 - 2 minutes after
the check-in counter open. The mean of dwelling time in
airside is 66 minutes for AirAsia and 63 minutes for
Mandala Air passengers.

the revenue is potentially decreased around 15.04%.
When the available space in airside area is reduced 10.5%
from the demand, the revenue is potentially decreasedaround 13.3%. Moreover, 5% increase in well-wishers
ratio results in 4.2% increase in revenue; and 10%
increase in number of passengers who arrive 30 minutes
before check-in time, results in 8% increase in revenue.
For Terminal 3 which serves domestic flights, theideal airside – landside split in departure area is 72.9% –27.1% (where the actual space available split is 52.5% –47.5%). From the available space, only 47.7% (878 m2)
and 43.4% (659 m2) are currently utilized for concessions
in airside and landside, respectively.

Figure 3-2. Landside and airside dwell time

This study applies the models formulated in previous
section to allocate spaces and positions for concessions in
Terminal 3. Under current commercial strategy of airsideand landside split, the terminal has relatively unbalanced
arrangement. There are 136 m2 (8.09%) differences
between the space demand and the capacity in airside,
while there are 946 m2 (62.2%) differences between
space demand and the capacity in landside.

4.

The issue of whether “LCTs are good for airports” iscertainly controversial in the air transport industry. Froman organizational standpoint, the airports and airlines
have diametrically opposite views on the matter. This
thesis has tackled two problems that potentially arose in
LCTdevelopment:
operational
efficiency andprofitability.
- Single pier is the most suitable configuration for
LCT since it gives shortest walking distance and also
requires smallest construction area.
- Solution with shorter aircraft taxiing distance is more
preferable than shorter passenger walking distance.
- The demography of LCC passengers is dominated byyoung people with relatively low income level who
travel for leisure purposes. This characteristic
becomes one of the factors that influence passenger
behavior toward concessions in LCT.
- The stores with highest potential revenue should beallocated in the more accessible positions with higher
passenger flow.
- Sales from the airside still accounts for greater
amount of retail revenue. For terminal with low
number of non-travelling customers (visitors, wellwishers), more commercial space should be allocatedin airside.
With better understanding of the nature of the LCT,
including its shortages and opportunities, LCT could
become a sustainable concept that can be adopted bymany airports all over the world.

Table 3-1. Optimal square meters and position for concessions
Space

Concessions demand(i)(m2)
Food and
690
Beverage
Travel624
Convenience
Specialized
230
Store
Total space
1544
demandAllocated space (m2) at
Allocated space (m2) atSpacedeparture airside
departure landside
demand< 10 10 - 50 > 50(m2) < 10 m 10 - 50 m > 50 m
m
m
m

220

470

0

360

360

0

0

0

0

624

145

20

125

0

0

170

60

69

0

69

0

220

640

684

574

380

194

0

Capacity

1680

220

640

820

1520

380

560

580

Area
occupied

878

220

640

18

659

380

279

0

Terminal 3 authority intends to attract other terminal
users beside passengers (visitors, well-wishers andgreeters) to visit and consume in concessions; they put
quite large area in departure landside. However, in
Terminal 3, the ratio of well-wishers to passengers isquite low (r = 0.2) since it only serves domestic shorthaul flights.

Concession Revenue/Month(Millions IDR)IDR 900
Airsi
de

IDR 800
IDR 700

Conclusions

IDR 600
Land
side

IDR 500

References

IDR 400
IDR 300

Total

[1]

IDR 200
IDR 100

[2]

IDR 0
0%

20%

40%

60%

80%

[3]

Landside %[4]

Figure 3-3. Optimal landside – airside split
1 USD  9,009 IDR[5]Sensitivity analysis is conducted to see how therevenue is influenced by the changes in commercial space
available in departure area. When the available space inlandside area is reduced 9.8% from the demand (518 m 2),

[6]

132

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