ISSN2188－1200

International Project Research-Workshop (I)Proceedings of 4th Malaysia Mathematics inIndustry Study Group (MMISG2023)

Chief Editor : Zaitul Marlizawati Zainuddin, Arifah Bahar

Editors: Shariffah Suhaila Syed Jamaludin, Zaiton Mat Isa, Nur Arina Bazilah Aziz,

Taufiq Khairi Ahmad Khairuddin, Shaymaa M.H.Darwish, Ahmad Razin Zainal Abidin,
Norhaiza Ahmad, Zainal Abdul Aziz, Hang See Pheng, Mohd Ali Khameini Ahmad
九州大学マス・フォア・インダストリ研究所

MI Lecture Note Vol.97 : Kyushu University

International Project Research-Workshop (I)Proceedings of 4th Malaysia Mathematics inIndustry Study Group (MMISG 2023)■Chief Editors:
Zaitul Marlizawati Zainuddin, Arifah Bahar■Editors:S hariffah Suhaila Syed Jamaludin, Zaiton Mat Isa, Nur Arina Bazilah Aziz,
Taufiq Khairi Ahmad Khairuddin, Shaymaa M.H. Darwish, Ahmad Razin Zainal Abidin,
Norhaiza Ahmad, Zainal Abdul Aziz, Hang See Pheng, Mohd Ali Khameini Ahmad

AAbout MI Lecture Note Series
The Math-for-Industry (MI) Lecture Note Series is the successor to the COE Lecture
Notes, which were published for the 21st COE Program “Development of Dynamic
Mathematics with High Functionality,” sponsored by Japan’s Ministry of Education,
Culture, Sports, Science and Technology (MEXT) from 2003 to 2007. The MI Lecture Note Series has published the notes of lectures organized under the following two
programs: “Training Program for Ph.D. and New Master’s Degree in Mathematics as
Required by Industry,” adopted as a Support Program for Improving Graduate School
Education by MEXT from 2007 to 2009; and “Education-and-Research Hub for
Mathematics-for-Industry,” adopted as a Global COE Program by MEXT from 2008 to2012.
In accordance with the establishment of the Institute of Mathematics for Industry (IMI)in April 2011 and the authorization of IMI’s Joint Research Center for Advanced andFundamental Mathematics-for-Industry as a MEXT Joint Usage / Research Center in
April 2013, hereafter the MI Lecture Notes Series will publish lecture notes and proceedings by worldwide researchers of MI to contribute to the development of MI.
October 2022
Kenji Kajiwara
Director, Institute of Mathematics for Industry

International Project Research-Workshop (I)Proceedings of 4th Malaysia Mathematics inIndustry Study Group (MMISG2023)
MI Lecture Note Vol.97, Institute of Mathematics for Industry, Kyushu University
ISSN 2188-1200
Date of issue: March 28, 2024
Chief Editors: Zaitul Marlizawati Zainuddin, Arifah Bahar
Editors: Shariffah Suhaila Syed Jamaludin, Zaiton Mat Isa, Nur Arina Bazilah Aziz, Taufiq Khairi Ahmad Khairuddin, Shaymaa M.H. Darwish, Ahmad Razin Zainal Abidin,
Norhaiza Ahmad, Zainal Abdul Aziz, Hang See Pheng, Mohd Ali Khameini Ahmad
Publisher:
Institute of Mathematics for Industry, Kyushu University
Graduate School of Mathematics, Kyushu University
Motooka 744, Nishi-ku, Fukuoka, 819-0395, JAPANTel +81-(0)92-802-4402, Fax +81-(0)92-802-4405
URL https://www.imi.kyushu-u.ac.jp/

BPREFACE
Mathematics in Industry Study Group is a collaborative problem-solving platform, uniting appliedmathematicians to address real-world challenges across industries. Originating at Oxford University inthe 1960s, this format has been instrumental in fostering practical solutions globally. At Universiti
Teknologi Malaysia (UTM), we take pride in being pioneers, having introduced this study group to
Malaysia since 2011. Our commitment continues and is evident in the recent organization of the 4th
Malaysia Mathematics in Industry Study Group (MMISG2023) held at Menara Razak, Universiti
Teknologi Malaysia, Kuala Lumpur, from November 20th to 22nd, organized by UTM Centre forIndustrial and Applied Mathematics (UTM-CIAM). It was also made possible through the joint effortsof UTM Department of Mathematical Sciences, Faculty of Science; the Institute of Mathematics for
Industry at Kyushu University, Japan; and MYHIMS Solutions LLP.
In general, the primary objective of Malaysia Mathematics in Industry Study Group-MMISG is tobridge the gap between academia and industry in Malaysia. It is a platform for STEM researchers toapply their expertise to solve industrial relevant problems, fostering a collaborative spirit between thetwo domains.
At MMISG2023, five industries, namely Ranhill SAJ Sdn Bhd, Total Logistic Services (M) Sdn. Bhd.,
MYHIMS Solution LLP, Langkawi Development Authority (LADA), and Ifactors Sdn. Bhd. presented
their industrial problems. The problems spanned from model reconstruction at archaeological sites to
cutting-edge developments in the logistic industry, and from forecasting volatility of agriculturalcommodity prices to optimizing operations processes.
To tackle the array of industrial issues, five cohesive sub-groups were formed, each guided by selecteddomain advisors. Within each group, members (termed as contributors) comprising of mathematicians,
engineers, computer scientists played crucial roles in identifying key scientific issues and mathematicalchallenges. More than 50 contributors from ten institutions, both local and international, including
Universiti Teknologi Malaysia, Universiti Malaya, Universiti Teknologi MARA, Universiti Malaysia
Sabah, Universiti Kebangsaan Malaysia, Universiti Malaysia Terengganu, Universiti Malaysia Pahang
Al-Sultan Abdullah, Universiti Teknologi Petronas, Kyushu University, Japan and Universitas Negeri
Malang, Indonesia participated. Online discussions were also conducted with UTM-CIAM international
partner at Oxford Centre for Industrial and Applied Mathematics (OCIAM). This collaborative effort
harnessed the expertise from various institutions, enriching the event with a wide spectrum ofmathematical and statistical approaches, all unified in the common goal of effective problem-solving.
This initiative seamlessly aligns with UTM's mission to actively engage in both local and global
networks, fostering partnerships with diverse organizations and industries to drive innovation throughinnovative solutions. The MMISG2023 has once again proven to be a pivotal event in fosteringcollaboration between academia and industry and further solidifies the event's reputation as a catalyst
for innovation and problem-solving. It is evidenced from the positive feedback received from theparticipants:
industrial
representatives
andcontributors.
In addition to the solutions and recommendations generated for the industry, MMISG2023 has also
catalyzed other significant outcomes, highlighting its impact as a significant program of Mathematics
in Industry. Preparations are underway for Memorandum of Understanding and Letters of Collaboration
between UTM-CIAM and the participating industries. Furthermore, technical reports have been
published, and the MI Lecture Note serves as another avenue for disseminating MMISG outputs.
Moreover, MMISG2023 marked the beginning of a larger project focused on system development for
Ifactors Sdn. Bhd. The continuation of studies initiated during MMISG2023 is anticipated, with the
expectation of yielding even more impactful results for the participating industries.
Chief Editors: Zaitul Marlizawati Zainuddin, Arifah Bahar
February 2024i

CONTENTS
Page
Preface
History of Malaysia Mathematics in Industry Study Group
Organizing Committee
Programme Schedule
List of Participants
Event Photos
Abstracts
Ifactors Sdn. Bhd.: Transient Modelling of Subsea Cable Laying
LADA: 3D Shape Reconstruction of Charcoal Chamber Monument
Ranhill SAJ Sdn. Bhd.: Predictive Modelling of Pipe Burst Towards
Sustainable Non-Revenue Water
TLS (M) Sdn. Bhd.: Tackling Carbon Emissions: Strategies for Medium andHeavy-Duty Trucks
MYHIMS Solution LLP: Price Forecasting Model for Main Vegetables in
Malaysia
Papers and Presentations
Ifactors Sdn. Bhd.: Transient Modelling of Subsea Cable Laying
LADA: 3D Shape Reconstruction of Charcoal Chamber Monument
Ranhill SAJ Sdn. Bhd.: Predictive Modelling of Pipe Burst Towards
Sustainable Non-Revenue Water
TLS (M) Sdn. Bhd.: Tackling Carbon Emissions: Strategies for Medium andHeavy-Duty Trucks
MYHIMS Solution LLP: Price Forecasting Model for Main Vegetables in
Malaysia

ii

i
1
3
4
5
68
11
9
12
10
13
11
14
12
15
13
16
14
51
69
71
107
111

151
145

HISTORY OF MALAYSIA MATHEMATICS IN INDUSTRY (MMISG)
Mathematics in Industry Study Group is a collaborative problem-solving platform, uniting
applied mathematicians to address real-world challenges across industries. Originating at
Oxford University in the 1960s, this format has been instrumental in fostering practicalsolutions globally.
At Universiti Teknologi Malaysia (UTM), we take pride in being pioneers, having introduced
this study group to Malaysia since 2011. The second MISG was held in 2014 and the third in2015.
MMISG 2011
The 1st MMISG was attended by 4 industries with 6 problems.
i)
ii)
iii)
iv)
v)
vi)

Optimal Placement of Security Cameras in In-door Spaces from MIMOS Bhd.
Efficient Reasoning in Conceptual Graph Knowledge Representation Model fromMIMOS Bhd.
River Pollution Problem in Iskandar Malaysia Region from J-Biotech
Cardiological Problem from KPJ Johor
Fouling in Heat Exchangers of Refineries from Petronas Penapisan (Melaka) Sdn. Bhd.
Oil Refinery Water Supply from Petronas Penapisan (Melaka) Sdn. Bhd.

MMISG 2014
The 2nd MMISG was attended by 6 industries with 6 problems.
i)
ii)
iii)
iv)
v)
vi)

Mathematical Modelling and Optimization for Biological-based Treatment of Taman
Timor Oxidation Pond, Johor from J-Biotech
River Pollution Attenuation in Aquifers Using Bank Infiltration Technique from
National Water Research Institute of Malaysia (NAHRIM)
Development of early warning system for rice blast and brown planthopper (BPH), pest
and disease of rice from Malaysian Agricultural Research and Development Institute(MARDI)
Coronary Bifurcation – Effect of Stent on Blood Flow from KPJ Johor
Green Propulsion Regenerative Electromagnetic Suspension System (GPRESS) from
PROTON
Cost Effective Minimum Water and Energy Networks for The Industries fromPROSPECT

MMISG 2015
The 3rd MMISG was attended by 7 industries with 7 problems.
i)ii)

Modelling Thiophene and Pyran -4-ONE to Early Signs of Ganoderma Infection in Oil
Palms from Agri-D Sdn. Bhd.
Optimal Stock Level and Inventory Policy from EP Manufacturing Berhad (EPMB)

1

iii)
iv)
v)

Subsea Cable Laying Problem from Ifactors Sdn. Bhd.
Multi-Sensor Imaging System for Target Detection from IR Technic Sdn. Bhd.
Modelling of Optimal Conservation of Giant Clam Species in Johor Islands from JBiotechvi) Modelling and Optimization of Clay-based Product for Green Cleansing from MIFF
Marketing Sdn. Bhd.
vii) Magneto-Rheological Semi-Active Suspension System (MaRS) from PROTON
Our commitment continues and is evident in the recent organization of the 4th Malaysia
Mathematics in Industry Study Group (MMISG2023) held at Menara Razak, Universiti
Teknologi Malaysia, Kuala Lumpur, from November 20th to 22nd, 2023.

2

ORGANIZING COMMITTEE
Chairman: Assoc. Prof. Dr. Zaitul Marlizawati Zainuddin
Co-Chair: Assoc. Prof. Dr. Shariffah Suhaila Syed Jamaludin
Secretary: Dr. Zaiton Mat Isa
Assistant Secretary: Mrs. Nik Zetti Amani Nik Faudzi
Treasurer: Dr. Nur Arina Bazilah Aziz
Assistant Treasurer: Mrs. Siti Nur Sakinah Ahmad
Scientific
Assoc. Prof. Dr. Arifah Bahar
Prof. Dr. Sharidan Shafie
Dr. Taufiq Khairi Ahmad Khairuddin
Assoc. Prof. Dr. Zarina Mohd Khalid
Assoc. Prof. Dr. Zuhaila Ismail
Prof. Dr. Zulkepli Majid
Dr. Koh Meng Hock
Dr. Adila Firdaus Arbain
Technical and Facility
Mr. Wan Rohaizad Wan Ibrahim
Dr. Ahmad Qushairi Mohamed
Assoc. Prof. Dr. Yeak Su Hoe
Dr. Amir Syafiq Syamin Syah Amir Hamzah
Dr. Tan Lit Ken
Protocol, Food, and Hospitality
Assoc. Prof. Dr. Norhaiza Ahmad
Dr. Syarifah Zyurina Nordin
Ms. Siti Zulfarina Fadzli
Dr. Noorehan Yaacob
Publication
Dr. Hang See Pheng
Assoc. Prof. Dr. Shariffah Suhaila Syed Jamaludin
Dr. Mohd Ali Khameini Ahmad
Accomodation, Transportation, and Registration
Dr. Nur Arina Bazilah Kamisan
Dr. Mohammad Izat Emir Zulkifly
Dr. Hafizah Farhah Saipan @ Saipol
Dr. Dzuliana Fatin Jamil
Mrs. Nurul Husna Mohammad Sairi
Mrs. Wong Yah Jin
Ms. Aniza Akaram
Intellectual Property
Dr. Ahmad Razin Zainal Abidin
Dr. Izyan Hazwani Hashim

3

PROGRAMME SCHEDULE
Time

Activity

Venue

20 November 2023 (Monday)
Menara Razak Seminar
Hall/Banquet Hall

0830 – 0915

Registration & Morning Tea

0915 – 0945

Opening Ceremony/Photo Session

0950 – 1010

Ranhill SAJ Problem Presentation

1015 – 1035

MYHIMS Solutions Problem
Presentation

1040 – 1100

Ifactors Sdn Bhd Problem
Presentation

1105 – 1125

TLS Problem Presentation

1130 – 1150

LADA problem Presentation

1200 – 1400

Lunch BreakBanquet Hall

1400 – 1730
1530 – 1600

Group Formation/Group Discussion
Afternoon Tea

Room 2, 3, 4, 5, 7
Banquet Hall

Menara Razak SeminarHall

21 November 2023 (Tuesday)
0830 – 1230
1000 – 1030

Group Discussion
Morning Tea

Room 2, 3, 4, 5, 7
Banquet Hall

1230 – 1400

Lunch BreakBanquet Hall

1400 – 1730
1530 – 1600

Group Discussion
Afternoon Tea

Room 2, 3, 4, 5, 7
Banquet Hall

22 November 2023 (Wednesday)
0830 – 1100
0930 – 1000

Group Discussion
Morning Tea

Room 2, 3, 4, 5, 7
Banquet Hall1105 – 1200

Output Presentation to The Industry

Room 2, 3, 4, 5, 71205 – 1215

Closing Ceremony

Banquet Hall

1215

Lunch

Banquet Hall

4

LIST OF PARTICIPANTS

NO

NAME

EMAIL

AFFILIATION

1.

Sharidan Shafie

sharidan@utm.my

2.

Zulkepli Majid

zulkeplimajid@utm.my3.

Arifah Bahararifah@utm.my

4.

Norhaiza Ahmad

norhaiza@utm.my

Shariffah Suhaila Syed
Jamaludin
Zaitul Marlizawati
Zainuddin
Istas Fahrurrazi
Nusyirwan

zmarlizawati@utm.my

8.

Zulhasni Abdul Rahim

zulhasni@utm.my

9.

Ahmad Razin Zainal
Abidinarazin@utm.my

10.

Dzuliana Fatin Jamildzulianafatin@utm.my

5.
6.
7.

11.
12.

Hafizah Farhah
Saipan@Saipol
Mohd Ali Khameini
Ahmad

suhailasj@utm.my

istaz@utm.my

hafizah.farhah@utm.my
ma.khameini@utm.my

13.

Noorehan Yaacob

noorehan@utm.my

14.

Nur Arina Bazilah Aziz

nurarina@utm.my

15.

Syarifah Zyurina
Nordinszyurina@utm.my

16.

Tan Lit Ken

tlken@utm.my

17.

Taufiq Khairi Ahmad
Khairuddin

taufiq@utm.my

18.

Zaiton Mat Isa

zaitonmi@utm.my

19.

Adila Firdaus Arbain

adilafirdaus@utm.my

20.

Mohamad S.J. Darwish

sjmohamad@utm.my

21.

Mohd Hazmil Syahidy
Abdol Azis

hazmil@utm.my

22.

Shaymaa M.H. Darwish

mdshaymaa@utm.my23.

Siti Mariam
Norrulashikin

sitimariam@utm.my

24.

Siti Rohani Mohd Nor

sitirohani@utm.my

25.

Aniza Akaram

aniza.akaram@utm.my

26.

Wan Rohaizad Wan
Ibrahimwrohaizad@utm.my

5

UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM : Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia

AFFILIATION
CODE
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

NO

NAME

27.

Justin Chan Zhe

jczhe09@gmail.com

28.

Khalid Solaman
Almadani

almadani@graduate.
utm.my29.

Lai Kok Yee

lai931010@hotmail. com

30.
31.

Nik Zetti Amani Nik
Faudzi
Ruzaini Zulhusni Bin
PuslanEMAIL

AFFILIATION

nikzettiamani@utm.my
ruzainizulhusni@gmail.
com
shukur.hasan@gmail.
com

32.

Shukur Hasan

33.

Zainol Mustafa

zbhm@ukm.edu.my

34.

Zulkifli Mohd Nopiah

zmn@ukm.edu.my35.

Nor Hamizah Miswan

36.

Razik Ridzuan Mohd
Tajuddinnorhamizah@ukm.edu.
my37.

Ramhya Kathirayson

38.

Adibah Shuib

39.

Abd Manan Samad40.

Zaharah Mohd Yusoff

zmy1208@uitm.edu.my

41.

Ismail Ma'arofismailmaarof@uitm.
edu.myrrmt@ukm.edu.myramhya1311@gmail. com
adibah@tmsk.uitm.edu.
mydr_abdmanansamad@
ieee.org

Mohd Mahayaudin Bin
MansorAuni Aslah Bin Mat
DaudRoslinazairimah
Zakaria

auni_aslah@umt.edu. my45.

Nor Azuana Ramliazuana@umpsa.edu. my46.

Norazaliza Mohd Jamilnorazaliza@ump.edu. my47.

Syahrizal Bin Sallehsyahrizal@gmail.com

48.

Jamaliatul Badriyahjamailatul.badriyah.mat@um.ac.id

42.
43.
44.

50.
51.
52.

Siti Rahayu Binti Mohd
HashimMohd Ismail Abd Aziz
Fadhilah Yusof
Zainal Abdul Aziz

53.

Aimi Athirah Ahmad

54.

Tagami, Daisuke

55.

Shota Shigetomi

56.

Putri Aidila Sofia Abu
Bakar

49.

maha@uitm.edu.myroslinazairimah@ump.
edu.myrahayu@ums.edu.my

UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UTM: Universiti Teknologi
Malaysia
UKM: Universiti Kebangsaan
Malaysia
UKM: Universiti Kebangsaan
Malaysia
UKM: Universiti Kebangsaan
Malaysia
UKM: Universiti Kebangsaan
Malaysia
UKM: Universiti Kebangsaan
Malaysia
UITM : Universiti Teknologi
Mara
UITM: Universiti Teknologi
Mara
UITM : Universiti Teknologi
Mara
UITM : Universiti Teknologi
Mara
UITM : Universiti Teknologi
Mara
UMT : Universiti Malaysia
Terengganu
UMP : Universiti Malaysia
Pahang
UMP : Universiti Malaysia
Pahang
UMP: Universiti Malaysia
Pahang
UMP: Universiti Malaysia
PahangUniversitas Negeri Malang
UMS: Universiti Malaysia
Sabah
MYHIMS Solutions
MYHIMS Solutions
MYHIMS Solutions

mismail@utm.my
fadhilahy@utm.my
zainalaz@utm.myaimiathirah@mardi.gov.
MYHIMS Solutions
mytagami@imi.kyushuKyushu University
u.ac.jps-shigetomi@imi.kyushuKyushu University
u.ac.jpaidila@ifactors.com.my

6

Ifactors

AFFILIATION
CODE
1
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
5
5
5
5
6
7
8
8
8
8
9
9
10

NO

NAME

57.

Kamarudin Ismail

58.
59.
60.

Muhammad Hanis Bin
Kamaruddin
Norazlina Ismail
Aidah Natasah
Sulaiman

61.

Nur Nabilah Jamal

62.

Raja Dzulfitrie Sa’ad
Zainal Azman Bin
Zawawi

63.
64.

Hanita DaudEMAILAFFILIATION

kamarudin@ifactors.
com.mymuhammadhanis@
graduate.utm.my
i-norazlina@utm.my
aidah.natasah@ranhill.
com.mynabilah.jamal@gmail.
com
dzulfitrie@tls.com.my
zainal@tls.com.my
hanita_daud@utp.edu.my

7

AFFILIATION
CODE

Ifactors

10

Ifactors

10

Ranhill SAJ

11

Ranhill SAJ

11

Ranhill SAJ

11

TLS

12

TLS

12

UTP: Universiti Teknologi
Petronas13

EVENT PHOTOS

MMISG2023 group photo following the opening ceremony, at Dewan Seminar Menara Razak, UTM Kuala Lumpur.

Industry representatives presenting their industrial problems to the participants of MMISG2023.

8

Intense discussion among industrial representatives, domain advisors and contributors of respective industrial
problems

9

The dedicated organizing team of MMISG2023 comprising of staﬀ from UTM-CIAM, UTM
Department of Mathema�cal Sciences, Faculty of Science and UTM- Malaysia-Japan Interna�onal
Ins�tute of Technology

10MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP(MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

Transient Modeling of Subsea Cable Laying
Industrial Representative:
Kamarudin Ismail10, Putri Aidila Sofia Abu Bakar10
Contributors:
Zainal Abd Aziz8, Sharidan Shafie1, Ahmad Razin Zainal Abidin1,
Shaymaa M. H. Darwish1, Mohd Hazmil Syahidy Abdol Azis1, Nik
Zetti Amani Nik Faudzi1, Noorehan Yaacob1, Dzuliana Fatin Jamil1,
Daisuke Tagami9, Auni Aslah Bin Mat Daud4, Norazaliza Mohd
Jamil5The installation of subsea cables, involving the deployment of cables on the ocean floor, issignificantly influenced by environmental factors such as ocean currents and seabedcharacteristics. Modeling this process is crucial during both planning and operational phases,
as it enables accurate predictions and control over cable placement, thereby minimizing risks
and ensuring the effectiveness, safety, and environmental sustainability of subsea installations.
The challenge in such modeling lies in the dynamic nature of the ocean environment, which
includes fluctuating currents, waves, and winds, coupled with the complexity of varying seabedterrains, requiring the consideration of transient effect. This report proposes the developmentof a dynamic model for cable laying that accounts for wave motion and the irregularities of theseafloor, merging theoretical research with practical applications and experience, creating
relevant and applicable models for real-world operations. The model development is structured
into three phases. The first phase involves a steady-state model focused on a rough seabed,omitting the impact of waves. This model is based on the Euler–Bernoulli equation that aims
to predict the cable layout at a specific time, considering initial tension, cable properties, andfluid dynamics. The complexity is reduced by introducing a non-dimensional set of equations,which scales down all engineering parameters to two key factors: fluid hydrodynamic drag andcable bending stiffness. Applied across various sea depths and using MATLAB for numericalsolutions, the finding suggests that cable elevation increases with depth. The second phasetackles a time-dependent issue, factoring in significant wave motion on a hypothetically flat
seabed, thus leading to a highly nonlinear formulation that requires the inclusion of a transientmodel. In the final phase, the report integrates both models to represent water depth as afunction of time and arc length for more comprehensive insights. Expected results from thisintegration are anticipated to enhance the accuracy and practicality of the developed model inthe real cable installation operation.

11

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP(MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

3D Shape Reconstruction of Charcoal Chamber Monument
Industrial Representative:
Rasanubari Asmah Rahmah Abd Hamid14
Contributors:
Taufiq Khairi Ahmad Khairuddin1, Zulkepli Majid1, Mohd Ali
Khameini Ahmad1, Abd Manan Samad3, Ismail Ma’arof3, Shota
Shigetomi9

Langkawi Island is a world-class tourism destination as it contains various historical assets andbecause of this, Langkawi Island has been recognized as one of the world-class Geopark sites
by UNESCO. As a world-class Geopark area and site, Langkawi Island provides 38 Geosites
for tourists to visit. One of the tourism assets found in the Geosite area is the charcoal chambermonument in Kubang Badak, Bukit Menora. According to historians, there were originally 12
charcoal chamber monuments but, as time has passed, only three of these structures haveendured, and they too have weathered the years, with only partial sections of their original
construction remain. The agency Langkawi Development Authority (LADA) then has been
entrusted to preserve and conserve these valuable relics for tourism activities and local
economy development. The main purpose of this study is to redevelop the complete charcoal
chamber monument by using the combination of three-dimensional (3D) modelling in
geoinformation and also mathematical modelling. Specifically, the objective of this research is
to develop a 3D model of the remaining historic charcoal chamber using Geospatial technology
before simulating the partial data from the model with the appropriate mathematicalformulation, in order to generate the complete 3D model of the monument. Besides, geometricmodeling involving common surfaces such as elliptic paraboloids and hemispheres will be usedin developing and predicting the actual dimensions of the charcoal chamber. Apart from that,
Mathematica and Matlab software will be used to display the 3D model of the charcoal
chamber. Based on the latest information during the discussion session held, three 3D modelsof the charcoal chamber have been proposed at the end of this MMISG 2023 workshop.

12

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP(MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

Predictive Modeling of Pipe Burst Towards Sustainable Non-Revenue Water
Industrial Representative:
Aida Natasah Sulaiman11 , Nur Nabilah Jamal11, Norazlina Ismail11
Contributors:
Norhaiza Ahmad1, Zainol Mustafa2, Arifah Bahar1, Zaiton Mat
Isa1, Istas Fahrurazi Nursyirwan1, Mohamad S.J. Darwish1, Tan
Lit Ken1, Hafizah Farhah Saipan @ Saipol1, Aniza Akaram1,
Ramhya Kathirayson2

Pipe burst is one of the major contributors to water loss in water supply systems thatsignificantly increase the Non-Revenue Water (NRW) level. The water authority has set atarget to reduce NRW to 25.5% by 2024. Although the NRW level is currently close to thetarget at approximately 25.40% or 457 million liters per day (MLD) as of September 2023,
there is a need for ongoing monitoring to prevent any potential increase in NRW if water losses
are not effectively controlled. The key challenge identified is the timely detection andprediction of pipe bursts, as this is crucial for the organization to make informed decisions inmonitoring water losses and minimizing the time taken to address and rectify pipe bursts. In
response to this challenge, two approaches are suggested with the primary focus on thestatistical approach, alongside with the Artificial Neural Network (ANN) model. For thestatistical approach, the Exponentially Weighted Moving Average (EWMA) Control Charts isused to identify transient signals related to pipe bursts. Data preprocessing has been conducted,
and the control charts have been applied to detect anomalies associated with pipe bursts.
Feature-based data also been implemented to further enhance the detection capabilities. The
study concludes that a combination of dynamic thresholding based on weekly patterns, targeted
analysis of reliable Flow data, and adaptive parameter settings in control charts, particularly
with a focus on shorter time series, enhance the effectiveness of pipe burst detection. The
findings recommend a pragmatic approach to address the challenges posed by variations andtrends in water monitoring data, providing valuable insights for proactive water systemmanagement and minimizing the impact of false alarms in anomaly detection. Additionally,
utilizing pressure, flow, and CPP pressure data as inputs, an Artificial Neural Network (ANN)model is trained to predict pipe bursts. By learning from historical data where instances ofbursts are correlated with specific patterns in these parameters, the ANN can quickly analyze
current data upon receiving a complaint call and predict the likelihood of a forthcoming burst.

13

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP(MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

Tackling Carbon Emissions:
Strategies for Medium and Heavy-Duty Trucks
Industrial Representative:
Raja Dzulfitrie Sa’ad12
Zainal Azman Zawawi12
Contributors:
Nur Arina Bazilah Aziz1, Adibah Shuib3, Zaitul Marlizawati Zainuddin1, Zulkifli Mohd
Nopiah2, Zulhasni Abdul Rahim1, Zulkarnain Abdul Latiff1, Zaharah Mohd Yusoff3,
Jamaliatul Badriyah6, Syarifah Zyurina Nordin1, Wan Rohaizad Wan Ibrahim1, Shukur
Hassan1, Khalid Solaman Almadani1

The escalating carbon emissions from transportation, particularly from medium-duty andheavy-duty trucks, pose significant challenges to the automotive supply chain logistics sector.
Notably, the transport sector accounts for a substantial 28.8% of total fossil fuel combustion
emissions, exceeding the global average. In response, Malaysia has committed to ambitious
targets, aiming for net-zero emissions by 2050 and a 45% reduction in CO2 intensity relative
to GDP by 2030. In accordance with this, industries, including Total Logistic Services (M)Sdn. Bhd. (TLS) are mandated to comply with regulations to mitigate carbon emissions. As
part of the Toyota Tsusho Group, TLS is engaged in a three-year strategy focusing on route
optimization, eco-driving training, vehicle maintenance, and transitioning to electric trucks totackle this challenge. In addition, TLS is also exploring avenues to further reduce the carbon
emissions from their medium-duty and heavy-duty trucks. The discussion begins byexamining the formula used to calculate carbon emissions, analyzing the parameters
influencing carbon emissions, and proposing initiatives associated with these parameters.
Additionally, the discussion also delves into tools used to measure carbon emissions. In
conclusion, it is asserted that the formula for calculating carbon emissions is adequate for
estimating the emissions from trucks without the need for specific tools. The controllable
factor identified is fuel efficiency, which is closely linked to driving style. Consequently,
initiatives aimed at improving drivers' driving style are recommended.

14

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP(MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

Price Forecasting Model for Main Vegetables in
Malaysia
Industrial Representative:
Aimi Athirah Ahmad8
Contributors:
Shariffah Suhaila Syed Jamaludin1, Fadhilah Yusof8,
Roslinazairimah Zakaria5, Mohd Mahayaudin Mansor3,
Nor Hamizah Miswan2, Siti Rahayu Mohd Hashim7,
Siti Mariam Norrulashikin1, Siti Rohani Mohd Nor1,
Hanita Daud13, Razik Ridzuan Mohd Tajuddin2,
Syahrizal Salleh5, Ruzaini Zulhusni Puslan1

A substantial portion of household income in Malaysia is allocated to food expenditures, andchili is a staple ingredient in Malaysian cuisine. Fluctuations in chili prices directly affect thecost of living for individuals and families, impacting their purchasing power and overall wellbeing. Forecasting chili prices helps in effective supply chain management. Producers,
distributors, and retailers can plan and adjust their operations based on anticipated price trends.
This, in turn, contributes to the country's efficiency and stability of the chili supply chain. This
study emphasizes the importance of comparing various forecasting models to identify the mostaccurate predictors of chili prices. The goal is to develop a model that can contribute to more
informed decision-making in crop production and market interventions, ultimately promoting
stability in the chili industry and ensuring sustainable practices. Statistical models and timeseries forecasting models, which include multiple linear regression (MLR), AutoRegressive
Integrated Moving Average with exogenous inputs (ARIMAX), and machine learningalgorithms that consist of Support Vector Regression (SVR), Random Forest Regression(RFR), Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) were tested
and compared. SVR and RF under machine learning algorithms performed best as theforecasted model followed by ARIMAX. ARIMAX models, extension of the ARIMA model,
effectively capture and predict patterns by incorporating significant exogenous variables.
Overall, the results show that the price of fertilizers, Movement Control Order (PKP) season,and chili production significantly affect the prices of chilies.

15

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

Transient Modeling of Subsea Cable Laying
Industry Representatives:

Kamarudin Ismail10, Putri Aidila Sofia Abu Bakar10(Ifactors Sdn. Bhd.)

Study Group Contributors:

Zainal Abd Aziz8, Sharidan Shafie1, Ahmad Razin Zainal
Abidin1, Shaymaa M. H. Darwish1, Mohd Hazmil
Syahidy Abdol Azis1, Nik Zetti Amani Nik Faudzi1,
Noorehan Yaacob1, Dzuliana Fatin Jamil1, Daisuke
Tagami9, Auni Aslah Bin Mat Daud4, Norazaliza Mohd
Jamil5

1.1 Introduction
Subsea cable laying, essential in the development of global communication and power
networks, involves deploying cables on the ocean floor, a process influenced by a range ofenvironmental factors, including ocean currents and seabed characteristics. Cable laying
modeling is vital in planning and operation as it enables precise prediction and control of cable
placement, minimizing risks and ensuring efficient, safe, and environmentally responsible
subsea installations.
Ifactors is a subsea cable installer that provides services to Telco operators and oil andgas companies that need to install subsea cable for their IT or DATA network infrastructure.
The types of cable installed are mainly fibre optics and composite. Ifactors is the only local
installer that has experience in laying deep water (>2000m) and also the only Malaysiancompany that carries the ISO 9001:2008 certification. Among the projects handled by Ifactors
were, Deep Water repair (>2500m) in Indonesia, Deep Water lay from Bali to Jawa Island,
Indonesia and also Petronas platform interconnection with shore in Turkmenistan and
Peninsular. With over a decade of experience, including cable laying in challenging locations
like the deep waters, Ifactors has emerged as a leading service provider in the cable laying
sector in the Asia Pacific Region. This study focuses on the cable laying challenges encounteredby Ifactors.
Installing subsea cables is a routine responsibility for engineers, involving theplacement of cables and pipelines beneath the ocean floor. The laying process is a critical andperilous phase for the cable, demanding meticulous control to prevent any damage while16

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

optimizing the speed of installation. During cable installation, a common challenge is theoccurrence of false slack. Submarine cables are usually deployed by lowering them from alaying vessel into seawater until they reach the seafloor. Throughout this process, the cables are
subjected to various forces, requiring careful consideration in cable design. While theconventional approach involves analyzing cables during installation with the assumption of ahorizontally flat seafloor, the real seafloor is marked by roughness. Bathymetric maps of theseafloor reveal faults, folds, and on-bottom channel systems. These irregularities may lead tothe development of false slack in certain areas or cause cable hanging in others, ultimately
resulting in cable failure. Despite engineering methods that can partially mitigate seafloorroughness, it is essential to consider the actual seafloor conditions during the cable-laying
process.
The laying of undersea communication cables spans nearly all of the Earth's oceans.
The establishment of this global communication infrastructure is becoming more commerciallycompetitive, where technical considerations like the speed and precision of cable laying canyield substantial commercial advantages. In this environment, the viability of high-speed cablelaying is crucial, as is a comprehension of the temporary dynamics involved in the cable laying
process when the cable ship adjusts its speed. Forecasting the transient behavior of the cableduring installation necessitates a nonlinear time-domain analysis. Once this analysis isconducted, its verification presents a challenging problem.
Effective cable laying operations demand the prevention of elevated dynamic tensions,as they can lead to adverse consequences. While numerous researchers have addressed the staticand dynamic facets of the cable laying process, only a limited number have delved into
comprehensive cable dynamics due to challenges associated with the interaction between thecable and the ship in waves.
Hence, to initiate the subsea cable installation, Ifactors company must ascertain the
optimal quantity of cable necessary for the successful completion of the laying process inchallenging seabed conditions. This determination is contingent on establishing the suitablehydraulic brake speed and manually regulating the residual tension of the cable. The objectiveof this study is to develop a mathematical model and conduct a numerical simulation capableof offering a reliable prediction for the transient behaviour of cables laid in non-flat seabedenvironments. This includes considering the impact of wave excitation, cable/ship interaction,and cable elasticity.
In the initial phases, the study employs a steady-state model across different water
depths to replicate the seabed shape and wave heights. Following this, the research advances todevelop a dynamic model designed to enable real-time control of the cable laying operation inchallenging sea conditions. The dynamic model aims to address the impact of wave excitation,
cable/ship interaction, irregular shape of seafloor and cable elasticity.

17

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

1.2 Problem Statement
The process of subsea cable installation faces challenges, such as the occurrence of false slack,
where irregularities in the rough seafloor can lead to cable failure. Submarine cables, typically
deployed from laying vessels, are subject to various forces that necessitate meticulous cable
design. The conventional approach assumes a flat seafloor; however, the actual seabed is often
marked by roughness. This fact underscores the importance of considering the real conditionsof the seafloor during cable laying. Another primary problem in optimizing cable laying
operations is the prevention of heightened dynamic tensions, which can lead to serious adverse
consequences. This issue becomes more pronounced in adverse weather conditions.
High-speed cable laying requires a comprehensive understanding of the dynamicchanges that occur when adjusting the speed of the cable-laying ship. Accurately predicting thecable's transient behavior under these conditions involves complex nonlinear time-domain
analysis, posing significant challenges in verification. To tackle these issues, incorporatingtransient modeling phase-by-phase is crucial, particularly focusing on dynamic loading
conditions and the movements of the laying vessel. Despite the extensive exploration of staticand dynamic aspects of cable laying in many research studies, there exists a noticeable research
gap in the in-depth study of the intricate interactions between the cable and ship, particularly inwave-influenced environments. This gap is a major challenge to developing precise and reliable
models for subsea cable installation, crucial for real-world operations.

1.3 Methodology
Our methodology comprises three primary phases: The initial phase involves modelling cablelaying on a rough seafloor without considering the wave effect, and this model operates in a
steady state. The second phase focuses on developing a transient cable laying model thatincorporates the wave effect but assumes a flat seabed. The third phase involves the integrationof both models.

1.3.1

First phase: Modelling cable laying in irregular seafloor

The increasing use of cables in deep-ocean engineering highlights the significance ofdetermining cable configurations as crucial parameters in the design process. This study
focuses on creating a model for the cable laying process from a vessel, employing a steadystate model depicted in Figure 1. This model, extensively discussed by Howison (2005),
utilizes the Euler–Bernoulli model for the displacement of a slender nearly straight beam.
18

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

The primary goal is to ascertain the cable's shape from the point it passes through a'tensioner' at the vessel to the moment it reaches the seabed. The cable experiences an initial
tension To applied by the vessel and buoyancy forces as it descends to the seabed. The arclength (s) is defined as 0 at the vessel and –L at the seabed, where L represents the cable's
actual length between the two points. The sea depth is denoted as h(s), and θ(s) represents
the angle between the curve and the x-axis.
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑= 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝜃𝜃𝜃𝜃 ,𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝜃𝜃𝜃𝜃
= 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃 ,= 𝜅𝜅𝜅𝜅,𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(𝑤𝑤𝑤𝑤𝑠𝑠𝑠𝑠𝑤𝑤𝑤𝑤ℎ 𝜅𝜅𝜅𝜅 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑤𝑤𝑤𝑤ℎ𝑒𝑒𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑤𝑤𝑤𝑤𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒𝑒𝑒)(1)Take into account a small segment of length δs on the beam, as depicted in Figure 2. The
forces exerted on the element's boundaries consist solely of the internal elastic forces at its
ends, along with a body force characterized by components fx and fy per unit length. In astate of equilibrium, the forces' disparity must be nullified. Consequently, the equilibrium
equations can be expressed as:
𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦
𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑥𝑥𝑥𝑥= 𝑓𝑓𝑓𝑓𝑥𝑥𝑥𝑥 ,= 𝑓𝑓𝑓𝑓𝑦𝑦𝑦𝑦 ,𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑− 𝐹𝐹𝐹𝐹𝑥𝑥𝑥𝑥 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃 + 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝜃𝜃𝜃𝜃 = 0
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(2)

The body force fx and fy can be expressed through terms of a drag force, fd and a buoyancy
force, fb, represented by the following equations:𝑓𝑓𝑓𝑓𝑑𝑑𝑑𝑑 = 𝐶𝐶𝐶𝐶𝑑𝑑𝑑𝑑 𝐴𝐴𝐴𝐴𝜌𝜌𝜌𝜌𝑤𝑤𝑤𝑤 𝑈𝑈𝑈𝑈 2
𝑓𝑓𝑓𝑓𝑏𝑏𝑏𝑏 = ∆𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝐴𝐴𝐴𝐴

where
ρw is sea water density.
U represents the velocity of the object in relation to the fluid,
A denotes the cross-sectional area of the cable,
Cd is the drag coefficient, andg stands for gravitational acceleration.
Finally, by taking into account the constitutive equation given as

19

(3)

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝜃𝜃𝜃𝜃
= 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(4)

with EI represents the cable bending stiffness, the governing equation for this particular
problem can be written as
𝑑𝑑𝑑𝑑 2 𝜃𝜃𝜃𝜃
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 2 − 𝐹𝐹𝐹𝐹𝑥𝑥𝑥𝑥 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃 + 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝜃𝜃𝜃𝜃 = 0
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(5a)

𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑥𝑥𝑥𝑥= 𝐶𝐶𝐶𝐶𝑑𝑑𝑑𝑑 𝐴𝐴𝐴𝐴𝜌𝜌𝜌𝜌𝑤𝑤𝑤𝑤 𝑈𝑈𝑈𝑈 2 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(5b)

𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦= ∆𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝐴𝐴𝐴𝐴
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(5c)

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑=𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝜃𝜃𝜃𝜃
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(5d)

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑= 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(5e)Here, the unknown parameters are θ, Fx, Fy, X and Y, and also there is one second-orderequation. Therefore, the required boundary conditions are

𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑑𝑑𝑑𝑑 = 0; 𝜃𝜃𝜃𝜃 = 𝜃𝜃𝜃𝜃𝑜𝑜𝑜𝑜 , 𝑑𝑑𝑑𝑑 = 1, 𝑑𝑑𝑑𝑑 = 0 𝑇𝑇𝑇𝑇 = 𝑇𝑇𝑇𝑇𝑜𝑜𝑜𝑜 , 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑑𝑑𝑑𝑑 = −1; 𝜃𝜃𝜃𝜃 = 0,

Figure 1: Layout of the cable20

𝑑𝑑𝑑𝑑𝜃𝜃𝜃𝜃
= 0, 𝑑𝑑𝑑𝑑 = 0
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(6)

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

Figure 2: Forces and moments on an element of a beam (Howison, 2005)

Before directly addressing the governing equation, a crucial step involves transforming thesystem of equations into a scaled or non-dimensionalized format. This allows the groupingof all engineering parameters, facilitating the simplification of any necessary assumptions
beforehand. For example, consider Equation (5c), having a scale of 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦 ~𝑓𝑓𝑓𝑓𝑜𝑜𝑜𝑜 𝐹𝐹𝐹𝐹�𝑦𝑦𝑦𝑦 and 𝑑𝑑𝑑𝑑~𝑑𝑑𝑑𝑑𝑜𝑜𝑜𝑜 𝑑𝑑𝑑𝑑̂
(where 𝐹𝐹𝐹𝐹�𝑦𝑦𝑦𝑦 and 𝑑𝑑𝑑𝑑̂ are the non-dimensional variables, while fo and so are the scaling factor) sothat:

𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦
𝑓𝑓𝑓𝑓𝑜𝑜𝑜𝑜 𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹�𝑦𝑦𝑦𝑦
1 𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹�𝑦𝑦𝑦𝑦 ∆𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝐴𝐴𝐴𝐴= ∆𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝐴𝐴𝐴𝐴= ∆𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝐴𝐴𝐴𝐴
=
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝑜𝑜𝑜𝑜 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑̂
𝑑𝑑𝑑𝑑𝑜𝑜𝑜𝑜 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑̂
𝑓𝑓𝑓𝑓𝑜𝑜𝑜𝑜(7)

If fo is taken as ∆gA ℎ(𝑑𝑑𝑑𝑑) and so as L ℎ(𝑑𝑑𝑑𝑑), the equation becomes:
1 𝑑𝑑𝑑𝑑𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦=1
𝐿𝐿𝐿𝐿 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(for simplicity, the upper mark ( � ) for dimensionless variable is ignored).

(8)

Therefore, this equation, in fact all Equations (5a–e), can be converted into dimensionlessequations by scaling the force variables (Fx and Fy) by ∆gAh, the space variables (X and Y)
by h and the arc length (s) by Lh. The final non-dimensional governing equation can be
written as:

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1ℎ(𝑑𝑑𝑑𝑑)3 𝛽𝛽𝛽𝛽𝜃𝜃𝜃𝜃,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 − 𝐹𝐹𝐹𝐹𝑥𝑥𝑥𝑥 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃 + 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝜃𝜃𝜃𝜃 = 0
𝐿𝐿𝐿𝐿2
1
𝐹𝐹𝐹𝐹 = 𝛼𝛼𝛼𝛼 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃
𝐿𝐿𝐿𝐿 𝑥𝑥𝑥𝑥 ,𝑠𝑠𝑠𝑠
1
𝐹𝐹𝐹𝐹 = 1
𝐿𝐿𝐿𝐿 𝑦𝑦𝑦𝑦 ,𝑠𝑠𝑠𝑠(9)

1
𝑑𝑑𝑑𝑑 =𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝜃𝜃𝜃𝜃
𝐿𝐿𝐿𝐿 ,𝑠𝑠𝑠𝑠
in which α and β are coefficients of:𝛼𝛼𝛼𝛼 =

1
𝑑𝑑𝑑𝑑 = 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃
𝐿𝐿𝐿𝐿 ,𝑠𝑠𝑠𝑠

𝐶𝐶𝐶𝐶𝑑𝑑𝑑𝑑 𝐴𝐴𝐴𝐴𝜌𝜌𝜌𝜌𝑤𝑤𝑤𝑤 𝑈𝑈𝑈𝑈 2(ℎ𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑦𝑦𝑦𝑦𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐)∆𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝐴𝐴𝐴𝐴

𝛽𝛽𝛽𝛽 =

𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐(10)

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸(𝑏𝑏𝑏𝑏𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑠𝑠𝑠𝑠𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒)∆𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝐴𝐴𝐴𝐴(a subscript variable with a comma in front ( ,s) means a derivative with respect to thevariable).
Now, the dimensionless boundary conditions become:
𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑑𝑑𝑑𝑑 = 0; 𝜃𝜃𝜃𝜃 = 𝜃𝜃𝜃𝜃𝑜𝑜𝑜𝑜 , 𝑑𝑑𝑑𝑑 = 1, 𝑑𝑑𝑑𝑑 = 0 𝑇𝑇𝑇𝑇 = 𝑇𝑇𝑇𝑇𝑜𝑜𝑜𝑜 ,𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑑𝑑𝑑𝑑 = −1; 𝜃𝜃𝜃𝜃 = 0,

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠= 0, 𝑑𝑑𝑑𝑑 = 0(11)

The governing Equation (9–11) is straightforward and can be solved via a numericalapproach except that the second order term makes the solution rather complicated. If one
considers an extra equation of:𝛾𝛾𝛾𝛾 =

1 𝑑𝑑𝑑𝑑𝜃𝜃𝜃𝜃⇒ 𝜃𝜃𝜃𝜃,𝑠𝑠𝑠𝑠 = 𝐿𝐿𝐿𝐿𝛾𝛾𝛾𝛾,
𝐿𝐿𝐿𝐿 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

so that the governing equation becomes:

22

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𝛾𝛾𝛾𝛾,𝑠𝑠𝑠𝑠 =𝐿𝐿𝐿𝐿2
�−𝐹𝐹𝐹𝐹𝑥𝑥𝑥𝑥 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃 + 𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑑𝑑𝑑𝑑 𝜃𝜃𝜃𝜃 �𝛽𝛽𝛽𝛽ℎ(𝑑𝑑𝑑𝑑)3
𝐹𝐹𝐹𝐹𝑥𝑥𝑥𝑥 ,𝑠𝑠𝑠𝑠 = 𝐿𝐿𝐿𝐿𝛼𝛼𝛼𝛼 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝜃𝜃

(13)

𝐹𝐹𝐹𝐹𝑦𝑦𝑦𝑦 ,𝑠𝑠𝑠𝑠 = 𝐿𝐿𝐿𝐿

𝑑𝑑𝑑𝑑,𝑠𝑠𝑠𝑠 = 𝐿𝐿𝐿𝐿𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝜃𝜃𝜃𝜃𝑑𝑑𝑑𝑑,𝑠𝑠𝑠𝑠 = 𝐿𝐿𝐿𝐿𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝜃𝜃𝜃𝜃This set of equations was solved numerically using MATLAB software as shown in
APPENDIX A. The results of the solution are discussed later in Section 1.4.

1.3.2.
seabedSecond phase: Transient model of cable laying under wave effect on flat

It is noted that the cable laying problem demands an analysis beyond the steady-state model
as it involves many variables that often makes the real operation of cable laying
unpredictable. For such concern, wave motion effect should be introduced, which leads toa variable sea depth depending on time, h(t).

Figure 3:Time-dependent problem for cable laying (source: Prpić-Oršić and Nabergoj,
2005)
As the contact points (one at the sea bed and another is at the tensioner of the vessel) changes
due to the wave motion, most of the variables are also depending on time, for example X(t),Y(t) and L(t). Moreover, additional variables of boat speed (U) and the cable motor speed(V) need to be considered.
In this phase we will follow a model developed by Prpić-Oršić and Nabergoj (2005). Figure
4 illustrated the three Cartesian reference systems: OXY (an inertial system fixed to the23MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

Earth), oxy (equilibrium axes moving with the mean velocity of the cable-laying ship), ando′x′y′ (a local system attached to the cable). The transformation rule between these systems
can be expressed as:
(14)

and

(15)
Here, t and n represent the tangent and normal unit vectors in the local system. The unit
vectors i, j, and I, J correspond to the moving and inertial systems, respectively. The angle
θ(p , t) between the cable element and the horizontal axis is a function of time t and thestretched arc length along the cable p. This length, measured from the shipboard connection
point, comprises the unstretched distance s and the elastic elongation. The relationshipbetween the stretched and unstretched arc lengths is given by dp=ds(1+ε), where ε is theuniaxial strain in the cable. Relations (14) and (15) permit expressing any point in any of
the three coordinate systems.

Figure 4: coordinate systems (source: Prpić-Oršić and Nabergoj, 2005)
From Figure 4, the following geometric compatibility relations are obtained (Prpić-Oršić
and Nabergoj 2005):

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MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia(16)

Now the summing of the forces parallel to the tangential axes results in(17)
and in the direction of the normal axes,(18)
The details of derivations and definitions of parameters in dynamic equilibrium condition,
Eqs. (17) and (18) can be found in (Prpić-Oršić and Nabergoj 2005).
Note that, the work done by Prpić-Oršić is actually an extension to the earlier works by Vaz,
M. A., & Patel, M. H. (1995) and Patel, M. H., & Vaz, M. A. (1995), considering a towed
cable and a laid cable, respectively, with the assumptions of continuous, inextensible andflexible (negligible bending stiffness) conditions. These works will be the progressing stepsof our modelling effort to complete the final nonlinear dynamics model by Prpić-Oršić andNabergoj (2005) to include the complete behaviour of stretched cable being laid, accounting
for the effects of transient, wave excitation, cable/ship interaction and cable elasticity.
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1.3.3.
seabedThird phase: Transient model of cable laying under wave effect on roughIn this phase Equations (9) and (10) will be combined with equations (17), (16) and (18)where the depth h(s, t) will be a function of time t and the stretched arc length s. Possibility
to include the numerical model developed by Trapper, P. A. (2022) will also be considered.
Trapper proposed a simple numerical technique, particularly adopting finite difference

discretization, for calculating the internal force envelope diagram as shown in Figure 5. The
capability of the proposed model to consider several scenarios of a pipe-lay across different
types of seafloor irregularity can be utilised.

Figure 5: Numerical model of pipeline by finite difference approach (source: Trapper, P.
A., 2022)

1.4 Results and discussions
1.4.1

First phase results

Numerical solution for Equation (12–13) gives θ, Fx, Fy, X, Y and also L. For calculation
purposes, the two parameters α and β were given a presumed value (without comparing toany engineering parameters) so as to ease the analysis. The main objective is to get thegeneral idea of the cable behaviour as the defining parameters change.
Figure 6 plots the shape of laying cables in a 2D plane with different water depth, startingfrom the point (0,1) as the cable passes through the tensioner of a vessel, to the point (-X,0)
as the cable touches the sea bed. In this case, both α and β are taken as 1, and the initial
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angle between the cable and the horizontal surface (θo) is 1 radian. The value of the water
depth h(s) is set to vary from 0 to 1 in order to observe the effect on the cable shape. The
depth is calculated from seafloor where ℎ(𝑑𝑑𝑑𝑑)=0 to sea surface. It can be seen that increasing
the depth reduced the amount of cable being laid, indicating higher elevation in the seafloor
in that particular area.

h(s)Figure 6: Shape of cables in different water depths

1.4.2

Second phase results

At present, the first model, as outlined in Vaz, M. A., & Patel, M. H. (1995), is underdevelopment, as detailed in APPENDIX B. This model treats a towed cable as continuous,
inextensible, and flexible, without considering bending stiffness.
Basically, the implemented algorithm in Vaz’s model involves dividing the total cable
length into n straight elements, connected by pin-nodes, to approximate the solution to thegoverning equation. This process transforms the integral operations in the cableconfiguration equations into summations over n terms, leading to a set of nonlinear ordinary
differential equations that require iterative solutions. This method simplifies the problem
by converting the original partial differential equations into a connected series of ordinary
differential equations. Using a large number of elements effectively transforms the model
into a chain assembly.

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While the final running codes provided in APPENDIX B still require improvements andupdates, the current progress of the codes has successfully plotted the initial configurationof the cable (before acceleration), as shown in Figure 7. Next, the iterative part of thenonlinear analysis will be updated, and validation will be conducted using the results
available in Vaz, M. A., & Patel, M. H. (1995). This will be followed by an update on themodel from Patel, M. H., & Vaz, M. A. (1995), which considers a cable being laid. Finally,
the model by Prpić-Oršić and Nabergoj (2005), which takes into account the stretchingeffect of the cable, will be incorporated.

Figure 7: Initial cable configuration by Vaz’s model

1.4.3

Third phase results

As highlighted earlier, this phase will involve developing a transient model of cable laying
under the coupling influence of wave effects and a rough seabed. As of now, this phasehas not been conducted but is planned for future works.

1.5 Conclusion and Recommendations
A mathematical model for cable laying was created, incorporating the impact of a non-flat
seafloor and wave motion. The derivation of the model unfolds in three phases. The first28

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
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phase involves a steady-state model of a rough seafloor, neglecting wave effects. The
formulation is based on the Euler–Bernoulli equation derived by Sam Howison, predicting
the cable layout at a specific time with known initial prescribed tension, cable properties,
and fluid behaviour. To simplify the formulation, a non-dimensional set of equations isderived by scaling all engineering parameters, resulting in two defining parameters related
to fluid hydrodynamic drag and cable bending stiffness. The model is applied with varying
sea depths, and numerical solutions are obtained using MATLAB. Results reveal a higher
cable location with increased depth. In the second phase, the model addresses a timedependent problem, incorporating significant wave motion while assuming a flat seafloor.
This entails a highly nonlinear formulation requiring resolution. In the third phase, bothmodels are integrated, introducing a time-dependent water depth h as a function of time t
and arc length s.

1.6 Acknowledgements
This work was supported by the Institute of Mathematics for Industry, Joint
Usage/Research Center in Kyushu University. (FY2023 Workshop (I) “MMISG2023”(2023b003).)

REFERENCES
Howison, S. (2005). Practical Applied Mathematics: Modelling, Analysis, Approximation.
Cambridge University Press.
Prpić-Oršić, J., & Nabergoj, R. (2005). Nonlinear dynamics of an elastic cable during laying
operations in rough sea. Applied Ocean Research, 27(6), 255-264.
Vaz, M. A., & Patel, M. H. (1995). Transient behaviour of towed marine cables in two
dimensions. Applied ocean research, 17(3), 143-153.
Patel, M. H., & Vaz, M. A. (1995). The transient behaviour of marine cables being laid—
the two-dimensional problem. Applied Ocean Research, 17(4), 245-258.
Trapper, P. A. (2022). A numerical model for geometrically nonlinear analysis of a pipelay on a rough seafloor. Ocean Engineering, 252, 111146.

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APPENDIX A
function [l,Xn] = cable2a()% Full cable solution% 8 April 2015
fn = 'cableIFactor';% PARAMETERS
alf = 1;% hydrodynamic dragbet = 0.01;% bending stiffness
T0 = 1;% tension
kap = -0;% curvature at boat
thetastar = 1; % angle at boat (alternative bc)
h = 1;% water depth% INITIAL GUESS FOR SOLUTION
x_guess = linspace(0,1,100);
y_guess = [0; 0; 0; 0; 0; 0];l_guess = 1;
solinit = bvpinit(x_guess,y_guess,l_guess);solinit.y(1,:) = atan(solinit.x+1); % approximate solution
solinit.y(2,:) = (1+(solinit.x+1).^2).^(-1);
sol = solinit;% SOLVE
options=bvpset('Vectorized','on','FJacobian',@fjac2,'NMax',10000);
sol = bvp4c(@geq2,@gbc2,sol,options);
l = sol.parameters;s = sol.parameters*sol.x;
theta = sol.y(1,:);
Fx = sol.y(3,:);
Fy = sol.y(4,:);
X = sol.y(5,:);Y = sol.y(6,:);% PLOT
figure(1); clf;
plot(s,theta,s,Fx,s,Fy);plot(s,theta);
xlabel('x'); ylabel('y');l0 = (2*T0-1).^(1/2);
hold on; plot(s,atan((s-s(1))./(T0.^2-l0.^2).^(1/2)),'k--');% asymptotic solution for alpha = 0
figure(2); clf;
plot(X,Y);
save(fn);% PLOT
figure(2); clf;
set(gcf,'Paperpositionmode','auto','units','centimeters','position',[2 2 20 10]);plot(sol.y(5,:),sol.y(6,:),'linewidth',2);xlabel('x'); ylabel('y');ylim([0 1]);shg;
% print(gcf,'-depsc2',fn,'-loose');30

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia%% SUBFUNCTIONS %%% BOUNDARY CONDITIONS
function res = gbc2(ya,yb,l)res(1,:) = ya(1,:);
res(2,:) = ya(2,:);%
res(3,:) = yb(2,:)-kap;
res(3,:) = yb(1,:)-thetastar; % ( alternative bc )%res(4,:) = yb(3,:).*cos(yb(1,:))+yb(4,:).*sin(yb(1,:))-T0;
res(4,:) = yb(3,:)-T0; % (alternative bc )res(5,:) = ya(6,:);res(6,:) = yb(6,:)-h;res(7,:) = yb(5,:);end% JACOBIAN OF ODE SYSTEM
function [dfdy,dfdp] = fjac2(x,y,l)theta = y(1,:);
psi = y(2,:);
Fx = y(3,:);
Fy = y(4,:);
X = y(5,:);Y = y(6,:);end

dfdy = [0 l 0 0 0 0;
l./bet.*( Fx.*cos(theta) + Fy.*sin(theta) ) 0 l./bet.*( sin(theta) ) l./bet.*( -cos(theta) ) 0 0;l.*alf.*cos(theta) 0 0 0 0 0 ;0 0 0 0 0 0 ;-l.*sin(theta) 0 0 0 0 0 ;l.*cos(theta) 0 0 0 0 0];
dfdp = [psi;1/bet.*( Fx.*sin(theta) - Fy.*cos(theta) );alf*sin(theta);
1;
cos(theta);sin(theta)];% ODE SYSTEM
function dydx = geq2(x,y,l)theta = y(1,:);
psi = y(2,:);
Fx = y(3,:);
Fy = y(4,:);
X = y(5,:);Y = y(6,:);enddydx(1,:) = l.*psi;dydx(2,:) = l./bet.*( Fx.*sin(theta) - Fy.*cos(theta) );dydx(3,:) = l.*alf*sin(theta);
dydx(4,:) = l.*1;
dydx(5,:) = l.*cos(theta);
dydx(6,:) = l.*sin(theta);

end

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APPENDIX B% THIS PROGRAM IS STILL UNDER DEVELOPMENT AND PROGRESSING.
% VALIDATION OF THE RESULTS IS YET TO BE MADE IN COMPARISON% WITH THE RESPECTIVE JOURNAL ARTICLES (VAZ, PATEL, 1995)% DATE: 15/12/2023
clear, clc% USER INPUT
g = 9.81;
V_0_x = 0.463; % initial ship speed
V_0_y = 0;
diameter = 0.0264;
L_total = 360;n = 3;
rho = 1;
CdUser = 2.054;
CfUser = 0.01;
cable_weigth_per_length = 10.59;
hydrodynamic_constant = 0.6173; % Hopland at LH% ALLOCATE VARIABLES
N = zeros(n, 1);
theta_0 = zeros(n,1);
theta = zeros(n,1);
theta_dt = zeros(n,1);
theta_dtdt = zeros(n,1);
T_ds = zeros(n, 1);
T_0 = zeros(n, 1);
H = zeros(n, 1);
H_0 = zeros(n, 1);
V_0 = zeros(n, 1);
C_f = zeros(n, 1);
C_d = zeros(n, 1);
vt = zeros(n, 1); % normal midpoint velocities
vn = zeros(n, 1); % tangential midpoint velocities
w = zeros(n, 1); % cable’s weight in sea water per unit length
rho_bar = zeros(n, 1);
L = ones(n, 1)*L_total/n;d = ones(n, 1)*diameter;% Coordinatesx = zeros(n, 1);
y = zeros(n, 1);% Apply local properties
C_f = C_f + CfUser;
C_d = C_d + CdUser;w = w + cable_weigth_per_length;% Initial conditions
V_0 = V_0 + V_0_x;
x_dt_0 = -V_0_x;% [Eq2a]% theta Eq(13a)for i = 1:ntheta_0(i) = acos( ...
sqrt( 1 + 1/4 * ( hydrodynamic_constant / V_0(i) )^4 ) - 1/2 * (hydrodynamic_constant / V_0(i) )^2)
endtheta = theta_0;32

MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
20-22 November, 2023, UTM KUALA LUMPUR, Malaysia% theta_dt Eq(13b)for i = 1:ntheta_dt_0(i) = 0;end
% vt Eq(10a)for i = 1:n
sumTerm = 0;
for j = 1:n-1
sumTerm = sumTerm + theta_dt(j) * L(j) * sin( theta(i) - theta(j) );end
vt(i) = - x_dt_0 * cos( theta(i) ) + sumTerm;end% vn Eq(10b)for i = 1:n
sumTerm = 0;
for j = 1:n-1
sumTerm = sumTerm + theta_dt(j) * L(j) * cos( theta(i) - theta(j) ) ...
+ theta_dt(i) * L(i) / 2;endvn(i) = x_dt_0 * sin( theta(i) ) + sumTermend% x0_dtdt (assumed)
x0_dtdt = 0;
% TIME MARCHING START HERE
% Eq(11a)for i = 1:n
H(i) = - 1/2 * rho * C_f(i) * pi * d(i) * vt(i)^2 - w(i) * sin( theta(i) );T_ds(i) = H(i);end% Guess value of N
for i = 2:nN(i-1) = T_0(i) * sin( theta(i) - theta(i-1) ) ...
- N(i) * cos( theta(i) - theta(i-1) )end
disp('try!!')% Eq(12)for i = 1:n
sumTerm = 0;
for j = 1:i-1
sumTerm = sumTerm + L(j) * ( ...
theta_dt(j)^2 * sin( theta(i) - theta(j) ) ...
+ theta_dtdt(j) * cos( theta(i) - theta(j) ));endtheta_dtdt(i) = ...
2 / L(i) * ( ...
- 1 / ( 2 * rho_bar (i) ) * rho * C_d(i) * d(i) * vn(i) * abs ( vn(i) ) ...
+ w(i) / rho_bar(i) * cos( theta(i) ) ...
- x0_dtdt * sin( theta(i) ) ...
+ 2 * N(i) / rho_bar(i) / L(i) ...
- sumTerm);end% Calculate x & y% assume initial location

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x(1) = 0;y(1) = 0;
for i = 2:nx(i) = x(i-1) - L(i)*sin(theta(i-1));y(i) = y(i-1) - L(i)*cos(theta(i-1));end
x,yplot(x,y,'r.-')34

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3D Shape Reconstruction of Charcoal Chamber Monument

Industry Representatives:

Rasanubari Asmah Rahmah Abd Hamid14(Langkawi Development Authority (LADA))

Study Group Contributors:

Taufiq Khairi Ahmad Khairuddin1, Zulkepli Majid1,
Mohd Ali Khameini Ahmad1, Abd Manan Samad3,
Ismail Ma’arof3, Shota Shigetomi9

1.1 Introduction
Langkawi Island boasts a distinctive tourism asset known as the Charcoal Chamber
Monument in Kubang Badak, Bukit Menora. This historical site has a rich legacy dating backmore than 150 years and was utilized for the production of coal, primarily from mangrove wood.
According to historians, there were originally 12 charcoal chamber monuments in existence [1].
However, as time has passed, only three of these structures have endured, and they, too, have
weathered the years, with only partial sections of their original construction remaining.
In light of this historical significance, it falls upon the Langkawi Development Authority(LADA) to undertake the responsibility of preserving these relics. They serve as tangible
evidence of the presence of a Siamese village on Langkawi Island, dating back to approximately
1841. By safeguarding and conserving these charcoal chambers, LADA not only ensures thepreservation of this invaluable historical heritage but also contributes significantly to thetourism potential of Langkawi. These chambers stand as a testament to the island’s rich pastand have the potential to be a compelling attraction for visitors seeking a deeper connection to
Langkawi’s history and culture.

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Figure 1. One of the remaining charcoal chamber monument (Left) in Kubang Badak,
Bukit Menora versus the complete replica of the charcoal chamber monument (Right)
found in Sungai Petani, Kedah.
Preserving the historic Charcoal Chamber Monument necessitates a comprehensive
approach that begins with accurately documenting its dimensions and shape. To achieve this,the use of advanced techniques, such as the creation of a three-dimensional model and theapplication of mathematical model, has been proposed. Creating a three-dimensional model
involves using modern technology, such as laser scanning or photogrammetry, to capture
precise measurements and detailed images of the monument from various angles. By generatinga digital 3D representation, preservationists and historians can gain a better understanding of
the monument’s structure, which serves as a vital reference for restoration and conservation
efforts. The application of mathematical model is a scientific and analytical approach that can
aid in reconstructing the monument’s original form and dimensions. This model can provide
insights into the monument’s historical proportions and shape, helping experts determine how
it might have appeared when first built.

1.2 Charcoal Chamber Shape Determination
In this study, the complete structure of a charcoal chamber monument will be
determined based on two methods. The first method is to geometrically model the remaining
charcoal chamber using laser scanning. After that, the complete structure of the charcoal
chamber will be developed based on suitable mathematical models.

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1.2.1 Geometric Model
Laser Scanning technique in geoinformation was previously used to collect the data (size,
dimension, points or locations, colours etc) of the remaining charcoal chamber monument,
located in Kubang Badak, Bukit Menora, Langkawi. The data was analysed to reconstruct andgeometrically model the incomplete chamber in the three-dimensional space. The complete
procedures to achieve this were actually explained in [1]. The left-hand side of Figure 2 shows
one of the surveying stations during the scanning. Specifically, 4 stations have been used toscan the chamber (see the right-hand side of Figure 2). Next, the left-hand side of Figure 3
presents the set of images after that scanning, that has been processed into the complete modelof the remaining charcoal chamber (see the right-hand side of Figure 3). In this study, further
analysis on this geometric model will be carried out, in order to determine the complete structureof the charcoal chamber.

Figure 2. The terrestrial laser scanning is used to collect the data (size and dimension)and capture the 3D image of one of the remaining charcoal chamber monuments.

1.2.2 Mathematical Modelling
Based on the 3D geometric model of the partial charcoal chamber, mathematical modelling will
be used to construct the complete 3D model of the charcoal chamber. Moreover, some methods
in geometric modelling will be considered when constructing the 3D model based on the related
mathematical formulation. These include some concepts in multivariable functions such as thecommon surfaces, level curves and intersection between surfaces.

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Figure 3. The set of images on the left are processed to produce the 3D image of thepartial charcoal chamber monuments.

1.3 Results and Discussion
During the study group, geoinformation software called as CloudCompare was used tofurther visualize 3D model of the incomplete structure of the charcoal chamber. Based on thevisualization, it was assumed that the remaining chamber was an open cylinder with circularbase. In Figure 4, it can be seen that the partial or incomplete structure of the charcoal chamber
has been further modelled as a circular cylinder.

Figure 4. Further process and analysis of the partial charcoal chamber monument54

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Next, using the assumption in Figure 4 and based on historical information (such as in[1]), the complete structure of the chamber will be developed according to suitable
mathematical formulations. In this case, it is further assumed that the complete charcoal
chamber is built by the combination of a circular cylindrical body and also a dome, as suggested
in the real replica of the chamber in Figure 1. Thus, multivariable functions of two variables
have been used to construct the solid.
By following the technique presented in [3], a solid bounded by a cylinder has been
firstly constructed. After that, another solid representing the dome is also constructed andattached to the top of the cylinder. However, 3 different solids have been suggested during thestudy, where the domes are all different, due to the lack of information about the dome.
Nevertheless, each dome must be completely attached to the cylinder. Therefore, the 3 domes
each is respectively constructed as a circular paraboloid, a hemisphere and a circular catenary(see Figure 5).

Figure 5. The 3 suggested complete shape of the charcoal chamber monument that has
circular shape as the base and the combination of the cylinder with the dome (paraboloid(left), hemisphere (center) and catenary (right)) as the body55

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1.4 Conclusion
The 3D complete shape of a charcoal chamber monument has been constructed based on thegeoinformation technique and mathematical modelling. Specifically, three different models of
the complete charcoal chamber have been suggested, where each model is built by the
combination of a cylinder with different domes. In the future, the proposed models need to befurther investigated to decide the actual shape of the original complete charcoal chamber.
1.5 Acknowledgment
This work was supported by Institute of Mathematics for Industry, Joint Usage/Research Centerin Kyushu University. (FY2023 Workshop (I) “MMISG2023” (2023b003).)
REFERENCES[1] Chen-Kim Lim, Kian-Lam Tan and Minhaz Farid Ahmed. Conservation of Culture

Heritage Tourism: A Case Study in Langkawi Kubang Badak Remnant Charcoal Kilns,
Sustainability 2023, 15, 6554.

[2] Zulkepli Majid, Mohd Farid Mohd Ariff, Syamsul Hendra Mahmud, Gobi Krishna AL

Sinniah, Anuar Aspuri, Azman Ariffin, Abdul Jalil Maulani and Nailul Insani. The Digital
Documentations of Historical Charcoal Chamber Monument Using LiDAR Technology,
Proceeding 2023 IEEE 14th Control and System Graduate Research Colloquium(ICSGRC), 5 Aug 2023, Shah Alam, Selangor, Malaysia, 163-168.

[3] Maslan Osman dan Yusuf Yaacob (2008) Multivariable and Vector Calculus, Penerbit

UTM Press, Johor Bahru, Malaysia.

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Predictive Modeling of Pipe Burst Towards Sustainable Non-Revenue Water

Industry Representatives:

Aida Natasah Sulaiman11, Nur Nabilah Jamal11,
Norazlina Ismail11(Ranhill SAJ Sdn. Bhd.)

Study Group Contributors:

Norhaiza Ahmad1, Zainol Mustafa2, Arifah Bahar1,
Zaiton Mat Isa1, Istas Fahrurazi Nursyirwan1, Mohamad
S.J. Darwish1, Tan Lit Ken1, Hafizah Farhah Saipan @Saipol1, Aniza Akaram1, Ramhya Kathirayson2

1.1 Introduction
This report provides valuable insights into the evaluation of predictive modeling for pipe burst
detection, aiming to address concerns, assess feasibility, and ensure a successful and reliableinstallation process. The objective is to implement a robust system that leverages on statistical
approach and Artificial Neural Network Model (ANN). Concerns and constraints revolvearound ensuring the accuracy of predictions, minimizing false positives, and optimizing the
integration of these methodologies into existing infrastructure.
In the statistical approach, the study starts by focusing on analyzing historical data,
scrutinizing the quality and patterns of the available data. The main concern lies around the
data's reliability by investigating potential outliers and assessing whether the dataset, confined
to a single year, is sufficient for meaningful predictive modelling. Investigation into utilizing
different types of control charts specifically variations of Exponentially Weighted Moving
Average Control Charts that can detect shifts in signals is considered. The study concludes thata combination of dynamic thresholding based on weekly patterns, targeted analysis of reliableflow data, and adaptive parameter settings in control charts, particularly with a focus on shortertime series, enhance the effectiveness of pipe burst detection. The adaptive parameter strategy
significantly reduces the percentage of points beyond limits in the control charts (0% to 9.1%),
emphasizing its effectiveness in identifying transient signals for pipe burst detection.
In ANN models, historical data serves as the foundation for training the network torecognize patterns indicative of bursts. These patterns are discerned through correlations
observed within the parameters encapsulated in the historical dataset. The LevenbergMarquardt algorithm, renowned for its efficacy in optimizing the neural network's parameters,
is frequently employed for this purpose. Notably, this algorithm is conveniently accessible as a
built-in function within platforms like Matlab, facilitating its seamless integration into theneural network training process. By leveraging the collective intelligence encoded in historical
data and employing sophisticated optimization techniques like the Levenberg-Marquardt
algorithm, ANN models can effectively discern and anticipate bursts based on underlying
patterns present in the data.
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1.2
Pipe Burst Detection With Control Chart Analysis For Water Monitoring
1.2.1 Introduction
Information on water distribution consists of measurements of water flow and water pressurerecorded at regular time intervals are typically used for water monitoring. In statistical analysis,
these data are presented in the form of continuous interval multivariate time series. In timeseries analysis, anomalies refer to observations or patterns in the data that deviate significantly
from the expected or normal behavior. These anomalies can be indicative of unusual events,
errors, or changes in the underlying processes being monitored. These deviations can manifest
as sudden spikes, drops, or irregularities, signaling potential events, errors, or shifts in theunderlying processes being observed.
Pipe burst events can be referred to as "anomalies" or "transients" in the time series data.
A transient, in this context, denotes a brief and temporary occurrence that takes place when
fluid, such as water, is rapidly and unexpectedly released due to a burst pipe. The burst initiates
an uncontrolled and swift flow of fluid, leading to a transient flow condition. This transientphase is marked by an abrupt surge in both pressure and flow rate, creating a sudden and intense
movement of fluid within the pipe system. In this study, we aim to detect the transient signals
based on the temporal consistency in the time series based on statistical approaches.
1.2.2 Objectives
2.2.1 to identify the transient signals of pipe burst using a statistical approach applied to water
reading measurements obtained at 15 minutes interval on a daily basis.
2.2.2 to determine an effective estimate for the control limits of statistical control charts formonitoring water distribution.
1.2.3 About Data
The company provides us with measurements from a logger device at a District Metered Area
(DMA) meter in Skudai, Johor. In the context of water distribution systems, these devices areused to monitor and measure water flow within a specific district or zone. This technology is
employed in water network management to enhance control, efficiency, and leak detection. The
measurements used in the analysis are Pressure Rate (mH), Flow Rate (l/s). Other
measurements are also available but they are not used since they are either aggregated or containtoo many missing data.
The pressure rate (mH), and flow Rate (l/s) data provided show some distinct characteristics.
(1) The recorded data could be affected by (1) the manner that the tap source is closed dueto the pipeburst, or (2) no data transfer on the recorded device due to technical issues.
These phenomena could result in spurious negative or zero values. The data needs to betreated or pre-processed prior to applying any analysis to avoid biased interpretation.
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20-22 November, 2023, UTM KUALA LUMPUR, Malaysia(2) The data is recorded at every 15 minute interval. This implies that the sequence data isin the form of a continuous interval time series data. Thus, the method used for analysis
needs to take into account this characteristic.

1.2.4 Methodology
1. 2.4.1 Data Pre-processing
The recorded data could be affected by: (1) the closure of the tap source due to a pipe burst and(2) the absence of data transfer on the recording device due to technical issues. Thesephenomena have the potential to introduce spurious negative or zero values. The company's
current protocol involves either retaining zero values or substituting (imputing) negative values
with zero. However, the persistence of zero values may give rise to computational challenges
and could potentially lead to misleading information, particularly when consecutive data points
exhibit zero values. Thus, the suggested course of action for addressing potential cases is toreplace the missing values with a minimal value to reflect no flow and or to replace the missing
values with the previous day’s reading.
1. 2.4.2 Exponentially Weighted Moving Average (EWMA) Based Control Charts
Here we have applied two types of control charts suitable for autocorrelated process: a
univariate Exponentially Weighted Moving Average (EWMA) control charts and its
multivariate extension, the Multivariate EWMA to obtain the upper and lower bounds for the
imputed Pressure and Flow dataset.
Similar to many control charts, EWMA type of charts operates on a threshold-based
methodology, simplifying the process of monitoring for deviations and facilitating ease of use
in quality control. The EWMA chart assigns exponentially decreasing weights to pastobservations, giving more emphasis to recent data (Sukparungsee et al., 2020).These charts are
particularly useful for detecting small shifts in the process parameter mean.
An EWMA control chart for monitoring the mean of a process is based on the following
statistic.
𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖 = 𝜆𝜆𝜆𝜆𝜆𝜆𝜆𝜆𝑖𝑖𝑖𝑖 + (1 − 𝜆𝜆𝜆𝜆)𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖𝑖𝑖 , 𝑖𝑖𝑖𝑖 = 1,2, ⋯.
(Eq.1)

where 𝜆𝜆𝜆𝜆 is the weighing parameter of the data with , 0 ≤ 𝜆𝜆𝜆𝜆<1, and 𝑋𝑋𝑋𝑋𝑖𝑖𝑖𝑖 is the mean of the process
at time 𝑖𝑖𝑖𝑖. At the initial value, 𝑍𝑍𝑍𝑍0 = 𝜇𝜇𝜇𝜇0 , where 𝑋𝑋𝑋𝑋𝑖𝑖𝑖𝑖 (𝑖𝑖𝑖𝑖 = 1,2, ⋯ ) are independent and normally
distributed observations, the statistics 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖 for sampline means 𝑋𝑋𝑋𝑋𝑖𝑖𝑖𝑖 is used. Then, the mean of 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖
are 𝐸𝐸𝐸𝐸(𝑋𝑋𝑋𝑋)𝑖𝑖𝑖𝑖 = 𝜇𝜇𝜇𝜇0 , and the variance of 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖 is given as follows:
𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉(𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖 ) = 𝜎𝜎𝜎𝜎𝑋𝑋𝑋𝑋2 �𝜆𝜆𝜆𝜆

2−𝜆𝜆𝜆𝜆�1 − (1 − 𝜆𝜆𝜆𝜆)2𝑖𝑖𝑖𝑖 �� , 𝑖𝑖𝑖𝑖 = 1,2, ⋯71(Eq.2)

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From this equation, as 𝑖𝑖𝑖𝑖 𝑖 ∞, then the asymptotic variance is 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉(𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖 ) = 𝜎𝜎𝜎𝜎𝑋𝑋𝑋𝑋2 �𝜆𝜆𝜆𝜆

2−𝜆𝜆𝜆𝜆the control limits:𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈/𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 = 𝜇𝜇𝜇𝜇0 ± 𝐻𝐻𝐻𝐻2 ��𝜎𝜎𝜎𝜎𝑋𝑋𝑋𝑋2 �𝜆𝜆𝜆𝜆

2−𝜆𝜆𝜆𝜆�, leading to(Eq.3)

��

where 𝐻𝐻𝐻𝐻2 is the coefficient of the control limit of EWMA control chart and 𝜇𝜇𝜇𝜇0 is the mean of

the process and variance is 𝜎𝜎𝜎𝜎𝑋𝑋𝑋𝑋2 .
Multivariate Exponentially Weighted Moving Average (MEWMA) chart is a statisticalcontrol chart used in quality control to monitor the mean vector of a multivariate process. It isan extension of the univariate EWMA chart to handle multiple variables simultaneously. The
MEWMA control chart is known to be sensitive to a small or gradual drift in the process for
two or more numeric variables. Examining the variables in a multivariate sense can beimportant especially when the variables are highly correlated. This is because joint out-ofcontrol conditions can occur without any individual variable violating its control limits whenplotted separately, Niaki et al (2011).
In this chart, let 𝑋𝑋𝑋𝑋𝑖𝑖𝑖𝑖 be a 𝑝𝑝𝑝𝑝 × 1 vector of the 𝑖𝑖𝑖𝑖th process sample containing the 𝑝𝑝𝑝𝑝 variate
normal distribution with in-control mean, 𝜇𝜇𝜇𝜇0 = 0, and covariance matrix 𝛴𝛴𝛴𝛴𝑋𝑋𝑋𝑋 . The MEWMA
control chart for monitoring the mean of a process is based on the following statistic:
𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖 = 𝜆𝜆𝜆𝜆𝜆𝜆𝜆𝜆𝑖𝑖𝑖𝑖 + (1 − 𝜆𝜆𝜆𝜆)𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖𝑖𝑖 , 𝑖𝑖𝑖𝑖 = 1,2,

where 𝜆𝜆𝜆𝜆 is the weighing parameter of the data, with 0 ≤ 𝜆𝜆𝜆𝜆<1. At the initial value we set, 𝑍𝑍𝑍𝑍0 =0, Note that if 𝛴𝛴𝛴𝛴−1
𝑍𝑍𝑍𝑍 is the inverse of the 𝑍𝑍𝑍𝑍 covariance matrix, then the plotted value in MEWMA
chart is 𝑄𝑄𝑄𝑄𝑖𝑖𝑖𝑖 = 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖′ 𝛴𝛴𝛴𝛴𝑍𝑍𝑍𝑍−1 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖 and the MEWMA chart signals if the following is satisfied:
𝑄𝑄𝑄𝑄𝑖𝑖𝑖𝑖 = 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖′ 𝛴𝛴𝛴𝛴𝑍𝑍𝑍𝑍−1 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖 > 𝑈𝑈𝑈𝑈

(Eq.4)

where 𝑈𝑈𝑈𝑈 and 𝜆𝜆𝜆𝜆 are the chart parameters to achieve a specific in-control average run length(ARL).
1.2.5

Results

1.2.5.1 Time Series Plots of Observed vs Imputed Water Monitoring Data
The time series plots presented in Figure 1 below illustrate the Pressure readings derived from
imputed data.

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Figure 1: Annual time series plot taken at every 15 minutes interval of raw pressure of theimputed pressure data. The red horizontal lines indicate the standard measurement level andthe red markers show the time of complaints received by the company.

It can be seen that following imputation, Figure 1 shows a minimum value of at least zero toreflect the actual pressure readings. The red dot markers indicate the time of the reported
complaints signifying the transient phase. It is observed that all the dots align with sudden surge
in flow rate, indicating an abrupt and intense fluid movement within the pipe system. The flowtime series readings based on the imputed data, plotted in Figure 2.

Figure 2: Annual time series plot taken at every 15 minutes interval of the imputed flow data(bottom). The red horizontal lines indicate the standard measurement level and the redmarkers show the time of complaint
Following imputation, the flow data reveals a minimum value of at least zero, aligning
accurately with the true flow readings. Overall, the red markers consistently aligned with thereported complaints, signifying the transient phase. However, it's noteworthy that not all red
markers coincide precisely with the sudden surge in flow rate, suggesting potential variations
or complexities in the observed patterns.
It is essential that we opt to utilize the imputed dataset for analysis to maintain continuity
in our examination and derive more meaningful observations.
1.2.5.2 Trends and Variations in Water Monitoring Data
We conducted additional examinations to explore potential seasonality and trends within thedata. Generally, variations in statistical characteristics, such as mean and standard deviation,

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can be anticipated in pipe flow and pressure. These fluctuations are attributed to the regularpatterns in water usage resulting from human behavior.

Figure 3: Violin plot for 15 mins interval weekly data within the year 2022 (a) Flow (b)Pressure
Figure 3 (a) shows the violin plot for the weekly flow across the year of data monitoring in
2022. Figure 3(b) shows the violin plot for the weekly pressure across the year of data
monitoring in 2022. It could be observed that certain weeks show higher variation than theothers especially for Pressure data. In contrast, flow data more or less show more stable reading
except for some burst of water flow in several weeks.

Figure 4: Average Weekly data each week within the year 2022 (a) Flow (b) Pressure
Figure 4 (a) shows the average flow for every week in 2022. Figure 4(b) shows the averagepressure for every week in 2022. It could be observed that the average flow and pressure is74

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different from week to week especially for pressure data. Flow data also show variations but
not as erratic as that of pressure data.
In summary, it can be observed that the plots presented (Figures 3 and 4) indicate
variations in statistical characteristics, specifically in the mean and standard deviation of theobservations. As control charts rely on these parameters to establish control limits (Eq.3, andEq.4), the accuracy of the control chart methods may be significantly compromised if applied
directly to the raw monitoring data. Thus, the weekly patterns in water consumption should beconsidered so that dynamic thresholds can be set according to real-time behaviour.
In addition, the noticeable shift in observations is more pronounced in the pressure datacompared to the Flow data. Consequently, utilizing flow data in leakage detection analysis isdeemed more reliable, as it is less prone to generating false alarms than the pressure data. In thenext section, we will focus the analysis on flow data for detection of pipeburst.
1.2.5.3 Detection of Pipe burst using Control Charts for Water Monitoring
In this section, the results of the identification on the transient signals of the pipe burst using
EWMA based Control charts are presented based on two approaches.
a) MEWMA and EWMA Control charts on Annual Datab) EWMA Control charts based on data segmentation and adaptive parametersettingsa) MEWMA and EWMA Control charts on Annual Data
In this section, the results of detection of pipe burst using the annual water pipe records on two
types of control charts: Exponentially Weighted Moving Average for multivariate data
(MEWMA) and Exponentially Weighted Moving Average for univariate data (EWMA) ispresented. In EWMA, pipe flow and pressure are presented in separate charts, whereas in
MEWMA, the two variables are applied simultaneously.
In MEWMA, pipe flow and pressure are applied together, forming multivariate data fora single control chart. Various parameter settings are applied to MEWMA. However, the
MEWMA chart (Figure 5) does not seem to show a useful representation of the data, as all theobservations are still contained in the limits. This would not be helpful in detecting the transient
signals as no significant points are detected to indicate the initial pipe burst event. This is likely
due to the large and varying shift parameters between pressure and flow rates over the time
period. When there are varying shift parameters, the chart may become overly sensitive to thesechanges, leading to false alarms or a decreased ability to identify genuine shifts.

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Figure 5: MEWMA control chart for both multivariate Pressure and Flow data. No detectedsignals beyond the control limits.

In EWMA, the parameters settings for the control charts are shown in Table 1. The control
charts for the pipe flow and pressure data are presented in separate charts.
Table 1: parameter settings for EWMA control charts
MODEL

Variables

Lambda

Sigma

EWMA

Imputed Flow

0.1

3

Points Beyond
Limits (%)
89%

0.5

3

78%

0.99

3

64%

Imputed Pressure 0.5

3

88%

0.8

3

84%

0.99

3

81%

The results for EWMA with different settings are as shown in Table 1. This setting shows thata substantial number of points are contained within the limits. As shown in the last column‘Points Beyond Limits (%),a subset of the observations are flagged as out of the control limits.
Table 1 also shows that different settings indicate different proportions of flagged out
observations. In particular the higher the value of lambda, the lower proportion of points will
be flagged down. It can be seen from Table 1 that flow data gave a better reading of lowest
proportion points beyond the limits at lambda 0.99 and Sigma as 3, at 64% compared to that ofpressure data at 81%.

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The control charts based on EWMA for flow and pressure is as shown in Figure 6 andFigure 7:

Figure 6: EWMA control chart for imputed Flow data, using the most optimal settings.

Figure 7: EWMA control chart for imputed pressure data, using the most optimal settings
Figure 6 and Figure 7, show the control charts for the most optimal settings for flow andpressure data. Due to the massive number of data, the control limits and detected points are notclear.
The EWMA chart appears to successfully detect a number of points beyond the control
limits, than MEWMA chart. However, the detected points are too many, and they may likely toinclude false alarm detected points. Also, the detected points detected are not clearly shown in
the graphs due to the massive annual data that is being analysed. In addition, the parameters
used in this control chart are based on the overall parameter value for the annual data, which
does not take into account of the fluctuation that occurs in phases in the data. Consequently, thedetected points can be challenging to identify in the monitoring process.
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In the next section, we continue to use EWMA chart and improve the detection methodby using segmented data with targeted parameter settings based on the feature of the data.
b) EWMA Control charts based on data segmentation and adaptive parametersettings
In this section, the results obtained from applying EWMA control charts to flow time series on
segmented data is presented. Specifically, water monitoring is conducted on a weekly basis,
employing a shorter time series data to observe transient signals on the control chart. Also, in
contrast to the previous analysis in section 2.5.3(a), here the parameter settings for the control
charts are adjusted to align with the weekly characteristics of the monitored data.
In section 2.5.2, it can be observed that the average flow has been found to vary overtime every
week. A snapshot of the calculated weekly average flow readings, the corresponding estimatedvariation and coefficient variation, as well as the complaint indicator can be seen in Table 5 (forthe whole list, refer to Appendix 1).
Table 2: Snapshot of the calculated weekly average flow readings, and the corresponding
estimated variation and coefficient variation
Week

Mean Flow

1
2
…
8
…
16
…
24
…
40
…
51
52

16.3
15.8
…
15.7
…
15.9
…
17.6
…
17.9
…
15.1
16.2

Standard
Deviation
8.5
8.3
…
6.1
…
4.9
…
10.5
…
11.8
…
4.5
8.7

Coefficient of
Variation (%)
52.1
52.5
…
38.9
…
30.8
…
59.7
…
65.9
…
29.8
53.7

Complaint
1
1
1
1
2
1
2

Table 2 provides an overview of the average flow rates observed on a weekly basis,
accompanied by relevant statistical measures in the year 2022. The standard deviation shows
the degree of dispersion around the mean, and the coefficient of variation (CV) expressed as apercentage, provides insight into the relative variability in comparison to the mean. Higher
standard deviation values implies higher variability; and higher CV implies higher relative
variability. For instance in Week 1, the average flow is 16.3. The degree of variation for otherobserved flow values in week 1, around 16.3 is 8.5. Also, the relative variability is calculated
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by the coefficient of variation is 52.1%. A higher CV may suggest increased volatility or
fluctuations within the dataset. For example, a CV value of 50% is more likely to raise concerns
than a CV of 29%.
It can be observed from Table 2, that the mean flow values exhibit some variation acrossdifferent weeks, ranging from 14.2 to 17.9. This indicates fluctuations in the average flow rateover the monitored period. In addition, Weeks 24 and 40 stand out with relatively higher
standard deviations, suggesting increased variability in flow during those periods. Also, higher
CV values, such as those in weeks 24, 40, and 52, indicate greater relative variability. While
the mean flow values vary, there were some consistent patterns or trends over time. Weeks 1 to
5, for instance, show a gradual decrease in mean flow, followed by a slight increase in thesubsequent weeks.
Table 3 outlines the number of complaints received, corresponding statistics and theparameter settings employed. employed. The selection of parameters in Table 3 is contingent
upon the variations observed in the weekly data, with a specific focus on the coefficient ofvariation (CV) values. If the CV for a given week exceeds 55% (indicated by the blue row inTable 3), the sample standard deviation for that week is utilized as the variation parameter inthe EWMA chart. Conversely, if the CV for the week is below 55% (highlighted by the green
row in Table 3), a constant rate of 15 is applied as the variation parameter in the EWMA chart.
This adaptive approach ensures a suitable adjustment of parameters based on the variability
characteristics of the weekly data.
Table 3: Adaptive parameter settings for EWMA control charts (weekly)
WeekComplaint

1
2
…
8
…
16
…
24
…
40
…
51
52

1
1
1
1
2
1
2

Coefficient of
Variation (%)
52.1
52.5
…
38.9
…
30.8
…
59.7
…
65.9
…
29.8
53.7

Standard
Deviation
8.5
8.3
…
6.1
…
4.9
…
10.5
…
11.8
…
4.5
8.7

Adaptive
Sigma
15
15
…
15
…
15
…
10.5
…
11.8

Points Beyond
Limits (%)
7%
1.3%
…
3.3%
…
0.6%
…
9.1%
…
7.1%

15
8.7

0%
7.6%

It can be observed from Table 6 that this adaptive parameter setting strategy results in a much
lower percentage of points beyond limits, varying from 0% to 9.1%. Notably, the data highlights
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the effectiveness of adaptive control limits in identifying transient signals, with lower
percentages indicating better control over the process. The data underscores the dynamic natureof the monitored system and the utility of adaptive approaches in responding to fluctuations in
the observed data.
The resulting EWMA control charts from these adaptive parameters for Flow data for weeks
showing different variations of data at varying CV values at Week 24 (CV 69%) as shown in
Figure 13.

Figure 8: EWMA control chart for week 24 (CV 69%) based on adaptive variation

1.2.6 Conclusions and Recommendations
This report recommends a combination of dynamic thresholding based on weekly patterns,
targeted analysis of reliable flow data, and adaptive parameter settings in control charts,
particularly with a focus on shorter time series, enhance the effectiveness of pipe burst
detection.
In this study, it can be observed that detecting gradual leakage can be challenging
because it often starts with a small amplitude and a slow, incremental increase, making it harder
to notice in the initial stages.
For water monitoring, data pre-processing is important to be carried out due to several
factors: (1) to ensure continuity and meaningful observations (2) to identify any significant
patterns or trends in the data and (3) to identify the most effective and appropriate statistical
tools and parameters to use for the data. The shift in observations, particularly in Pressure data,
underscores the reliability of utilizing Flow data for leakage detection due to its lower
susceptibility to false alarms. The shift in observations, especially in Pressure data, suggests

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that Flow data is more reliable for this purpose and has a lower susceptibility to false alarms.
Therefore, Flow data is recommended over Pressure data for leakage detection.
The analysis also emphasizes the importance of considering weekly flow patterns inwater monitoring for establishing dynamic thresholds in control charts. The use of EWMA
control charts on segmented flow time series with fine-tuned parameters, resulting in improved
control over the process. The adaptive parameter strategy significantly reduces the percentage
of points beyond limits (0% to 9.1%), emphasizing its effectiveness in identifying transient
signals. This underscores the dynamic nature of the system, highlighting the utility of adaptive
approaches in responding to fluctuations in observed data and facilitating the easy detection oftransient signals.
The analysis done in this study can be replicated to test the transient signals in future
data. Hoewever, note that the adaptive parameter settings recommended in Appendix 1 assumes
that the patterns in water measurements is similar from year to year. Thus, further analysis on
the trends and variation of the data from several years need to be carried out to assure that thisassumption is validated.
An alternative approach that can be considered to detect transient signals for water
monitoring is using a form of transformation in the data called differencing in the control charts.
For instance, we can transform the data by the taking the lag of 7 days to detect as an alarm tocalculate the mean and variations for the control chart. By considering this weekly differencing,trend variation is expected caused by any gradual leakage events while removing the influenceof the gross trend in the monitoring data that can be caused by other factors.

1.3 Pipe Burst Detection With Artificial Neural Network (ANN)
1.3.1 Introduction
Utilizing pressure, flow, and critical pressure data as inputs, an Artificial Neural Network(ANN) model is trained to detect pipe bursts in water supply systems. By learning from
historical data where instances of bursts are correlated with specific patterns in theseparameters, the ANN can quickly analyze current data and predict the likelihood of a burst
event.
1.3.2 Objectives(a) Develop an Artificial Neural Network (ANN) model capable of predicting pipe bursts basedon input variables of pressure impute, flow impute, and critical pressure impute.
(b) Use the generated ANN model to validate the predicted value with complaint data

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1.3.3 Methodology
1.3.3.1 Artificial Neural Network (ANN) model
An Artificial Neural Network (ANN) is a mathematical model designed to imitate the structureand functions of biological neural networks. A basic Artificial Neural Network (ANN) usuallycomprises three layers: input, hidden, and output layers, as illustrated in Figure 9. The initiallayer is the input layer, responsible for accepting independent variables. The final layer is theoutput layer, responsible for generating output variables. The layers situated between the input
and output layers are referred to as "hidden layers" (Zhu et al., 2022).
The fundamental component of every artificial neural network is the artificial neuron,
which is a simple mathematical model or function. This model follows three basic sets of rules:
multiplication, summation, and activation. At the input of the artificial neuron, the inputs
undergo a process called weighting, where each input value is multiplied by its corresponding
weight. In the middle section of the artificial neuron, a sum function adds up all the weighted
inputs and bias. Finally, at the output of the artificial neuron, the sum of the previously weighted
inputs and bias passes through an activation function, also known as a transfer function (refer
Figure 10). The transfer functions usually have a sigmoid shape, but they may also take theform of other non-linear functions, piecewise linear functions, or step functions. Here, An ANN
with 2 hidden layers for 3 features or inputs with Sigmoid Symmetric Transfer Function is used.

Figure 9: Illustration of a typical neural network model (Zhu et al., 2022)

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Figure 10: Illustration of a typical flow of neural network model
1.3.3.2 Backpropagation method Neural Network based on Levenberg-Marquardt
Although there are several various network models, the backpropagation neural network(BPNN) is particularly effective in recognizing patterns within practical applications, as
highlighted by Lek & Guégan (1999). The BPNN model aims to minimize the mean squareerror between the predicted and expected values by adjusting the connection weights of the
network (Li et al., 2012). BPNN comprises of three layers: input layer, hidden layer, and output
layer. The input signal is forward-propagated from the input layer, via the hide layer, to the
output layer. The difference between the predicted output and expected output of the network
is referred to as the error signal. This error signal is then backpropagated from the output layer
to the input layer in a step-by-step manner across the layer. Throughout this process, the weightvalues of the network are adjusted based on the error feedback to make the predicted output
closer to the expected one (Li et al., 2012). The Levenberg-Marquardt (LM) algorithm isessentially an evolution of the gradient descent algorithm and Newton algorithm, whichsignificantly accelerate the convergence rate by reducing the iteration process, resulting in moreaccurate data. In comparison with the traditional BPNNs, such as slow convergence speed andlocal minimum problems, the convergence rate of the LM algorithm is the fastest of all
traditional or improved networks. The improved BPNNs with the LM algorithm havedemonstrated excellent results as indicated by research from Wang (2013), Hua et al. (2008),
Jian et al. (2012) and Miao et al. (2011). In this study, LM will be implemented.

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1.3.4 Results
An ANN is employed for predictive modeling, featuring two hidden layers to process three
input features. The Sigmoid Symmetric Transfer Function is applied, and the LevenbergMarquardt (LM) algorithm is utilized for optimization. The training data, depicted in Table 4,
offers a glimpse into the input and output layers.
The initial three columns of Table 4 represent the input layer, comprising Pressure,
Flow, and CP Pressure. The last column of Table 4 signifies the output layer, indicating theoccurrence of pipeburst. The training data for the output layer adopts values ranging from 0 to
1. Zero values denote non-occurrence and the value one denotes occurrence of pipeburst events.
Other values in this column are strategically designed to mirror the gradual progression ofpipeburst events. Specifically, output values are set from 0.2 to 0.5 before a reported incident,
escalating to 1 post-complaint. This design aims to capture and reflect the evolving nature ofpipeburst occurrences in the model's training phase.
Table 5 presents the outcomes of the generated artificial neural network (ANN) model'svalidation process. The results demonstrate the model's performance in predicting events basedon actual data. Upon validation, it was observed that out of the total 10 complaints considered,
the ANN model accurately predicted the occurrence of events in 7 instances, while in 3 cases,
the predictions deviated from the actual outcomes. This discrepancy underscores the necessity
for further refinement and optimization of the ANN model to enhance its predictive accuracy.
Table 4: Sample training data for ANN input and output layers
PressureImpute

FlowImpute

CPPressureImpute

Pipeburst Event
Occurrence

12.6

6.03

12.23

0

12.6

5.72

12.34

0

12.6

6

12.34

0

12.6

5.96

12.34

0.2

10.6

28.71

9.73

0.5

8.7

50.36

5.78

0.5

8.6

51

5.69

0.5

8.5

50.99

5.48

0.5

8.3

51.49

5.3

1

8

51.76

5.11

1

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7.8

52.3

4.89

1

7.6

52.44

4.74

1

3.8

39.91

2.19

1

5.8

62.8

1.09

0

7.1

53.98

3.58

0

8.3

45.93

5.73

0

Table 5 ANN model's validation
ANN
LM-15

ANN
LM-30

Actual Pipeburst
Event
Occurrence

Probability of
Pipeburst
Occurrence

Predicted
Pipeburst Event
Occurrence

Probability of
Pipeburst
Occurrence

Predicted
Pipeburst Event
Occurrence

1

0.78

1

0.74

1

1

0.27

0

0.35

0

1

0.79

1

0.71

1

1

0.93

1

0.92

1

1

0.71

1

0.91

1

1

0.12

0

0.25

0

1

0.96

1

0.92

1

1

0.83

1

0.82

1

1

0.89

1

0.93

1

1

0.000200.00040

1.4 Acknowledgements
This work was supported by the Institute of Mathematics for Industry, Joint Usage/ResearchCenter in Kyushu University. (FY2023 Workshop (I) “MMISG2023” (2023b003).)85

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INDUSTRY PRESENTATION SLIDES

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OUTPUT PRESENTATION SLIDES

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Tackling Carbon Emissions: Strategies for Medium and Heavy-Duty Trucks

Industry Representatives: Raja Dzulfitrie Sa’ad12
Zainal Azman bin Zawawi12(Total Logistic Services (M) Sdn. Bhd.)

Study Group Contributors: Nur Arina Bazilah bt Aziz1, Adibah Shuib3, Zaitul
Marlizawati Zainuddin1, Zulkifli Mohd Nopiah2, Zulhasni
Bin Abdul Rahim1, Zulkarnain Bin Abdul Latiff1, Zaharah
Mohd Yusoff3, Jamaliatul Badriyah6, Syarifah Zyurina
Nordin1, Wan Rohaizad Wan Ibrahim1, Shukur Hassan1,
Khalid Solaman Almadani1

1.1 Introduction
As a country experiencing rapid industrialization and economic growth, Malaysia faces
significant challenges related to carbon emissions and their impact on the environment. The
nation’s economic development, while crucial for progress, has led to increased carbon
emissions primarily from industries, transportation, and energy production. Emissions levels
across all economic sectors have increased over the 2010–2017 period. CO2 emissions fromthe transport sector represented 28.8% of total fossil fuel combustion in Malaysia, well above
the global average of 24.5%. Road transport is also the largest CO2 emitter among all transport
subsectors (International Energy Agency- IEA, 2019).
To address this issue, Malaysia has implemented substantial efforts to address
greenhouse gas (GHG) emissions by making a dedicated commitment to the global community.
Under its National Determined Contribution (NDC) pledge presented during COP26 andreiterated in COP27, the Malaysian government has pledged to achieve net-zero emissions by
2050. Additionally, there is a commitment to reduce CO2 intensity in relation to GDP by 45%
by 2030 (MIDA, 2023).
This pledge has been incorporated into the 12th Malaysia Plan and the National Energy
Policy 2022-2040 (NEP, 2022). This policy documents the way forward strategically andoutlines key priorities for the energy sector in the coming years. Figure 1 below shows theaction plan for the heavy vehicles segment in the National Energy Policy 2022-2040 (Ministryof Economy, 2022)

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Figure 1: Action Plan for the Heavy Vehicles Segment in Malaysia National Energy
Plan 2022-2040
Under the government's recent policy, industries are mandated to adhere to and follow
regulations aimed at curbing carbon emissions including Total Logistic Services (M) Sdn.
Bhd (TLS).
As an integral part of the Toyota Tsusho Group of companies, TLS is mandated toparticipate in the Company Group Wide Carbon Neutrality Challenges, which entail achieving
two primary objectives as shown in Figure 2 below.

Figure 2: Toyota Tsusho Group Carbon Neutrality Declaration

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At present, TLS has devised a three-year strategy to meet these challenges. This plan
includes several scheduled activities such as optimizing routes, scheduled vehicle
maintenance, implementing a truck aging policy limiting trucks to a maximum of 20 years inservice, providing eco-driving skill training for drivers, setting a maximum idling period of 3
minutes, exploring innovative solutions using mathematical approaches or tools, andtransitioning to electric trucks.
One approach involves employing innovative solutions, notably mathematical
approaches, to identify the factors contributing to carbon emissions. Through this analysis,
TLS aims to effectively address the carbon emission issue by gaining a deeper understandingof the involved factors.
1.2 Identification of Factors Contributing to Carbon Emissions
1.2.1 Carbon Emission Formula
To ascertain the factors contributing to carbon emissions in heavy and medium-duty
vehicles, an examination of the carbon emission formula has been conducted. This formula has
been obtained from the TLS calculation sheet.

Within this formula, Toyota Tshusho has predefined certain parameters including
distance, number of trips, and vehicle utilization. Consequently, the parameters available forinvestigation encompass emissions factor and fuel efficiency. The exploration of factors
influencing carbon emissions will be centered around these two parameters.
1.2.2

Factors Contributing to the Carbon Emissions

As global concerns regarding climate change and environmental sustainability intensify,
there is a growing need to understand and address the factors that contribute to carbon
emissions. Carbon emissions, largely in the form of carbon dioxide (CO2), are a significant
driver of anthropogenic climate change. From the carbon emission formula in Section 1.2.1, thecarbon emission factors can be investigated based on the carbon emission parameter and fuelefficiency.
1.2.2.1 Emission Factor
An emission factor is a fundamental concept used to quantify the rate at which aparticular pollutant, such as carbon dioxide (CO2), is emitted into the atmosphere per unit ofactivity or fuel consumption. It provides a standardized measure of emissions associated withvarious industrial processes, transportation activities, energy production, and other human109MALAYSIA MATHEMATICS IN INDUSTRY STUDY GROUP (MMISG2023)
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activities. In most instances, these factors are derived from averages of available data ofacceptable quality, assumed to represent long-term averages for all facilities within the source
category, thus serving as population averages. The emission factor is typically calculated using
empirical data obtained from laboratory experiments, field measurements, or emissions
inventories. It may vary depending on the specific activity or process being analysed. The
general equation for emission estimation is formulated asdenotes the emission factor, andwhere represents emissions, signifies the activity rate,
stands for overall emission reduction efficiency, expressed as a percentage. ER is further
delineated as the product of the efficiency of the control device's destruction or removal and thecapture efficiency of the control system. Some factors influencing the emission factor,
including fuel type, combustion technology, operating conditions, fuel quality and control
technologies. Table 1 below shows the Carbon emissions factors for different types of fuel
(EEA, 2023).
Table 1: Carbon emission factors for different types of fuelAccurate estimation of emission factors is essential for assessing the environmental
impact of various human activities and for developing effective strategies to mitigate emissions
and combat climate change. Based on Table 1, one way of reducing the carbon emissions is bychanging the fuel type to the one with low emission factor.
1.2.2.2 Fuel Efficiency
The quantity of emissions generated by medium and heavy-duty vehicles in various
scenarios is influenced by multiple factors such as vehicle class and weight, driving patterns,
vehicle use, fuel category, engine exhaust treatment, vehicle age, and terrain conditions (Clark
et al., 2002). Understanding the complex interplay of these factors is paramount for devising
effective strategies to mitigate carbon emissions, transition towards sustainable practices, andfostering a more resilient and environmentally conscious global community.
Factors influencing fuel efficiency were investigated, exploring both controllable anduncontrollable factors as shown in Figure 3 below.
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1.
2.
3.
4.
5.

Uncontrollable factors
Vehicle design
Maintenance practices
Vehicle load
Route planning
Vehicle age

Controllable factors (short-term)
1. Driving behaviour
2. Fuel quality
3. Alternative fuelControllable factors (long-term)
1. Engine technology

Figure 3: Uncontrollable and controllable factors in carbon emissions influencing fuelefficiency
1.2.2.2.1 Uncontrollable Factors
Uncontrollable factors are those that have been either addressed or are beyond thecontrol of TLS, such as fixed elements or external conditions like weather. However, the fixedfactors such as vehicle age, vehicle load and maintenance schedule could also be further studies
to validate the efficacy of the specified scheme.
i)

Vehicle Design

Vehicle design plays a pivotal role in determining the efficiency and environmental
impact of automobiles. Two crucial aspects of vehicle design that significantly influence fuelefficiency are aerodynamics and weight. Aerodynamics plays a crucial role in the design of
medium and heavy trucks, influencing both fuel efficiency and overall performance.
Streamlined designs that minimize air resistance are essential for reducing drag forces,
especially at higher speeds. For trucks, which often operate on highways and cover long
distances, aerodynamic features such as sloped front ends, side fairings, and aerodynamic
mirrors are implemented to enhance fuel efficiency. The objective is to reduce the energy
required to overcome air resistance, thereby optimizing fuel consumption. Engineers employ
wind tunnel testing and computational fluid dynamics to refine truck designs, ensuring they cut
through the air with minimal resistance.
The weight of a medium or heavy truck is a critical factor affecting its fuel efficiency
and operational cost. Lighter vehicles generally demand less energy to move, translating to
improved fuel efficiency. Truck designers and manufacturers focus on employing materials thatoffer strength while minimizing weight, such as high-strength steel and aluminum alloys.
Additionally, advancements in materials technology and manufacturing processes contribute to
the development of lightweight components without compromising structural integrity. Payload
capacity is another consideration, balancing the need for a robust structure with the ability tocarry substantial loads. By optimizing the weight of truck components, manufacturers canenhance fuel efficiency, reduce emissions, and increase the overall economic viability of thevehicle.

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Recommendation: While currently classified as an uncontrollable factor, there is potential in
the future to procure more aerodynamic designs featuring optimal truck component weights toboost fuel efficiency. Additionally, TLS can consider integrating multiple vehicles with varying
weights into their delivery schedule.
ii)

Maintenance Practices

Maintenance practices contribute to the longevity and efficiency in the medium andheavy-duty vehicles. These substantial machines demand meticulous care to ensure optimal
performance, longevity, and fuel efficiency. Proper and timely maintenance is the cornerstoneof ensuring that medium and heavy-duty vehicles operate at their peak efficiency. This is thecurrent practices in TLS to ensure optimal engine functioning, improving fuel efficiency andreducing emissions. Regular maintenance practices include scheduled oil changes, which areessential for preserving engine health and lubrication. This prevents friction-induced wear andtear, enhancing fuel efficiency. Air filter replacements are equally crucial, as clean filters
optimize air intake, facilitating efficient combustion. Engine tune-ups, involving inspections ofvarious components and adjustments, ensure that the engine operates at its designed efficiency,
promoting not only performance but also fuel economy. Regular maintenance practices, whenadhered to diligently, contribute significantly to extending the operational life of these vehicles
and minimizing fuel consumption. For the old engine, it is advisable to maintain the catalytic
converter regularly to help reduce the carbon emissions.
Tire maintenance is not to be ignored for medium and heavy-duty vehicles. Properly
inflated tires reduce rolling resistance, the force required to move a tire over a surface. By
maintaining the recommended tire pressure, rolling resistance is minimized, improving fuelefficiency. Routine tire inspections for wear and alignment issues are also essential. Misaligned
or improperly inflated tires not only compromise safety but can also lead to increased fuelconsumption. Regular tire rotations further ensure even wear, optimizing the lifespan of thetires and contributing to sustained fuel efficiency. In essence, tire maintenance is a proactive
measure that directly impacts operational costs and the overall environmental footprint of thesevehicles.
Recommendation: TLS may explore the utilization of Operations Research (OR) techniques
to optimize maintenance schedules, taking into account factors like vehicle availability,
resource constraints, maintenance requirements, and operational priorities. This approachensures that maintenance tasks are scheduled at optimal times to minimize operational
disruptions while maximizing vehicle uptime. Additionally, proactive preventive maintenanceplanning can be conducted by analyzing historical maintenance data, vehicle usage patterns,
and manufacturer recommendations. By scheduling maintenance preemptively based on
anticipated failure rates or usage thresholds, the risk of costly breakdowns and unexpected
downtime can be mitigated.

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iii)

Vehicle Load

The concept of optimal vehicle load is a fundamental consideration in the carbon
emission investigation for medium and heavy-duty vehicles. For the logistics companies, thetransportation must navigate the delicate balance between payload requirements and fuelconservation. The weight a vehicle carries, specifically in terms of cargo, is a critical factor
influencing fuel efficiency for medium and heavy-duty vehicles. Carrying unnecessary weights
demands more energy for acceleration and maintaining speed, resulting in increased fuelconsumption. Therefore, careful consideration of cargo weight is vital. Understanding the
payload requirements for a given journey and loading only what is essential not only conserves
fuel but also ensures compliance with safety regulations and enhances the overall efficiency of
the transportation process. One of the proactive measures for enhancing fuel efficiency is theperiodic removal of excess cargo. Regular assessments of payload requirements and ensuringthat vehicles only carry what is necessary contribute to sustained fuel savings. Moreover, thispractice aligns with safety standards and ensures that vehicles operate within their designed
capacities, promoting both efficiency and longevity. In TLS, the vehicle load is alreadydetermined according to their demand and delivery schedule. Therefore, this is not acontrollable factor in this study. However, it is known that heavier loads increase fuelconsumption and emissions, necessitating more energy to transport goods.
Recommendation: By utilizing OR techniques to optimize vehicle loading based on delivery
schedules and demand patterns, it can efficiently load vehicles with the right mix of deliveries
hence reduce the number of trips needed and maximize vehicle utilization, thereby minimizing
carbon emissions per delivery.
iv)

Route Planning

The efficiency of route planning is essential for medium and heavy-duty vehicles. The
strategic selection of routes is crucial not only for timely deliveries but also for the optimizationof fuel efficiency. Optimal routes consider factors such as traffic patterns, road conditions, andpotential delays. Leveraging advanced mapping technologies and real-time traffic data, route
planners can identify the most efficient paths for vehicles to navigate. Route planning thatstrategically circumvents congested areas helps vehicles maintain a consistent speed, reducing
the overall fuel consumption. This not only conserves resources but also contributes to timely
deliveries, streamlining the logistics process.
Recommendation: TLS can consider alternative delivery strategies such as consolidation,
pooling, or collaborative delivery models to reduce the number of vehicles on the road andminimize emissions. By consolidating deliveries to maximize route efficiency, TLS can lower
overall carbon emissions.

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Vehicle Age

The age of medium and heavy-duty vehicles significantly influences carbon emissions,with older vehicles typically emitting higher levels of pollutants compared to newer
counterparts. As vehicles age, wear and tear on engine components can lead to decreased
efficiency and suboptimal combustion processes, resulting in increased carbon emissions. Olderengines may lack advanced technologies and emission control systems found in newer models,
further exacerbating their environmental impact. Additionally, aging vehicles may experience
deteriorating fuel efficiency, requiring more fuel to achieve the same level of performance,
thereby contributing to higher carbon emissions. Regular maintenance and periodic upgradesor replacements of older vehicles with newer, more fuel-efficient models can help mitigate theimpact of vehicle age on carbon emissions, promoting cleaner and more sustainabletransportation practices.
Recommendation: While acquiring newer vehicles may seem like the straightforward solution,
given the high cost involved, it is advisable for TLS to prioritize regular maintenance andupgrades for their older vehicles. This approach will aid in mitigating the impact of vehicle age
onfuel
efficiency
andcarbonemissions.
In addition, instead of a fixed years service, strategy such as fleet replacement planning could
also be utilized. Fleet replacement planning involves developing a structured approach todetermine when existing vehicles or equipment should be replaced with newer models or
upgraded versions. By systematically evaluating the condition and performance of fleet assets
and making informed decisions about replacement timing and strategy, organizations can
optimize fleet efficiency, minimize operating costs, and ensure the reliability and safety of theiroperations.
1.2.2.2.2 Controllable Factors
This discussion will focus on the controllable factors that could be administered by TLS.
They are divided into short-term and long-term factors. Short-term factors here are the factorsthat can be adjusted relatively quickly and have an immediate impact on carbon emissions suchas driving behavior, fuel quality and alternative fuel. Meanwhile, long-term factors are thosethat require more time and effort to adjust and have a gradual impact on carbon emissions overan extended period such as fleet replacement plan. Although it takes time to implement andyield results, they often lead to sustained reductions in carbon emissions over the long term.

i)

Driving Behaviour

Efficient driving behavior is a pivotal factor in the quest for sustainability and fuelefficiency in the realm of medium and heavy-duty vehicles. How drivers operate thesesubstantial machines has a profound impact on fuel consumption, emissions, and overall

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operational costs. This emphasizes the significance of maintaining optimal speeds andembracing smooth driving techniques to enhance fuel efficiency.
The speed at which a medium or heavy-duty vehicle is operated is directly linked to its
fuel efficiency. Maintaining optimal speeds is paramount in ensuring that the engine operates
within its most efficient range. Excessive speeds not only lead to increased air resistance,
demanding more energy to propel the vehicle, but they can also result in the engine operating
at less efficient levels. Striking the right balance by adhering to recommended speed limits andadjusting driving speed based on road conditions contributes significantly to fuel efficiency. By
doing so, drivers can achieve a harmonious blend of performance and economy. Throughobservation from the TLS’s fleet management system, the drivers’ speed is recorded and thoseexceeding the speed limit of 90 km/hr will be monitored further.
Smooth driving techniques, characterized by gentle acceleration and braking, areintegral to fuel savings. Abrupt acceleration and harsh braking not only consume more fuel but
also contribute to increased wear and tear on the vehicle's components. Gentle acceleration
allows the engine to operate more efficiently, while gradual braking harnesses kinetic energy,
potentially reducing the need for excessive fuel consumption. Driver training programs oftenemphasize the importance of smooth driving habits, recognizing that a steady and controlled
approach to acceleration and braking positively influences fuel efficiency, enhances safety, andextends the lifespan of the vehicle. Minimizing stops, such as unnecessary detours or frequent
breaks, optimizes fuel efficiency. Continuous motion allows the vehicle to operate within its
most efficient range, contributing to overall energy conservation. Moreover, reducing thenumber of stops enhances the predictability of delivery schedules, improving the reliability oflogistics operations.
Idling, or leaving the engine running while the vehicle is stationary, is a significant
contributor to unnecessary fuel consumption. Addressing idling time is a crucial aspect ofdriving behavior for medium and heavy-duty vehicles. Excessive idling not only consumes fuelwithout contributing to forward motion but also increases emissions. Implementing strategies
to minimize idling, such as turning off the engine during prolonged stops, contributes tosubstantial fuel savings and aligns with eco-friendly driving practices.
Recommendation: TLS may also consider initiating a carbon emission awareness program fordrivers and providing incentives to those who exhibit exemplary driving behavior.
ii)

Fuel Quality

The type and quality of fuel a vehicle consumes plays a foundational role in its overall
efficiency. Medium and heavy-duty vehicles, often equipped with robust engines, are designed
to operate optimally with specific fuel types. High-quality fuels, meeting, or exceeding industry
standards, ensure a cleaner and more efficient combustion process. Clean fuels, such as thosewith low sulfur content, contribute to reduced emissions, aligning with stringent environmental

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regulations. Moreover, the compatibility between the fuel type and the engine design is crucial
for achieving peak performance and fuel efficiency.
Recommendation: TLS may consider employing high-quality, cleaner fuels to improve
combustion, thereby reducing carbon emissions. Regular testing can help detect any variations
in fuel quality, enabling prompt corrective action to be taken.
iii)

Alternative FuelThe utilization of alternative fuels presents a promising avenue for reducing carbon
emissions from medium and heavy-duty vehicles. By transitioning away from traditional fossil
fuels to cleaner alternatives such as biodiesel, ethanol, natural gas, or hydrogen, these vehicles
can significantly mitigate their environmental impact. Alternative fuels offer the potential for
lower carbon content, resulting in reduced greenhouse gas emissions during combustion.
Additionally, some alternative fuels, such as natural gas or hydrogen, produce fewer harmful
pollutants like particulate matter and nitrogen oxides, further enhancing air quality. However,
the adoption of alternative fuels is not without challenges. The production and distribution
infrastructure for alternative fuels are often underdeveloped and costly to implement, limiting
their widespread availability and affordability. Additionally, certain alternative fuels may
require modifications to vehicle engines or fuel systems, adding complexity and expense to thetransition process. Moreover, the production of some alternative fuels may still generate
emissions or contribute to other environmental concerns, highlighting the importance ofcomprehensive lifecycle assessments when evaluating their impact on carbon emissions.
Another common alternative is to use fuel additives to improve combustion efficiency andreduce emissions. Certain additives can help optimize fuel performance, leading to improved
engine efficiency.
Recommendation: Given that the adoption of alternative fuels could entail significant
expenses associated with engine modifications and transitioning processes, TLS may initiallyexplore the use of fuel additives. However, it's crucial to approach the use of fuel additives
cautiously due to potential side effects such as decreased engine performance or increased
emissions.
iv)

Engine Technology

For medium and heavy-duty vehicles, the convergence of advanced technology andemission control systems represents a pivotal step towards sustainability and operational
efficiency. Innovative solutions such as hybrid systems, regenerative braking, and start-stop
systems offer promising avenues for reducing fuel consumption and minimizing environmental
impact. Hybrid technologies seamlessly integrate electric propulsion with traditional internal
combustion engines, optimizing fuel efficiency and curbing emissions. Similarly, regenerative
braking harnesses kinetic energy during braking, while start-stop systems mitigate unnecessary
idling, further enhancing fuel savings. However, despite their transformative potential, these116

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technologies may pose challenges such as higher upfront costs and potential maintenancecomplexities. Additionally, the integration of advanced systems requires careful considerationof infrastructure and training to ensure optimal performance and reliability in diverse operating
conditions.
Recommendation: TLS can opt for the new engine technology for their current vehicles inorder to have the latest emission control technology. Although this solution might be costly, it
is still cheaper compared to purchasing new trucks or the electric trucks.
1.3 Emission Analysis and Evaluation
In order to see the relationship between selected factors, a comprehensive analysis of carbon
emissions, fuel efficiency, vehicle age dynamics, and optimization strategies was conducted.
By exploring various analytical approaches and tools, we endeavour to offer insights into
optimizing vehicle assignments and mitigating carbon emissions effectively. This section
serves as a foundational exploration into understanding and addressing key challenges in TLS's
quest for carbon neutrality and operational excellence.
1.3.1 Data
The data obtained from TLS is shown in Table 2 as follows. This data is collected from TLS
fleet management systems. There are 10 trucks varies from 6 to 20 years old. In this data, theinformation on the type of roads, the distance and fuel used are given. Based on theseinformation, fuel consumption and fuel efficiency are calculated using the following formula.
,

Table 2: Data from TLS fleet management system.

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1.3.2

Carbon Emissions Calculation

Using the data in Section 1.3.1 and carbon emission formula in Section 1.2.1, the carbon
emissions for September and October have been calculated and shown in Table 3 and Table 4,
respectively.
Table 3: Carbon emissions in September 2023
Veh. No.

EngineAgeEngineDisplacement

Road
Type

Distance
Sept
(km)

Fuel
Sept (L)BPF6316
BPF6318
BPF6317
BPF6319
WA4314G
W5044Q
W3087A
BJV4101
DEE9884
NBK9957

6
6
6
6
9
10
10
16
18
20

7961
7961
7961
7961
7961
7961
7961
7961
7961
7961

federalfederalhighway
highwayfederalfederalhighwayfederalfederalfederal4366.55335.613772.3
5066.43368.47743.26749.92511.95166.97677.31590
1475.42565.151596.43
1202.92497.742790.7846.8
1682.742510.31

Fuel
Efficiency
Sept(km/L)
2.75
3.62
5.37
3.17
2.8
3.1
2.42
2.97
3.07
3.06

Fuel
Consumption
Sept (L/km)

CarbonEmission(kg)

0.3636
0.27620.18620.31550.3571
0.3226
0.4132
0.33670.32570.32684255.35
3950.116873.33
4283.273224.04
6694.127475.102266.634510.526723.91

Table 4: Carbon emission in October 2023
Veh. No.

EngineAgeEngineDisplacement

Road
Type

Distance
Oct (km)

Fuel
Oct (L)

Fuel
Efficiency
Oct (km/L)

Fuel
Consumption
Oct (L/km)

CarbonEmission(kg)

BPF6316
BPF6318
BPF6317
BPF6319
WA4314G
W5044Q
W3087A
BJV4101
DEE9884
NBK9957

6
6
6
6
9
10
10
16
18
20

7961
7961
7961
7961
7961
7961
7961
7961
7961
7961

federalfederalhighway
highwayfederalfederalhighwayfederalfederalfederal4812.46074.415088.96140.63492.89822.27761.13196.15683.17518.91747.81739.18
3618.2
1809.41305.83451.432763.41097.91518.72324.2

2.75
3.49
4.17
3.39
2.67
2.85
2.81
2.91
3.74
3.24

0.3636
0.28650.23980.29500.37450.35090.35590.3436
0.26740.30864689.904664.589697.424854.523505.889236.317402.05
2943.494072.38
6219.34

The analysis of the data above reveals several key insights into the factors influencing fuelefficiency and carbon emissions. Engine age appears to correlate with fuel efficiency, with older
vehicles generally exhibiting lower efficiency. Road type significantly affects fuel consumption
and carbon emissions, with vehicles traveling on highways demonstrating higher efficiency andlower emissions compared to those on federal roads. Variation in fuel efficiency among vehicles
suggests the influence of factors such as driving behavior and maintenance practices.

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1.3.3 Fuel Efficiency and Vehicle Age Analysis
Figure 4 illustrates the fuel efficiency comparison among vehicles during September
and October. The plot also incorporates engine age to examine the correlation between fuelefficiency and engine age. The data reveals that while newer vehicles generally exhibit higher
fuel efficiency, there are instances where older vehicles, such as NBK9957 (20 years old),
outperform newer counterparts like BPF6316 (6 years old) in terms of fuel efficiency.

Figure 4: Fuel efficiency for September and October compared with the vehicle age.
This is further proved through correlation test as shown in Figure 5.

Figure 5: Correlation test between fuel efficiency in September and October with the vehicleage.
The negative correlation coefficient values for both results suggest a very weak negative
correlation between engine age and fuel efficiency. In other words, there appears to be a slighttendency for fuel efficiency to decrease slightly as the engine age increases, but the relationshipis extremely weak. This means that engine age alone may not be a significant predictor of fuelefficiency for the medium and heavy-duty vehicles being studied. Other factors, such as
maintenance practices, driving conditions, and vehicle technology, likely have a more
substantial influence on fuel efficiency than engine age alone. Therefore, while there may be aminor trend indicating decreased fuel efficiency with older engines, it is not a strong enoughrelationship to draw definitive conclusions about the impact of engine age on fuel efficiency in
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20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

this context. Additionally, given the relatively small sample size, the results may lack accuracy
and robustness.
1.3.4

Carbon Emissions and Fuel Efficiency Analysis

Fuel efficiency refers to the amount of energy extracted from a fuel source relative tothe amount of fuel consumed during operation. Vehicles with higher fuel efficiency can travel
longer distances using the same amount of fuel, resulting in reduced fuel consumption per unitof distance or work done. On the other hand, carbon emission refers to the release of carbon
dioxide (CO2) and other greenhouse gases into the atmosphere as a byproduct of fuelcombustion.
In theory, there exists a direct relationship between fuel efficiency and carbon emission
where higher fuel efficiency typically leads to lower carbon emission. This is because vehicles
that can extract more energy from a given amount of fuel produce fewer emissions per unit of
distance travelled. However, the relationship is not always straightforward. Various factors caninfluence carbon emission independently of fuel efficiency, such as engine technology, fueltype, vehicle weight, driving conditions, and maintenance practices. Additionally,
improvements in fuel efficiency alone may not necessarily result in proportional reductions incarbon emission if other factors influencing emissions remain unchanged.
Figure 6a) and 6b) show the relationship between carbon emissions and fuel efficiency
for September and October 2023 in TLS.

Figure 6a): Carbon emission and fuelefficiency in September 2023

Figure 6b): Carbon emission and fuelefficiency in October 2023

It is observed in Figure 6 that there is variability in both fuel efficiency and carbon
emission values across the sampled vehicles. Upon examining the relationship between fuelefficiency and carbon emission, no clear linear trend emerges. While some vehicles with higher
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fuel efficiency exhibit lower carbon emissions, this pattern is not consistent across all vehicles.
This suggests that factors beyond fuel efficiency alone play a role in determining carbon
emission levels, such as vehicle technology, driving conditions, and maintenance practices.
Further analysis may be necessary to fully understand the complex relationship between fuelefficiency and carbon emission in medium and heavy-duty vehicles.
In practice, achieving significant reductions in carbon emission often requires amultifaceted approach that includes improving fuel efficiency, adopting cleaner fuel sources,
implementing emission control technologies, optimizing vehicle design, and promoting
sustainable driving practices. By addressing these factors holistically, it is possible to maximize
fuel efficiency while minimizing carbon emission, leading to more environmentally friendly
and sustainable transportation systems.
1.3.5 Vehicle Assignments Analysis
Presently, TLS has devised a three-year strategy to meet the carbon emission challenges.
This plan includes pre-determined vehicle load, routing, delivery schedule and staff assignment.
However, based on the sample vehicle data from TLS as shown in Table 5 and Table 7 below,
high fuel efficiency vehicle is assigned to the shorter distance. Therefore, alternative measures
must be explored to address the impact of these pre-determined parameters on fuel consumption
and emissions. One potential strategy involves optimizing vehicle assignments. It is anticipatedthat by reassigning fuel efficient vehicles to the longer routes, can help reduce fuel consumption
and emissions associated with transporting goods.

Table 5: Data from TLS in September 2023
SEPTEMBE
R

Distance(km)

Fuel(Litre)Fuel
Consumption(Litre/km)

BPF6316
WA4314GWYR2942
NBK9957
BJV4101
BPF6318
W5044QBPF6317
BPF6319
W3087A

4366.50
3368.407433.407677.302511.905335.60
7743.2013772.30
5066.406749.90

1590.00
1202.902790.702510.31846.801475.402497.74
2565.151596.43
2790.70

0.364
0.357
0.375
0.327
0.337
0.277
0.323
0.186
0.315
0.413

Fuel
Efficiency(km/Litre
)
2.75
2.80
2.66
3.06
2.97
3.62
3.10
5.37
3.17
2.42

DEE9884

5166.901682.74

0.326

3.07

Total Carbon Emitted

121

Total
CarbonEmission(kg)
4261.203223.77
7479.086727.632269.423954.076693.946874.60
4278.43
7479.084509.74
57750.97

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20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

By prioritizing the assignment of the most fuel-efficient vehicles to cover longer distances,
potential gains in operational efficiency and environmental sustainability could be achieved.
Table 6 presents the proposed vehicle reassignment plan, organized from the most fuel-efficient
vehicles to the least. Through this reassignment strategy, a notable reduction in total carbon
emissions of 2.96% was attained, decreasing from 57,750.97 kg to 56,039.83 kg. This outcome
underscores the efficacy of proactive measures such as vehicle reassignment in mitigating
environmental impact while enhancing operational performance within the logistics framework.
Table 6: Vehicle reassignment in September 2023
SEPTEMBER

Distance(km)

BPF6317
BPF6318
BPF6319
W5044Q
DEE9884
NBK9957
BJV4101
WA4314G
BPF6316
WYR294213772.30
7743.207677.30
7433.406749.905335.60
5166.905066.40
4366.50
3368.40

Fuel
Consumption(Litre/km)
0.186
0.277
0.315
0.323
0.326
0.327
0.337
0.357
0.364
0.375

W3087A

2511.90

0.413

Fuel Efficiency(km/Litre)

Total Carbon
Emission (kg)

5.37
3.62
3.17
3.10
3.07
3.06
2.97
2.80
2.75
2.66

6874.60
5738.286483.266426.125891.414675.604668.134848.874261.203389.102.422783.25

Total Carbon Emitted
Carbon Reduction

56039.83
1711.15

Table 7: Data from TLS in October 2023
OCTOBER

Distance(km)

BPF6316
WA4314GWYR2942
NBK9957
BJV4101
BPF6318
W5044QBPF6317
BPF6319
W3087A
DEE9884

4812.40
3492.806710.207518.903196.106074.409822.2015088.90
6140.60
7761.105683.10

Fuel
Efficiency(km/Litre)
1747.802.75
1305.80
2.67
1590.644.22
2324.2
3.24
1097.90
2.91
1739.183.49
3451.432.85
3618.2
4.17
1809.43.39
2763.402.81
1518.7
3.74
Total Carbon Emitted
Fuel(Litre)122

Fuel Consumption(Litre/km)

Total Carbon
Emission (kg)

0.363
0.374
0.237
0.309
0.344
0.286
0.351
0.240
0.295
0.356
0.267

4684.103499.544262.926228.862942.374661.00
9249.839696.784849.197405.914070.1261550.62

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20-22 November, 2023, UTM KUALA LUMPUR, Malaysia

In a parallel investigation conducted using data from October as shown in Table 7, thetotal carbon emissions were computed to be 61,550.62 kg. Following the implementation ofvehicle reassignments, aimed at optimizing efficiency and reducing emissions, the new total
carbon emissions were significantly decreased to 59,749.96 kg, representing a notable reductionof approximately 2.93%. This data is presented in Table 8, showcasing the effectiveness ofstrategic vehicle reallocation in achieving environmental sustainability goals within thelogistics operation. Through such initiatives, not only can carbon emissions be curtailed, but
operational efficiency can also be enhanced, exemplifying the potential benefits of proactive
measures in the pursuit of a greener and more efficient transportation landscape.
Table 8: Vehicle reassignment in October 2023
OCTOBE
R

Fuel Efficiency(km/Litre)

Fuel Consumption(Litre/km)

WYR2942

4.22

0.237

BPF6317
DEE9884
BPF6318
BPF6319
NBK9957
BJV4101
W5044Q
W3087ABPF6316
WA4314G

4.17
0.240
3.74
0.267
3.49
0.286
3.39
0.295
3.24
0.309
2.91
0.344
2.85
0.351
2.81
0.356
2.75
0.363
2.67
0.374
Total Carbon Emitted
Carbon Reduction

Distance(km)
15088.909822.207761.107518.906710.206140.60
6074.405683.104812.40
3492.803196.10

Total Carbon
Emission (kg)9585.816312.175558.34
5769.395299.00
5087.045592.175351.934592.163399.68
3202.2759749.96
1800.66

1.4 Tools for Measuring the Amount of Carbon Emitted
A discussion was also held regarding the methodology for quantifying carbon emissions
from medium and heavy-duty vehicles. It was observed that there are currently no available
tools specifically designed for this purpose. Instead, the carbon emission formula was deemed
adequate for measurement.
Additionally, there is a contention regarding the existence of a tool aimed at reducing
carbon emissions. However, it has only undergone testing with small vehicles, and there is noevidence supporting its effectiveness for medium and heavy-duty vehicles.

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1.5 Conclusion and Recommendations
In conclusion, our investigation into carbon emissions and fuel efficiency in medium andheavy-duty vehicles has provided valuable insights into strategies for reducing environmental
impact while enhancing operational efficiency within the logistics sector. Analysis of the data
revealed a correlation between fuel efficiency and vehicle age, albeit with a weak negative
correlation, indicating that newer vehicles tend to exhibit slightly higher fuel efficiency.
Additionally, while no clear linear relationship was observed between carbon emissions andfuel efficiency, it was evident that factors beyond fuel efficiency alone influence emissions
levels.
Our findings show the importance of proactive measures such as vehicle reassignment, route
optimization, and the adoption of cleaner fuels to mitigate carbon emissions. By prioritizing theassignment of the most fuel-efficient vehicles to cover longer distances, significant reductions
in carbon emissions were achieved, highlighting the potential impact of strategic planning on
environmental sustainability.
Furthermore, driving behaviour emerged as a critical factor influencing fuel efficiency andcarbon emissions. Gentle acceleration, smooth driving, and maintaining optimal speeds were
found to contribute to fuel savings and reduced emissions. As such, investing in driver training
programs and promoting eco-driving behaviours can play a pivotal role in achieving emission
reduction targets while optimizing fuel efficiency. Providing incentives based on efficientdriving style could also serve as a method to instill this value among drivers.
Considering these findings, we recommend the implementation of comprehensive
mitigation strategies that encompass vehicle reassignment, route optimization, adoption ofcleaner fuels, and driver training initiatives. By adopting a multifaceted approach to address thecomplex interplay between fuel efficiency, carbon emissions, and driving behaviour, logistics
companies such as TLS can achieve significant reductions in their carbon footprint while
simultaneously enhancing operational efficiency and sustainability.
1.6 Acknowledgements
This work was supported by Institute of Mathematics for Industry, Joint Usage/ResearchCenter in Kyushu University. (FY2023 Workshop (I) “MMISG2023” (2023b003).)124

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REFERENCES
Clark, N. N., Kern, J. M., Atkinson, C. M. & Nine, R. D. 2002. Factors Affecting Heavy-Duty Diesel Vehicle
Emissions. Journal of the Air & Waste Management Association, 52, 84 - 94.
EEA 2023. The European Handbook of Emission Factor for Road Transportation. In: Agency, E. E. (ed.).
International Energy Agency- Iea, P. 2019. CO2 Emissions From Fuel Combustion 2019 Edition.
MIDA, M. I. D. A. 2023. Carbon Pricing: Path Towards Carbon Neutral Growth In Malaysia [Online].
Available: https://www.mida.gov.my/carbon-pricing-path-towards-carbon-neutral-growth-inmalaysia/ [Accessed 18/12/2023 2023].
Ministry of Economy. 2022. National Energy Policy 2022-2040 [Online]. Available:
https://www.ekonomi.gov.my/sites/default/files/2022-09/National_Energy_Policy_20222040.pdf [Accessed].
NEP 2022. National Energy Policy 2022-2040. In: Economic, M. O. (ed.).

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OUTPUT PRESENTATION SLIDES

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Trends and changes in Chili prices������������������������������������������������������������������������������������
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1.3.2 Identifying significant factors that influence chili prices�������������������������������������������������������������������������������������������� ���� ���������������� �������� ���������� ��� ���� ����������� ��� ����� ������� ���� �������������� ���������� ���� ��������� ��� ���� ���������� ���� ������� ���� ����������
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1.6 Acknowledgement������������������������������������������������������������������������������������������
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REFERENCES
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MI レクチャーノートシリーズ刊行にあたり
本レクチャーノートシリーズは、文部科学省 21 世紀 COE プログラム「機能数理学の構築と展開」（H15-19 年度）において作成した COE Lecture Notesの続刊であり、文部科学省大学院教育改革支援プログラム「産業界が求める数学博士と新修士養成」（H19-21 年度）および、同グローバル COE プログラ
ム「マス・フォア・インダストリ教育研究拠点」（H20-24 年度）において行われた講義の講義録として出版されてきた。平成 23 年 4 月のマス・フォア・イ
ンダストリ研究所（IMI）設立と平成 25 年 4 月の IMI の文部科学省共同利用・共同研究拠点として「産業数学の先進的・基礎的共同研究拠点」の認定を受け、今後、レクチャーノートは、マス・フォア・インダストリに関わる国内外の研究者による講義の講義録、会議録等として出版し、マス・フォア・インダ
ストリの本格的な展開に資するものとする。
2022 年 10 月
マス・フォア・インダストリ研究所
所長

梶原 健司

International Project Research-Workshop (I)Proceedings of 4th Malaysia Mathematics inIndustry Study Group (MMISG 2023)
発 行
編集長
編 集

発

行

印

刷

2024年 3 月 28 日
Zaitul Marlizawati Zainuddin, Arifah BaharS hariffah Suhaila Syed Jamaludin, Zaiton Mat Isa, Nur Arina Bazilah Aziz,
Taufiq Khairi Ahmad Khairuddin, Shaymaa M.H. Darwish, Ahmad Razin Zainal Abidin,
Norhaiza Ahmad, Zainal Abdul Aziz, Hang See Pheng, Mohd Ali Khameini Ahmad
九州大学マス・フォア・インダストリ研究所
九州大学大学院数理学府〒819-0395 福岡市西区元岡744
九州大学数理・IMI 事務室
TEL 092-802-4402 FAX 092-802-4405
URL https://www.imi.kyushu-u.ac.jp/
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TEL 092-531-7102 FAX 092-524-4411

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穴井 宏和
福本 康秀

平成23年度 数学・数理科学と諸科学・産業との連携研究ワーク February 20, 2012
ショップ 拡がっていく数学 ～期待される “見えない力” ～154pages隆
卓
文生

157pages

128pages

140pages

72pages

210pages

October 28, 2011

November 30, 2011

January 27, 2012

シリーズ既刊
Issue

Author／Editor

Title

Published

COE Lecture Note Vol.38

Fumio Hiroshima
Itaru Sasaki
Herbert Spohn
Akito Suzuki

Enhanced Binding in Quantum Field Theory

COE Lecture Note Vol.39

Institute of Mathematics
for Industry,
Kyushu University

Multiscale Mathematics: Hierarchy of collective phenomena andinter­relations between hierarchical structures 180pages

March 13, 2012

COE Lecture Note Vol.40

井ノ口順一
太田 泰広
筧
三郎
梶原 健司
松浦
望

離散可積分系・離散微分幾何チュートリアル2012

March 15, 2012

COE Lecture Note Vol.41

Institute of Mathematics
for Industry,
Kyushu University

Forum “Math-for-Industry” 2012
“Information Recovery and Discovery”

COE Lecture Note Vol.42

佐伯
若山
山本

Study Group Workshop 2012 Abstract, Lecture & Report

178pages

November 19, 2012

COE Lecture Note Vol.43

Institute of Mathematics
for Industry,
Kyushu University

Combinatorics and Numerical Analysis Joint Workshop

103pages

December 27, 2012

COE Lecture Note Vol.44

萩原学

モダン符号理論からポストモダン符号理論への展望

107pages

January 30, 2013

COE Lecture Note Vol.45

金山

寛

Joint Research Workshop of Institute of Mathematics for Industry(IMI), Kyushu University“Propagation of Ultra-large-scale Computation by the Domaindecomposition-method for Industrial Problems (PUCDIP 2012)”
121pages

February 19, 2013

COE Lecture Note Vol.46

西井 龍映
栄 伸一郎
岡田 勘三
落合 啓之
小磯 深幸
斎藤 新悟
白井 朋之

科学・技術の研究課題への数学アプローチ―数学モデリングの基礎と展開― 325pages

February 28, 2013

COE Lecture Note Vol.47

SOO TECK LEE

BRANCHING RULES AND BRANCHING ALGEBRAS FOR THE
COMPLEX CLASSICAL GROUPS 40pages

March 8, 2013

COE Lecture Note Vol.48

溝口
脇
平坂
谷口
島袋

博多ワークショップ「組み合わせとその応用」 124pages

March 28, 2013

修
正人
昌宏

佳寛
隼人
貢
哲至
修

204pages

March 12, 2012

152pages

October 22, 2012

91pagesシリーズ既刊
Issue

Author／Editor

COE Lecture Note Vol.49

照井
小原
濱田
横山
穴井
横田

MI Lecture Note Vol.50

章
功任
龍義
俊一
宏和
博史

Title

Published

マス・フォア・インダストリ研究所 共同利用研究集会 II
数式処理研究と産学連携の新たな発展 137pages

August 9, 2013

Ken Anjyo
Hiroyuki Ochiai
Yoshinori Dobashi
Yoshihiro Mizoguchi
Shizuo Kaji

Symposium MEIS2013:
Mathematical Progress in Expressive Image Synthesis

October 21, 2013

MI Lecture Note Vol.51

Institute of Mathematics
for Industry, Kyushu
University

Forum “Math-for-Industry” 2013
“The Impact of Applications on Mathematics”

MI Lecture Note Vol.52

佐伯
岡田
髙木
若山
山本

Study Group Workshop 2013 Abstract, Lecture & Report
142pages

November 15, 2013

MI Lecture Note Vol.53

四方 義啓
櫻井 幸一
安田 貴徳
Xavier Dahan

平成25年度 九州大学マス・フォア・インダストリ研究所
共同利用研究集会 安全・安心社会基盤構築のための代数構造～サイバー社会の信頼性確保のための数理学～ 158pages

December 26, 2013

MI Lecture Note Vol.54

Takashi Takiguchi
Hiroshi Fujiwara

Inverse problems for practice, the present and the future

January 30, 2014

MI Lecture Note Vol.55

栄 伸一郎
溝口 佳寛
脇
隼人
渋田 敬史

Study Group Workshop 2013 数学協働プログラム Lecture & Report
98pages

February 10, 2014

MI Lecture Note Vol.56

Yoshihiro Mizoguchi
Hayato Waki
Takafumi Shibuta
Tetsuji Taniguchi
Osamu Shimabukuro
Makoto Tagami
Hirotake Kurihara
Shuya Chiba

Hakata Workshop 2014
~ Discrete Mathematics and its Applications ~March 28, 2014

MI Lecture Note Vol.57

Institute of Mathematics
for Industry, Kyushu
University

Forum “Math-for-Industry” 2014:“Applications + Practical Conceptualization + Mathematics = fruitful
Innovation” 93pages

October 23, 2014

MI Lecture Note Vol.58

安生健一
落合啓之

Symposium MEIS2014:
Mathematical Progress in Expressive Image Synthesis

November 12, 2014

修
勘三
剛
正人
昌宏

154pages

October 30, 2013

97pages

93pages

141pages

135pagesシリーズ既刊
Issue

Author／Editor

MI Lecture Note Vol.59

西井
岡田
梶原
髙木
若山
脇
山本

龍映
勘三
健司
剛
正人
隼人
昌宏

MI Lecture Note Vol.60

西浦博

MI Lecture Note Vol.61

Title

Published

Study Group Workshop 2014 数学協働プログラム
Abstract, Lecture & Report 196pages

November 14, 2014

平成26年度九州大学 IMI 共同利用研究・研究集会（I）
感染症数理モデルの実用化と産業及び政策での活用のための新たな展開 120pages

November 28, 2014

溝口 佳寛
Jacques Garrigue
萩原
学
Reynald Affeldt

研究集会
高信頼な理論と実装のための定理証明および定理証明器
Theorem proving and provers for reliable theory and implementations(TPP2014) 138pages

February 26, 2015

MI Lecture Note Vol.62

白井朋之

Workshop on “β-transformation and related topics”

March 10, 2015

MI Lecture Note Vol.63

白井朋之

Workshop on “Probabilistic models with determinantal structure”
107pages

August 20, 2015

MI Lecture Note Vol.64

落合
土橋

啓之
宜典

Symposium MEIS2015:
Mathematical Progress in Expressive Image Synthesis

September 18, 2015

MI Lecture Note Vol.65

Institute of Mathematics
for Industry, Kyushu
University

Forum “Math-for-Industry” 2015
“The Role and Importance of Mathematics in Innovation”

MI Lecture Note Vol.66

岡田 勘三
藤澤 克己
白井 朋之
若山 正人
脇
隼人
Philip Broadbridge
山本 昌宏

Study Group Workshop 2015 Abstract, Lecture & Report
156pages

MI Lecture Note Vol.67

Institute of Mathematics
for Industry, Kyushu
University

IMI-La Trobe Joint Conference“Mathematics for Materials Science and Processing”
66pages

February 5, 2016

MI Lecture Note Vol.68

古庄
小谷
新甫

英和
久寿
洋史

結び目と Grothendieck-Teichmüller 群
116pages

February 22, 2016

MI Lecture Note Vol.69

土橋
鍛治

宜典
静雄

Symposium MEIS2016:
Mathematical Progress in Expressive Image Synthesis

MI Lecture Note Vol.70

Institute of Mathematics
for Industry,
Kyushu University

Forum “Math-for-Industry” 2016
“Agriculture as a metaphor for creativity in all human endeavors”
98pagesNovember 2, 2016

MI Lecture Note Vol.71

小磯
二宮
山本

Study Group Workshop 2016 Abstract, Lecture & Report

November 21, 2016

深幸
嘉行
昌宏

59pages

124pages

74pages

October 23, 2015

November 5, 2015

82pages

143pages

October 24, 2016

シリーズ既刊
Issue

Author／Editor
朝雄
泉
文生

Title

Published

MI Lecture Note Vol.72

新井
小嶋
廣島

MI Lecture Note Vol.73

穴田 啓晃
Kirill Morozov
須賀 祐治
奥村 伸也
櫻井 幸一

Secret Sharing for Dependability, Usability and Security of Network
Storage and Its Mathematical Modeling 211pages

March 15, 2017

MI Lecture Note Vol.74

QUISPEL, G. Reinout W.
BADER, Philipp
MCLAREN, David I.
TAGAMI, Daisuke

IMI-La Trobe Joint Conference
Geometric Numerical Integration and its Applications

March 31, 2017

MI Lecture Note Vol.75

手塚
田上
山本

Study Group Workshop 2017 Abstract, Lecture & Report

MI Lecture Note Vol.76

宇田川誠一

Tzitzéica 方程式の有限間隙解に付随した極小曲面の構成理論―Tzitzéica 方程式の楕円関数解を出発点として― 68pages

August 4, 2017

MI Lecture Note Vol.77

松谷 茂樹
佐伯
修
中川 淳一
田上 大助
上坂 正晃
Pierluigi Cesana
濵田 裕康

平成29年度 九州大学マス・フォア・インダストリ研究所
共同利用研究集会（I）
結晶の界面，転位，構造の数理 148pages

December 20, 2017

MI Lecture Note Vol.78

瀧澤 重志
小林 和博
佐藤憲一郎
斎藤
努
清水 正明
間瀬 正啓
藤澤 克樹
神山 直之

平成29年度 九州大学マス・フォア・インダストリ研究所
プロジェクト研究 研究集会（I）
防災・避難計画の数理モデルの高度化と社会実装へ向けて
136pages

February 26, 2018

MI Lecture Note Vol.79

神山
畔上

平成29年度 AIMaP チュートリアル
最適化理論の基礎と応用 96pages

February 28, 2018

MI Lecture Note Vol.80

Kirill Morozov
Hiroaki Anada
Yuji Suga

IMI Workshop of the Joint Research Projects
Cryptographic Technologies for Securing Network Storage
and Their Mathematical Modeling 116pages

March 30, 2018

MI Lecture Note Vol.81

Tsuyoshi Takagi
Masato Wakayama
Keisuke Tanaka
Noboru Kunihiro
Kazufumi Kimoto
Yasuhiko Ikematsu

IMI Workshop of the Joint Research Projects
International Symposium on Mathematics, Quantum Theory,
and Cryptography 246pages

September 25, 2019

MI Lecture Note Vol.82

池森

令和 2 年度 AIMaP チュートリアル
新型コロナウイルス感染症にかかわる諸問題の数理
145pages

March 22, 2021

集
大助
昌宏

直之
秀幸

俊文

Mathematical quantum field theory and related topics

133pages

71pages

118pages

January 27, 2017

October 20, 2017

シリーズ既刊
Issue

Author／Editor

Title

Published

MI Lecture Note Vol.83

早川健太郎
軸丸 芳揮
横須賀洋平
可香谷 隆
林
和希
堺
雄亮

シェル理論・膜理論への微分幾何学からのアプローチとその建築曲面設計への応用 49pages

July 28, 2021

MI Lecture Note Vol.84

Taketoshi Kawabe
Yoshihiro Mizoguchi
Junichi Kako
Masakazu Mukai
Yuji Yasui

SICE-JSAE-AIMaP Tutorial
Advanced Automotive Control and Mathematics

December 27, 2021

MI Lecture Note Vol.85

Hiroaki Anada
Yasuhiko Ikematsu
Koji Nuida
Satsuya Ohata
Yuntao Wang

IMI Workshop of the Joint Usage Research Projects
February 9, 2022
Exploring Mathematical and Practical Principles of Secure Computationand Secret Sharing 114pages

MI Lecture Note Vol.86

濱田 直希
穴井 宏和
梅田 裕平
千葉 一永
佐藤 寛之
能島 裕介
加葉田雄太朗
一木 俊助
早野 健太
佐伯
修

2020年度採択分 九州大学マス・フォア・インダストリ研究所
共同利用研究集会
進化計算の数理 135pages

February 22, 2022

MI Lecture Note Vol.87

Osamu Saeki,
Ho Tu Bao,
Shizuo Kaji,
Kenji Kajiwara,
Nguyen Ha Nam,
Ta Hai Tung,
Melanie Roberts,
Masato Wakayama,
Le Minh Ha,
Philip Broadbridge

Proceedings of Forum “Math-for-Industry” 2021
-Mathematics for Digital Economy- 122pages

March 28, 2022

MI Lecture Note Vol.88

Daniel PACKWOOD
Pierluigi CESANA,
Shigenori FUJIKAWA,
Yasuhide FUKUMOTO,
Petros SOFRONIS,Alex STAYKOVPerspectives on Artificial Intelligence and Machine Learning inMaterials Science, February 4-6, 2022 74pages

November 8, 2022

110pagesシリーズ既刊
Issue

Author／Editor

Title

Published

MI Lecture Note Vol.89

松谷 茂樹
落合 啓之
井上 和俊
小磯 深幸
佐伯
修
白井 朋之
垂水 竜一
内藤 久資
中川 淳一
濵田 裕康
松江
要
加葉田雄太朗2022年度採択分 九州大学マス・フォア・インダストリ研究所
共同利用研究集会
材料科学における幾何と代数 III 356pages

December 7, 2022

MI Lecture Note Vol.90

中山
谷川
品野
近藤
石原
鍛冶
藤澤

尚子
拓司
勇治
正章
亨
静雄
克樹

2022年度採択分 九州大学マス・フォア・インダストリ研究所
共同利用研究集会
データ格付けサービス実現のための数理基盤の構築 58pages

December 12, 2022

MI Lecture Note Vol.91

Katsuki Fujisawa
Shizuo Kaji
Toru Ishihara
Masaaki Kondo
Yuji Shinano
Takuji Tanigawa
Naoko Nakayama

IMI Workshop of the Joint Usage Research Projects
Construction of Mathematical Basis for Realizing Data Rating Service
610pages

December 27, 2022

MI Lecture Note Vol.92

丹田
三宮
廣島

2022年度採択分 九州大学マス・フォア・インダストリ研究所
共同利用研究集会
時間・量子測定・準古典近似の理論と実験〜古典論と量子論の境界〜 150pages

Janualy 6, 2023

MI Lecture Note Vol.93

Philip Broadbridge
Luke Bennetts
Melanie Roberts
Kenji Kajiwara

Proceedings of Forum “Math-for-Industry” 2022
-Mathematics of Public Health and Sustainability-

June 19, 2023

MI Lecture Note Vol.94

國廣
池松
伊豆
穴田
縫田

2023年度採択分 九州大学マス・フォア・インダストリ研究所
共同利用研究集会
現代暗号に対する安全性解析・攻撃の数理 260pages

Janualy 11, 2024

MI Lecture Note Vol.95

Osamu Saeki
Wojciech Domitrz
Stanisław Janeczko
Marcin Zubilewicz
Michał Zwierzyński

International Project Research-Workshop (I)WORKSHOP on Mathematics for Industry 364pages

March 14, 2024

MI Lecture Note Vol.96

澤田

2023年度採択分 九州大学マス・フォア・インダストリ研究所
共同利用研究集会
デジタル化時代に求められる斜面防災の思考法 70pages

March 18, 2024

聡
俊
文生

昇
泰彦
哲也
啓晃
光司

茉伊

170pages九州大学マス・フォア・インダストリ研究所

九州大学大学院 数理学府
URL http://www.imi.kyushu-u.ac.jp/