United States

Department of

Agriculture

Forest Service

Southeastern Forest

Experiment Station

General Technical Report SE-28

Wood Residue Distribution

Simulator (WORDS)

Douglas A. Eza, James W. McMinn, Petei E. Dress
August 1984

Southeastern Forest Experiment Station

200 Weaver Blvd.

Asheville, North Carolina 28804
Wood Residue Distribution

Simulator (WORDS)

Douylas A. Eza

Research Associate

James W. McMinn

Research Forester

Peter E. Dress

Professor

ABSTRACT

Successful development of woody biomass for energy will depend on the distribution of local supply and demand

within subregions, rather than on the

total inventory of residues. The Wood

Residue Distribution Simulator (WORDS)

attempts to find a least-cost allocation

of residues from local sources of supply

to local sources of demand, given the

cost of the materials, their distribution, and the distribution of demand.

The results are useful in evaluating the

feasibility of developing wood energy

either for a subregion in general or for

specific locales. This paper describes

WORDS and gives an example of its application to mill residues in the State of

Georgia.

Keywords: Energy, supply, demand,

Georgia.

Introduction

Interest in the development of wood

energy remains high in the Southeast.

Some States in the region maintain ongoiny programs to promote the use of

noncommercial forest biomass for energy

production. A substantial quantity of

mill residues is generated, and there is

a growing capability for recovering poorquality material that previously has remained in the woods. Hence, residues

for energy application are expected to

be available for the long term with

forest residues replacing mill residues

as the latter decline in availability.

Much of the promotion to date has been

based on the total inventory of potential

wood fuels over fairly large geographical

areas. Such totals are of limited value

to the prospective user because they indicate only in a general way whether

more precise assessments are justified.

Successful development of many resources

depends upon the distribution of local

supply and demand within subregions.

This is especially true of woody biomass

used for energy because of its relatively

low per unit heating value.

Our objective was to develop and evaluate methods for estimating the effective

supply of wood residue fuels over a subregion given the cost of such materials,

their distribution, and the distribution

of the demand for them. The resulting

Wood Residue Distribution Simulator

(WORDS) attempts to find a least-cost

wood residue allocation from local

sources of supply to local points of demand. Simulation output may be used as

a basis for evaluating whether or not

wood residue is a viable energy source

for either the subregion as a whole or

for specific locales within the subregion. This paper describes the WORDS

program and gives an example and evaluation of its use, with the State of

Georgia as a pilot area.

Eza and Dress are with the School of Forest Resources, University of Georgia, Athens, GA; McMinn

is with the Southeastern Forest Experiment Station,

USDA Forest Service, Athens, GA.
The Simulator

Two versions of WORDS have been developed, one for use in batch mode, the

other for interactive mode on a computer

TSO terminal. Both versions have three

distinct segments. The first segment

summarizes uncommitted wood residues and

calculates average costs and .energy values for those residues. The second computes cost-effective shipping distances

as a function of a constant wood energy

value, a schedule of .shipping costs, and

a variablle wood residue purchase price.

This segment also calculates the potential cost-effective supply available to

each demand source. The third segment

derives the allocation of wood residues

from the sources of supply to the

sources of demand, assuming that the

demand sources compete for supply pools

common to them.

Limitations on WORDS are inherent primarily in the dimensions of arrays.

These can be readily altered to increase

the simulator's capacity. "Supply unit"

and "demand unit" are the terms used to

denote locations from which and to which

residues might be shipped. WORDS can

accommodate up to 160 units for both

supply and demand. Because a subregion

may have many more actual supply and

demand sources, it may be necessary to

aggregate sources so that the total units

of each is 160 or less. Thus, a particular demand or supply unit might be a

composite of several. supply or demand

sources. To calculate transportation

distances, the weighted geographical

center of the several sources would be

the ideal location of the aggregate

unit. In practice, however, it may be

necessary to use some other location,

such as city centers or county seats, so

that transportation distance values can

be derived at reasonable cost.

"Wood residues" denotes any of a number of similar materials, usually with

different heating characteristics and

different costs. WORDS can allocate any

combination of residues up to eight.

Data on each individual residue are inputs to the simulator, and the program

calculates aggregate values for the userspecified combination.

A "cost-effective" wood fuel is defined

as having a lower delivered cost per unit

energy than an alternate energy source.

We wish to emphasize strongly that an

accurate estimate of the feasibility of

using wood residues for energy must incorporate other costs in addition to the

delivered cost of fuel. For example,

storage and handling costs are inherently greater because wood residues are

bulkier than fossil fuels. Some measure

of conversion costs (such as capital cost

amortized over the life of the woodfueled system) must also be considered.

Also, wood-fueled systems are more expensive to maintain than gas- or oilfired systems.' Costs such as these

must be included when estimating the

overall feasibility of wood residues as

an energy source. Currently, WORDS does

not incorporate these costs, but it has

been structured so that they can be

easily added.

The three simulator segments are in

the form of three computer programs, as

follows:

Residue Summary Program

Because residue types vary in purchase

price and energy value, it is important

to be able to evaluate each type separately and in combination with others.

A description of each supply unit's

wood residue inventory is required.

This description consists of up to eight

categories of wood residues (such as

lKarchesy, Joseph; Koch, Peter. Energy production

from hardwoods growing .on southern pine sites.

Gen. Tech. Rep. SO-24. New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern

Forest Experiment Station; 1979. 59 p.

2
hardwood bark, softwood chips, etc.),

the quantity available in each category,

the energy value of each type of residue

in millions of Btu's per ton, and the

cost of each residue type in dollars per

ton. After the user specifies some desired combination of residue types, the

program calculates, for each supply

source, the total residue available, the

average price of the residue, and the

average heating value of the residue.

These values are printed out in a report

and stored in an external file for use

in subsequent segments.

The program also calculates the maximum distance the given combination of

residues can be shipped, with costeffectiveness as a constraint. First,

the user must develop a subroutine which,

given a transport mileage, will return

the cost of shipping 1 ton of residues

that number of miles. The delivered

cost of some alternate energy source

(the "break-even" cost) must also be

specified. By using the systemwide

average heating value of residues, the

program creates a table of the maximum

cost-effective residue shipping distances as a function of purchase price,

ranging from user-supplied minimum to

maximum in 50-cent increments. Because

shipping costs are likely'to be on a

graduated scal'e, the computations are

performed iteratively. For each price

increment, delivered residue costs (in

dollars per million Btu's) are calculated

for transportation distance increments

until the cost exceeds some user-supplied

alternate fuel .delivered cost. The last

distance used before'the break-even cost

is exceeded is considered the maximum

cost-effective shipping distance for

that particular residue cost. To give

the user some reference values, both the

average systemwide cost of residue and

the average sysstemwide unit Btu content

of the residue are reported.

Potential Supply Availability

Program

The second segment uses calculations

from then first segment to determine the

supply of wood residue potentially available to each demand unit, with costeffectiveness as a constraint and competition among demand units for common

supply pools ignored.

With demand unit-supply unit distances

and the transportation cost function,

the program calculates a per million Btu

transportation cost between every demand

unit and every supply unit. It adds to

this the purchase price of the residue

at the supply unit, then compares that

cost to the break-even cost at the demand source;'every transfer that results

in a cost lower than break-even is designated as cost-effective. The breakeven cost may be different for each

demand unit because a unique alternate

fuel cost may be specified for each

demand unit.

The program sums the total cost-effective supply for each demand source and

prints out this sum as the supply potentially available to the demand source.

At the discretion of the user, every

cost-effective supply-demand transfer

can be reported as well. The program

also sums all supply and demand in the

system, compares the two, and reports

whether or not a deficit exists. Finally, supply and demand files are created

for inputs to the Residue Allocation

Program.

Residue Allocation Program

This program is adapted from a recreation distribution simulation model developed by Dress and Devine' and allocates

wood residues from supply units to demand units, all of which are competing

*Dress, Peter IE.; Devine, Hugh A., Jr. A simulation model for the estimation of unmet recreation

facilities demand. Georgia Comprehensive Recreation Plan (GORP). Atlanta, GA: Georgia Department

of Natural Resources; unpublished paper on file;

1978.

3
for the supply. The object is to approximate a least-cost allocation through a

simulation procedure. Although linear

programming could be used to find the

optimal (i.e., the absolute least-cost)

allocation, linear programming is expensive for large supply-demand systems.

The simulation procedure approximates

the optimal solution at a fraction of

the cost. Dress and Devine3 provide

extensive documentation on the basic

model, so our discussion is limited to

its application to wood residue allocation. An optimal systemwide allocation

is, in general, the one that satisfies

as much demand as possible while minimizing the cost of doing so. In standard

optimization terminology, our problem is

to minimize total costs of allocating

wood residues, subject to the constraints

that either all demand is satisfied, or

all supply is used, or both.

In the wood residue model each supplydemand transfer is assigned to a preference class, based on the delivered cost

of fuel for the transfer. The user specifies the number of preference classes

to be assigned and the cost range for

each class. The program computes the

purchase price plus transport cost for

every potential supply-demand transfer

and, based on the class boundaries, assigns each transfer to a preference

class. Preference class 1 (the highest)

will contain all the least expensive

transfers; preference class 2, the next

least expensive; and so forth.

Allocation is made in increments, the

size of which are specified by the user.

In general, smaller increments provide

closer-to-optimal solutions but at a

higher cost in computer time. The program allocates an increment of supply to

a demand unit from every supply unit in

the demand unit's preference class 1.

It then proceeds to another demand unit,

repeating the procedure. When all demand

units have received their first increment

allocations, the program returns to the

first demand unit to allocate the second

increment. This iterative process continues until all possible allocations in

preference class 1 have been made. Normally this will occur either when demands

have been satisfied or supplies exhausted

along class 1 supply-demand transfer

routes. If, after preference class 1

allocations are made, uncommitted supplies or unsatisfied demands still exist

in the system, the program proceeds to

repeat the above procedure for each successive preference class until all supply is allocated or all demand is satisfied. Allocating by preference class

sequence results in a relatively low-cost

overall allocation, because the lowest

cost allocations (as a group) are made

before more expensive allocations. Allocating within preference classes by

increments simulates competition, because the supply is distributed, bit by

bit, among demand units in the same preference class. When a supply source is

exhausted or a demand ,source satisfied,

no more allocations involving those units

are made, although the procedure continues

for the other units.

Inherent in this type of simulation is

the fact that the quantity allocated to

a given demand unit depends on the order

of that demand unit in the sequence. If

supply within a preference class is exhausted between the first increment allocation in a given iteration and the

last, those demand units receiving the

a!locations first in the iteration will

receive a larger supply at the lower

cost. This is.why a simulation will not

necessarily r"esult in the optimum allocation ensured by a linear program, which

essentially makes simultaneous allocations to all demand units in the propor3Dress, Peter E.; Devine, Hugh A., Jr. Decription

of procedures, concepts and computation software

for the estimation of unmet recreational demands.

Georgia Comprehensive Recreation Plan (GORP).

Atlanta, GA: Georgia Department of Natural R&

sources; unpublished paper on file; 1978.

4
tions that result in least cost. To

reduce this effect, the demand units are

randomly reordered before allocations

within each preference class, eliminating

a consistent automatic bias.

Finally, reports are generated by the

program. The quantity of residue allocated from every supply unit to every

demand unit as well as a list of the

preference class assignment for each

supply-demand transfer are produced as

tables. Another report shows the satisfied and unsatisfied demand of each demand unit as well as the allocated and

unallocated inventory of each supply

unit.

A WORDS Example

Data from 1980 were used to test WORDS.

The residue type consisted of unused mill

residues in the State of Georgia. The

residue production figures were from a

survey conducted periodically by the

Georgia Forestry Commission. Counties

were used as supply units, with county

seats taken as transportation nodes.

Because of the confidential nature of

these supp1.y data, the county supply

codes were randomized and reports listed

only code numbers rather than county

names. Supplies were aggregated by

county and Icomprised eight residue

types--softwood bark, hardwood bark,

softwood shavings, hardwood shavings,

softwood chips, hardwood chips, softwood dust, and hardwood dust.

For approximate energy value calculations, the residues were classified as

either hardwoods or softwoods and as

either bark or other wood fibers. Potential heating values were calculated

by correcting for moisture content as

given by Taras and Clark4 and by estimating available heat according to .Karchesy and Xoch.5 While burning efficiencies vary with equipment, a 70 percent efficiency was used to represent

woodfired systems in general. The resulting available energy values were:

Residues Million Btu/green ton

Softwood bark 7.482

Hardwood bark 6.056

Other softwood 5.789

Other hardwood 5.960

No explicit estimates of demand were

available, so we formulated a hypothetical situation to represent a "real-world"

problem. The assumption was that all

nonelectrically heated public schools

were to be converted to wood-fired heating systems. Estimates of countywide

demand for each county were based on

average annual heating energy use per

student in three climatically stratified

zones.

A schedule of approximate shipping

rates was derived from confidential industry sources in the State:

Miles Dollars/ton-mile

o- 40 0.245

41- 60 .205

61- 80 .195

81- 100 .185

lOO+ .170

The following average residue prices

were derived, again, from several confi4Taras, Michael A.; Clark, Alexander, III. Aboveground biomass of loblolly pine in a natural,

uneven-aged sawtimber stand in central Alabama.

Tappi 58(2): 103-105; 1975.

5See footnote 1.
dential industry sources in the State:

- - Residue Dollars/ton

Softwood and hardwood bark 5.50

Softwood chips 18.00

Hardwood chips 11.00

Softwood and hardwood dust 5.50

Softwood and hardwood 11.00

shavings

Model Parameters

The following parameters were used for

the example solution:

l Minimum and maximum wood residue purchase prices for calculating costeffective shipping distances were $5

and $15 per ton, respectively.

l Three preference classes were used for

supply-demand transfers; class 1, from

$0 to $20; cl ass 2, from $20.01 to $35;

class 3, from $35.01 to $100 per delivered ton of residue.

l The allocation increment was 500 tons,

with the maximum number of iterations

set at 100. This would permit 50,000

(100x50) tons of residue to be shipped

from any supply unit or to any demand

unit in a given preference class. The

object was to limit iterations but to

base the limitations on a sufficiently

large quantity so that either supply

would be exhausted or demand met within a preference class before the maximum number of iterations was reached.

Results and Discussion

Although the problem addressed was hypothetical, the data enabled us to draw

some useful conclusions about both the

simulator's potential use and the development of wood residue as an energy

source.

Cost-effective shipping distances for

wood residues., With an inflated 1980

natural gas price of $0.48 per therm

(100,000 Btu) and an assumed efficiency

of 80 percent for gas-fired equipment,

the estimated alternate fuel cost was

about $6 per million Btu. For our data

set, this resulted in cost-effective

shipping mileages ranging from 137 miles

at a residue price of $15 per ton to 196

miles at a residue purchase price of $5

per ton. As a general reference, the

weighted cost of all residues in the

State at that time was $6.66 per ton.

While transportation costs and wood

residue prices are likely to increase,

these will be offset at least partially

by higher alternate fuel costs. The implication is that over the near term,

mill residues could be a feasible source

of energy even at transfer distances as

great as 100 miles.

Residue availability. Wood residues,

particularly mill residues, will become

scarcer as the demand for less expensive

energy increases. The large amount of

residue existing in Georgia in 1980 suggests, however, that it may be several

years before demand exceeds supply. By

using natural gas as the alternate fuel

source for all Georgia counties, the

WORDS solution indicated that every county in the State has at least 47 other

counties (out of a possible 158) from

which it could potentially draw wood residue under the cost-effective constraint.

Under competition among counties, WORDS

still indicated that, for the hypothetical

problem, the total demand could be satisfied over the entire system and at a

better than break-even cost. No county

would have to pay more than an average

of $4.50 per million Btu, and over 90

percent of the counties could obtain

their residues at a delivered cost of

under $3 ner million Btu.

The model. WORDS appears to be a usefu7 and inexpensive tool to aid in assessing the feasibility of large-scale

use of wood residues for energy. Model

parameters are easy to alter, hence

sensitivity analyses to evaluate the

effects of any changes in price or in

the residue mix are easily made. The

model is versatile enough for large or

small residue supply-demand systems and

with only minor changes can be adapted

to other supply-demand systems as well.

6
r

P - - -

Eza, Douglas A.; McMinn, James W.; Dress, Peter E. I

Wood resi,due distribution simulator (WORDS). Gen. Tech. Rep.

SE-28. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southeastern Forest Experiment Station; 1984. 6 pp. I

Successful development of woody biomass for energy will depend

on the distribution of local supply and demand within subregions,

rather than on the total inventory of residues. The Wood Residue

Distribution Simulator (WORDS) attempts to find a least-cost

allocation of residues from local sources of supply to local

sources of demand, given the cost of the materials, their distribution, and the distribution of demand. The results are use- I

I

ful in evaluating the feasibility of developing wood energy

either for a subregion in general or for specific locales. This

paper describes WORDS and gives an example of its application

to mill residues in the State of Georgia. I

--

Eza, Douglas A.; McMinn, James W.; Dress, Peter E.

Wood residue distribution simulator (WORUS). tien. Tech. Rep.

1

SE-28. Asheville, NC: U.S. Department of Agriculture, forest

Service, Southeastern Forest Experiment Station; 1984. 6 pp.

Successful development of woody biomass for energy will depend

on the distribution of local supply and demand within subregions,

rather than on the total inventory of residues. The Wood Residue

Distribution Simulator (WORDS) attempts to find a least-cost

allocation of residues from local sources of supply to local

sources of demand, given the cost of the materials, their distribution, and the distribution of demand. The results are useful in evaluating the feasibility of developing wood energy

either for a subregion in general or for specific locales. This

paper describes WORDS and gives an example of its application

to mill residues in the State of Georgia.

I

KEYWORDS: Energy, supply, demand, Georgia. L I

KEYWORDS: Energy, supply, demand, Georgia.

- - - ----


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use management of the Nation’s

forest resources for sustained

yields of wood, water, forage,

wildlife, and recreation. Through

forestry research, cooperation

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increasingly greater service to a

growing Nation.

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who believes he or she has been discriminated against in

any USDA-related activity should write immediately to

the Secretary of Agriculture, Washington, D.C. 20250.