NAT'L INST. OF STAND & TECH

A11107 ESTblfl

NBS

PUBLICATIONS

NBS SPECIAL PUBLICATION 657

U.S. DEPARTMENT OF CGMMERCE/National Bureau of Standards

The Resource Recovery

Planning Model: A New

Tool for Solid Waste

Management

|0 0

.US7
NATIONAL BUREAU OF STANDARDS

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OF STANDARDS

The Resource Recovery Planning Model:

a5^

A New Tool for Solid Waste Management "^,1

Robert E. Chapman

Center for Applied Mathematics

National Engineering Laboratory

National Bureau of Standards

U.S. Department of Commerce

Washington, DC 20234

and

Edward B. Berman

Edward B. Berman Associates, Inc.

Marblehead, MA 01945

Sponsored by the

Office of Recycled Materials

National Measurement Laboratory

National Bureau of Standards

U.S. Department of Commerce

Washington, DC 20234

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

Issued July 1983
Library of Congress Catalog Card Number: 83-600544

National Bureau of Standards Special Publication 657

Natl. Bur. Stand. (U.S.), Spec. Publ. 657, 198 pages (July 1983)

CODEN: XNBSAV

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1983

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, DC 20402

Price $6.00

(Add 25 percent for other than U.S. mailing)
PREFACE

This research was conducted under the sponsorship of the Office of Recycled

Materials, National Measurement Laboratory, National Bureau of Standards

(NBS), by the Operations Research Division, Center for Applied Mathematics,

National Engineering Laboratory, NBS. The Resource Recovery Planning (RRPLAN)

model described in this report is an outgrowth of a cooperative project

between the Office of Recycled Materials, the Operation Research Division, and

Edward B. Berman Associates, Inc. The RRPLAN model is designed with three

purposes in mind. First and foremost, is the ability to generate a preferred

plan for resource recovery. Second, is the capability to evaluate a scenario

specified by the decision maker for technical and economic feasibility.

Third, is its use as a tool to facilitate the decision making process by

providing answers to many what-if questions through an in-depth sensitivity

analysis

.

The RRPLAN model is a part of a larger program specified for the Office of

Recycled Materials in the Resource Conservation and Recovery Act of 1976 (P.L.

94-580). This legislation directs the Secretary of Commerce, acting through

NBS, to hold public hearings and publish guidelines for the development of

specifications. These specifications are needed for the classification of

materials which can be recovered from wastes now destined for disposal.

One of the goals of the Resource Conservation and Recovery Act, as it relates

to this project, is to provide a framework for fostering the advances required

to promote a more efficient mix of economic and environmental factors

associated with the disposal and subsequent recovery and/or reuse of resources contained in municipal solid waste. The RRPLAN model addresses this goal by

providing a methodology for the economic assessment of current and potential

technologies, market potentials, and institutional barriers to resource conservation and recovery. The model is designed for use by both the public

and private sectors in seeking to analyze the economic issues associated with

alternative solid waste management programs.

The authors would like to express their gratitude to the many individuals

whose cooperation helped them as model builders to better understand the needs

of the intended users of the RRPLAN model. Appreciation is extended to Ms.

Rosalie T. Matthews, Office of Recycled Materials, for her assistance, insight

and many helpful comments throughout the course of this effort. Appreciation

is also extended to Ms. Cathy Hudson, Ms. Barbara Heneberry and Mr. Michael

Britti, formerly with the Office of Recycled Materials, for their comments on earlier drafts of this document. Appreciation is also extended to Ms.

Christal Waters, of the California Solid Waste Management Board, for her

assistance in preparing and critiquing the result of a case study for a typical region of California. Special appreciation is extended to Mssrs.

Patrick W. Cooke, Office of Recycled Materials, and William G. Hall,

Operations Research Division, for their extensive comments and editorial

assistance which made the timely completion of this study possible. Special

appreciation is also extended to Dr. Harvey Yakowitz, Office of Recycled

Materials, whose stimulating discussions provided guidance and encouragement

throughout this effort.

iii
EXECUTIVE SUMMARY

Solid waste management is among the most complex municipal or regional

governmental tasks facing policy makers today. The physical problem of where

to locate the facilities is further complicated by the increasing costs of

disposal, new technologies, environmental regulations and the unavailability

of land for landfill. The problem becomes even more difficult when one notes

that solid waste management facilities may exhibit economies of scale in

construction and processing. This implies that the benefits associated with

lower per unit processing costs can only be achieved through regionalization.

However, the process of regionalization creates two fundamental problems:

(1) the complexity of the regional system design; and (2) the need for

political consensus. Both of these problems have to be addressed by

developing and clearly presenting technical and economic data about the

consequences of various regional approaches. Finally, by opting for

regionalization, decision makers must weigh the benefits of reduced processing

costs against the risk of cost overruns which are inherent in large

construction projects.

Over the past decade, there has been a proliferation of mathematical models

attacking such issues as facility location, vehicle routing and manpower

planning. Due to unrealistic data requirements or the complexity of using and

interpreting the model's results, the application of these models to the solid

waste management problem has often been disappointing. More recent modeling

approaches have attempted to couple the analytical power of the computer with

the ability to organize the thoughts of the decision maker systematically so that important pieces of information are not overlooked. From these

experiences, it is possible to define a set of criteria that any model which

purports to deal with solid waste management problems should satisfy. At a minimum, the model should be able to capture explicitly the effects of

alternative financial and cost-sharing arrangements, allow for economies of

scale, and be amenable to sensitivity analysis associated with a well-chosen

set of what-if questions.

Although the general solid waste management problem poses some formidable

difficulties to decision makers, the economics of resource recovery is

significantly more complicated, requiring an in-depth analysis of facility

design and cost as well as market size, structure and location. In order to

address these complicating factors, RRPLAN deals with two interdependent

issues. On the one hand, RRPLAN explicitly incorporates potential economies

of scale in the construction and operation of a solid waste processing

facility. The model is thus able to support the basic tradeoff of savings

from centralized processing versus the costs of additional haul required to

bring it about. On the other hand, RRPLAN uses a detailed cost accounting

system to attack the economic issues, carefully measuring the effects on overall program costs due to decisions affecting siting, routing, marketing

and financing. By integrating the technical issues of processing with these

four major decision points, RRPLAN permits a wide variety of questions to be

examined carefully.

iv
In reality, RRPLAN is a descendant of two earlier models. These models are known as WRAP (Waste Resource Allocation Program) and RAMP (Recovery And

Market Planning). Both models were developed by the Mitre Corporation, the

former through funding from the Environmental Protection Agency. There are substantial differences between RRPLAN, WRAP and RAMP, especially regarding

their software support systems and their treatment of market structure. The

major focus of WRAP is on the identification of a preferred plan which

includes the best candidate sites, the appropriate processing and disposal

technology at each site, the sizing of each site and all transportation

linkages among centers of waste generation, processing sites, and disposal

sites. A major weakness of WRAP is its implicit assumption that any market

for recoverables (e.g., ferrous products and newsprint) is unlimited. If

market saturation is an important consideration, then WRAP'S solution would

represent an overly optimistic plan which could lead to serious cash flow

problems if the plan were implemented. RAMP adds the saturation effect by

incorporating both declining price and limited size markets. RRPLAN

incorporates all of the capabilities of WRAP and RAMP, as well as numerous enhancements which render its cost accounting system far superior to those

used in its predecessors. Furthermore, its more reasonable data requirements

than wrap's coupled with the type and nature of its output should permit

RRPLAN to greatly facilitate the regional planning and decision making

processes. RRPLAN adds a sophisticated cost model, built-in source-separation

options, an automatic dedicated transfer station function, user-defined cost,

energy, and commodity categories, and an extensive analysis of costs by source

and site (including a projected full-cost tipping fee for each site).

A case study of a hypothetical region is used to carefully illustrate the

types of analysis which can be performed by RRPLAN. Once the user has worked

through the case study presented in this report, they should be able to handle

a wide variety of solid waste management problems.

It is anticipated that the use of this model should result in significant

savings in costs and physical resources. An additional benefit associated

with the use of RRPLAN is its ability to help decision makers weigh the risks

associated with alternative regional plans. For example, the repeated use of

the model, based on a carefully formulated set of what-if questions, may point

to areas where potential savings running into the tens of millions of dollars

over a typical planning period could accrue to the community, and what risks

the comunity would have to accept to achieve these savings.

V
Table of Contents

Page

PREFACE iii

EXECUTIVE SUMMARY iv

LIST OF EXHIBITS vii

LIST OF FIGURES ix

LIST OF TABLES ix

1. INTRODUCTION 1

1 . 1 Background 1 1.2 Purpose 2 1.3 Scope and Approach 2

2. THE RESOURCE RECOVERY PLANNING (RRPLAN) MODEL 4

2.1 Overview... 4

2.2 Methodology 5 2.3 Assumptions and Limitations 16

3. INPUT DATA REQUIREMENTS 20

3.1 Data Collection and Maintenance for the RRPLAN Model 20

3.2 Case Study Input File 36

3.3 Packer and Van Distance Files 94

4. THE RRPLAN OUTPUT AND ITS INTERPRETATION 101

4.1 Input Summary 101

4.2 Analysis of the Regional Plan 114

4.3 Post-Optimality Analysis 125

5. GUIDANCE ON USING RRPLAN 136

5.1 Setting Up a Test Run 136

5.2 Controlling the Structure of the Solution 150

APPENDIX A GLOSSARY OF INPUT VARIABLES 157

APPENDIX B SAMPLE WORKSHEETS FOR THE NORTHEASTERN MASSACHUSETTS

CASE EXAMPLE 180

REFERENCES 188

vi
LIST OF EXHIBITS

Page

3.1 Worksheet I: General Information 24

3.2 Worhsheet II: Paper Market Data 26

3.3 Worksheet III: Cost Category Data 27

3.4 Worksheet IV: Energy Category Data 28

3.5 Worksheet V: Commodity Category Data 28

3.6 Worksheet VI: Source Data 29

3.7 Worksheet VII: Site Data 30

3.8 Worksheet VIII: Landfill Data 30

3.9 Worksheet IX: Process Data 31

3.10 Worksheet X: Site-Process Combination Data 32

3.11 Worksheet XI: Market Data 33

3.12 Worksheet XII: Cost Summation Data 34

3.13 Worksheet XIII: Transportation Data 35

3.14 Listing of a Typical Case Study File 37

3.15 Case Study File: Program Controls and General Data 44

3.16 Case Study File: General Information 46

3.17 Case Study File: Source Energy Data 48

3.18 Case Study File: Energy Factors 50

3.19 Case Study File: Trigger Prices for Paper Market 52

3.20 Case Study File: Critical Paper Market Prices 54

3.21 Case Study File: Paper Market Price Distribution 56

3.22 Case Study File: Cost Category Data (Operating) 58

3.23 Case Study File: Cost Category Data (Capital) 60

3.24 Case Study File: Energy Category Data 62

3.25 Case Study File: Commodity Category Data 64

3.26 Case Study File: Source Data 66

3.27 Case Study File: Site Data 68

3.28 Case Study File: Landfill Data 70

3.29 Case Study File: Process Data (1) 72

3.30 Case Study File: Process Data (2) 74

3.31 Case Study File: Process Data (3) 76

3.32 Case Study File: Site-Process Combination Data 78

3.33 Case Study File: Market Data (1) 80

3.34 Case Study File: Market Data (2) 82

3.35 Case Study File: Market Data (3) 84

3.36 Case Study File: Cost Summation Data 86

3.37 Case Study File: Transportation Data (1) 88

3.38 Case Study File: Transportation Data (2) 90

3.39 Case Study File: Transportation Data (3) 92

3.40 Case Study File: Typical Distance File for Packers 98

3.41 Case Study File: Typical Distance File for Vans 99

4.1 Input Summary of Program Controls and General Information 103

4.2 Input Summary of Cost, Energy and Commodity Category Data 105

4.3 Input Summary of Source, Site and Landfill Data 106

4.4 Input Summary for Process and Site-Process Combination Data 108

4.5 Input Summary for Market and cost summation Category Data Ill

4.6 Transportation Activities Considered by the Model: Site-to-Site

,

Site-to-Market , and Source-to-Site (Excluding Conditional Source

Separation) 112

vil
Page

4.7 Transportation Activities Considered by the Model:

Conditional Source Separation Options 113

4.8 Abbreviated Form of the Solution 115

4.9 Transportation Activities in the Solution 116

4.10 Processing Activities in the Solution 118

4.11 Marketing Activities in the Solution 121

4.12 Revenue Summary by Market Type 123

4.13 Analytical Summary of the Solution 124

4.14 Analysis of Splits Occurring in the Solution: Source Tonnage,

Site/SIPROC, Location-Commodity 126

4.15 Analysis of Site Routings for the Four Sites Considered

by the Model 128

4.16 Analysis of Source Routings for Lynn, Massachusetts 130

4.17 Analysis of Source Routings for Marblehead, Massachusetts 131

4.18 Analysis of Source Routings for Salem, Massachusetts 132

4.19 Analysis of Source Routings for Revere, Massachusetts 133

4.20 Source Cost and Site Cost Analysis 135

B.l Completed General Information Worksheet 181

B.2 Completed Paper Market Data Worksheet 182

B.3 Completed Cost Category Data Worksheet 182

B.4 Completed Energy Category Data Worksheet 183

B.5 Completed Commodity Category Data Worksheet 183

B.6 Completed Source Data Worksheet 184

B.7 Completed Site Data Worksheet 184

B.8 Completed Landfill Data Worksheet 185

B.9 Completed Process Data Worksheet 185

B.IO Completed Site-Process Combination Data Worksheet 186

B.ll Completed Market Data Worksheet 186

6.12 Completed Cost Summation Data Worksheet 187

B.13 Completed Transportation Data Worksheet 187

viii
LIST OF FIGURES

Page

2.1 How RRPLAN Addresses the Economics of Resource Recovery 8

2.2 Model Organization 12

3.1 Organization of the Case Study Input File 42

LIST OF TABLES

2.1 Equations Used by RRPLAN 13

2.2 Activities Considered by RRPLAN 14

2.3 Allowable Ranges for Key Variables Affecting Problem Size 19

3.1 User Input Groups 41

3.2 Structure of the Packer and Van Distance Files 97

5.1 Guidelines for Calculating the Number of Rows in the A Matrix 141

5.2 Guidelines for Calculating the Columns of Rows in the A Matrix.... 142

5.3 Calculating the Number of Rows in the A Matrix for the Example

Presented in Chapter 3 144

5.4 Calculating the Number of Columns in the A Matrix for the Example

Presented in Chapter 3 144

5.5 General Errors 147

5.6 Linkage Errors 148

5.7 Process-Related Errors 149

ix

1 . INTRODUCTION

1.1 BACKGROUND

The problem of identifying cost-effective methods for processing a region's

solid waste stream goes beyond the identification of sites which are politically feasible. As fiscal pressures on communities increase, the

economics of waste disposal becomes an ever more important issue. Resource

recovery may be an attractive alternative, providing planners with the

potential for reducing the costs of processing the region's waste stream while

conserving valuable land, material and energy resources. The economics of resource recovery is significantly more complicated than other alternatives

however, requiring an in-depth analysis of facility design and cost as well as market size, structure and location.

The Resource Recovery Planning (RRPLAN) model was especially designed to

address the complicating factors mentioned above. On the one hand, RRPLAN

explicitly incorporates potential economies of scale in the construction and

operation of a solid waste processing facility. The model is thus able to

support the basic tradeoff of savings from centralized processing versus the

costs of additional haul required to bring it about. On the other hand,

RRPLAN uses a detailed cost accounting system to attack the economic issues,

carefully measuring the effects on overall program costs due to decisions

affecting siting, routing, marketing and financing. By integrating the

technical issues of processing with these four major decision points, RRPLAN

permits a wide variety of questions to be examined carefully.

RRPLAN was developed in a cooperative program under the sponsorship of the

National Bureau of Standards' Office of Recycled Materials by Edward B. Berman

Associates, Inc., with the direct support of the NBS's Center for Applied

Mathematics. RRPLAN has been tested extensively through pilot study

applications to data from the State of Louisiana, central Mississippi, the

California Solid Waste Management Board, and New York City. Members of the

NBS team met with representatives of each region studied to discuss the

implications that the use of the model would have on project planning and

implementation. The clarity with which the implications of various

assumptions could be measured stimulated numerous discussions, indicating that

the model may be used extensively by solid waste management officials. A

series of mini-tutorials on the model were presented to officials from

Mississippi, New York and California. A two-day workshop for a broad cross section of solid waste management officials was presented in September of

1982. The workshop was sponsored by NBS, the Association of State and

Territorial Solid Waste Management Officials, the Government Refuse Collection

and Disposal Association, Inc., and the U.S. Conference of Mayors.

1
1 ,2 Purpose

The purpose of this report is to present a non-technical description of the

RRPLAN computer program. The model was designed to handle a full range of

solid waste management problems, ranging from the basic landfill option to

complex marketing arrangements for recoverables as special cases. The basic

philosophy behind the model is one of optimization. This approach was taken

because it permits the economic and engineering data associated with the

problem (e.g., waste generation rates and the location of processing

facilities) to be organized in an objective manner. Although the model can focus on energy as its main objective, we believe that the minimization of the

costs of processing a region's solid waste stream will be the objective most

often emphasized by decision makers. To a certain degree, one could think of

RRPLAN as a sieve which reveals those policy scenarios which have the greatest

potential for reducing costs. Once a scenario, or series of scenarios, have

been identified, the group carrying out the study can perform a detailed

analysis of its various components (e.g., fleet composition and vehicle

routing) . It is important to point out that the use of the model , as envisioned by its developers, is iterative. Thus, whenever the model is

exercised, the user should formulate a number of questions which represent

variations about some baseline scenario. In this way the user can gain

valuable insights into how changing circumstances or assumptions affect costs

and processing decisions.

1 .3 Scope and Approach

The documentation for the RRPLAN computer program is divided into two parts, a User's Manual and a Programmer's Manual, each of which is designed to be selfcontained and hence may be used independently. The first part, which includes

this document, is designed to serve as a User's Manual. This approach was taken because most users are not concerned with the internal workings of the

program. On the other hand, it is frequently useful for the programmer, who

has the responsibility of both setting up the program on the host system as well as making changes to the source code which reflect user demands or peculiarities of the operating system, to have access to the User's Manual so that changes can be made in the most efficient manner.

The RRPLAN computer program is outlined in chapter 2. A description of the

philosophy and methodology behind RRPLAN is given first, followed by a discussion of the data requirements, the various options available to the

user, as well as some of the limitations of the program.

Chapter 3 consists of a detailed example in which all inputs to the RRPLAN

computer program are carefully discussed. The chapter uses a hypothetical

evaluation of several communities in the Northeastern portion of the

Commonwealth of Massachusetts as a means of illustration. A complete set of

2
worksheets designed especially for the RRPLAN computer program are then

presented. The chapter includes a sample input deck of computer cards as well

as a discussion of what each item means and where it should be placed in the

input deck.

The output associated with the example constructed in chapter 3 is analyzed in

chapter 4. This chapter aims at both illustrating the types of output reports

produced by RRPLAN as well as a line-by-line description of what the output means. Three major categories of output reports will be analyzed.

The fifth chapter provides a set of guidelines for making efficient use of the

RRPLAN computer program. Topics discussed which are thought to be of interest

to a large group of users include: (1) how to effectively find and correct

input errors when they occur; (2) when and how to use a particular combination

of options; (3) how to handle large or unusually complex problems; and

(4) how to batch runs so that a sensitivity analysis can be performed both

quickly and efficiently.

The report also includes two technical appendices which describe each input

variable in greater detail than permitted in chapter 3 and provide a cross walk between the worksheets and the input deck.

The companion report^, which serves as a Programmer's Manual, focuses on the

more technical aspects of the RRPLAN program documentation. A mathematical

discussion of the computer program serves to introduce the basic philosophy

behind the algorithm. Each routine is described, focusing on such topics as: (1) purpose; (2) calling sequence; (3) common blocks used; and (4) reports

produced. A functional description of the model is used to facilitate the

task of setting up RRPLAN on a user's computer system. A series of tests are outlined which should permit programmers to verify if the model produces

correct solutions. The program is written in FORTRAN and complies with the

guidelines set down in the ANSI X3. 9-1 978 software standard.

2

E.B. Herman, R.E. Chapman and H.K. Hung, Program Documentation for the

Resource Recovery Planning Model , National Bureau of Standards, NBSIR (in

preparation)

.

American National Standards Institute, American National Standard Programming

Language FORTRAN , ANSI X3. 9-1 978, New York, 1978.

3
2. THE RESOURCE RECOVERY PLANNING (RRPLAN) MODEL

2.1 OVERVIEW

Solid waste management is among the most complex municipal or regional

governmental tasks facing policy makers today. The nonhomogeneous composition

of the waste stream has resulted in a proliferation of approaches for handling

the problem. Finding the best approach is complicated by noting that some communities have used mathematical models and met with little success whereas

others have not and were able to achieve some reasonable solution. Over the

last decade, many mathematical models have been developed and implemented with

varying degrees of success.^ Although small communities would not need an elaborate model, techniques from engineering economics based on life cycle

costing can still be quite useful in containing costs. ^ Perhaps the greatest

advantage offered by models however, is the way in which they help organize

the thoughts of the decision maker so that important pieces of information are not overlooked. More recent modeling approaches have focused on this point;

consequently, reliance on these models should produce useful results in

dealing with the overall problem.

The RRPLAN model described in this report may be thought of as a descendant of

two earlier models. These models are known as WRAP (Waste Resource Allocation

Program) and RAMP (Recovery And Market Planning) . Both models were developed

by the Mitre Corporation, the former through funding from the Environmental

Protection Agency.^ There are substantial differences between the two models,

especially regarding their software support systems and their treatment of

market structure. Both models use the same optimizer as RRPLAN. In WRAP, as in RRPLAN, a front end is available to build the equations for input into the

optimizer, and a back end is available to interpret the solution. In RAMP, a more sophisticated equation structure is available, including the full market

structure in RRPLAN, but the user must prepare equations for direct input into

the optimizer, and must interpret its solution. The major focus of WRAP is on the identification of a preferred plan which includes the best candidate

sites, the appropriate processing and disposal technology at each site, the

sizing of each site and all transportation linkages among centers of waste

generation, processing sites, and disposal sites. A major weakness of WRAP is

J.C. Liebman, "Models in Solid Waste Management," in S. Gass and R. Sisson,

A Guide to Models in Governmental Planning and Operations , (Potomac,

Maryland: Sauger Books, 1975), pp. 139-164.

Analysts who wish to study a small region should consult the report by

Barton and MacAuley before attempting to use the RRPLAN model. (D.R. Barton

and P. MacAuley, An Economic Analysis of Resource Recovery Facilities and

Waste Newspaper Recycling , Bureau of Industrial Economics, U.S. Department

of Commerce, August 1982.)

'Doc\jmentation for WRAP is included in the following reports: E.B. Berman,

WRAP-A Model for Regional Solid Waste Planning: User's Guide , U.S.

Environmental Protection Agency, SW-574, 1977; and V. Hensey, WRAP-A Model

for Regional Solid Waste Planning: Programmer's Manual , U.S. Environmental

Protection Agency, SW-573, 1977. All documentation for RAMP is included in

the following report: Use of RAMP (Recovery And Market Planning) For The

Evaluation of Policy Issues in Resource Recovery, mimeo, undated.

4
its implicit assumption that any market for recoverables (e.g., ferrous

products and newsprint) is unlimited. If market saturation is an important

consideration, then WRAP'S solution would represent an overly optimistic plan

which could lead to serious cash flow problems if the plan were implemented.

RAMP adds the saturation effect by incorporating both declining price and

limited size markets.

As the description of the RRPLAN model unfolds, it will become evident that it

incorporates all of the capabilities of WRAP and RAMP, as well as numerous enhancements which render its cost accounting system far superior to those

used in its predecessors. Furthermore, its more reasonable data requirements

than WRAP coupled with the type and nature of its output should permit RRPLAN

to greatly facilitate the regional planning and decision making processes.

RRPLAN adds a sophisticated cost model, built-in source-separation options, an automatic dedicated transfer station function, user-defined cost, energy, and

commodity categories, an extensive analysis of costs by source and site

(including a projected full-cost tipping fee for each site), four new optimizing modes, and two new forcing modes. These will all be described in

detail in this manual.

2.2 METHODOLOGY

RRPLAN is a computer model designed with three purposes in mind. First and

foremost, is the ability to generate a preferred plan for resource recovery.

Second, is the capability to evaluate a scenario specified by the decision

maker for technical and economic feasibility. Third, is its use as a tool to

facilitate the decision making process by providing answers to many what-if

questions through an in-depth sensitivity analysis.

The model has five basic modes of optimization. Each mode serves to define the

type of objective which is to be minimized or maximized. The first two modes

of optimization minimize the total cost of the regional plan over a specified

planning period. If both the timing and magnitude of cash flows are important, then the discounted costs of the regional plan should be minimized.

If only the magnitude of cash flows is important, then undiscounted costs

should be minimized. It is important to note that the choice of discounted

rather than undiscounted costs, or vice versa, may cause both the costs of the

plan and the physical flows within the system to differ. The first mode,

minimize lifetime discounted cost, is the preferred criterion for plan

selection and evaluation. The third mode of operation seeks to maximize the

net energy (energy produced (saved) from (due to) resource recovery activities

minus all other energy inputs) of the regional plan. Such an objective

function might be useful in comparing various waste-to-energy programs. The

fourth mode of operation seeks to minimize a linear form (weighted sum) of

program cost and net energy categories. This approach might prove useful in

comparing mixtures of traditional and waste-to-energy programs if some form of

matching formula for funds was in effect and will permit the user to weight

cost or energy categories other than equally. The final mode of operation

focuses on the topic of scenario evaluation . For example, a region may have a proposed plan which needs to be evaluated from the viewpoint of technical and

economic feasibility. Typical questions addressed under this mode of

operation would include the following. Are all facilities able to process

5
the indicated waste stream without exceeding their rated capacity, or that of

plants down the line which they feed into? Can resources be reallocated so that overall costs are reduced?

In order to handle the five types of objectives just discussed, RRPLAN

includes a set of cost categories, energy categories, and commodities. Cost

categories are the heart of RRPLAN' s accounting system. They are included so that the differential impacts of the regional plan on the various segments of

the population, as well as financial transfers into the region from marketing

activities, can be measured. Individual cost categories are classified as either operating (i.e. annual recurring costs) or capital. Each cost category

has a cost growth scenario (differential inflation rate) associated with it, so that it may increase more (or less) rapidly than the general rate of

inflation. Financial arrangements are explicit in all capital cost

accounting. All capital items, except those currently in use, are assumed to

be purchased at the beginning of the planning period. Replacements which

occur within the planning period are inflated by the differential inflation

rate from the first year of the planning period to the year of replacement. A

standard amortization calculation is used to generate an annual cost for each

year of the capitalization of the loan. That cost is deflated by the general

inflation rate from the year of cost to the year of purchase to represent the

decline in constant dollars through time after the cost is fixed in current

dollars by the act of purchase. A series of cost summation categories permit

the user to aggregate cost categories having differential inflation rates,

useful lives, etc., into a single cost summation category for further

analysis. Energy categories may be handled in a similar manner. The model

greatly facilitates the process of performing a detailed cost analysis, once a solution has been found, by leaving the user ample opportunity to define the

types of commodities processed and sold (e.g., municipal solid waste (MSW) and

steam). The use of separate commodity categories also permits one to measure

the effects of differential transportation costs (e.g., handling MSW versus newsprint) as well as cost-sharing arrangements for processing and/or revenue- sharing arrangements among municipalities for marketing activities.

The basic structure of RRPLAN consists of a set of equations and activities

relating sources of solid wastes, sites where the wastes can be processed and

markets for energy or recovered materials. The model begins with the sources

of solid waste (e.g., a community in a region or a district in a metropolitan

area). Each source has associated with it a location, an estimated waste

generation figure, a series of options for preseparating paper, glass and

cans, and a set of transportation linkages to sites where the waste is

processed. Sites, as used in RRPLAN, may contain a landfill, a transfer

station, an incinerator without heat recovery, or a resource recovery

facility. Each site has associated with it a location, the type of processes

it can accommodate (referred to as site-process combinations), the operating

and capital costs required to process the wastes, capacity considerations,

outputs of wastes and marketable items, and transportation linkages. The

model can handle markets with downward sloping demand curves, and with

constraints on capacity, as well as ones which can receive unlimited

quantities of recoverables . Each market has associated with it a location, a

6
revenue schedule, and transportation linkages. The equations require all

wastes to be transported to a site for processing, all wastes arriving at a site to be processed, all outputs of a site to be processed or sold, and that no capacities are exceeded. The activities in the model represent physical

flows (e.g., the amount of waste incinerated at a particular site).

The model approaches the difficult problem of siting and sizing solid waste

processing facilities by first approximating non linearities in the capital

and operating cost functions with up to three linear segments. Each segment

has an intercept (a fixed charge) and a slope (an incremental cost associated

with increased processing activities). The introduction of fixed charges

imposes certain complications, causing the solution domain to be lumpy. This

requires a specially designed optimization technique to generate meaningful

solutions. The technique used in RRPLAN involves a fixed-charge linear

programming algorithm with a forcing procedure to insure that the model can pass over an area of temporarily increasing cost in the solution domain (i.e.,

a lump) to find the true optimum. ^ RRPLAN adds new methods of forcing

representing a significant improvement over methods used in other models^ in

which each site (or site-process combination) which was in the solution is

forced out of the solution and vice versa. This approach permits the solution

domain to be searched in a more coordinated way by operating on all activities

(e.g., transportation, processing, marketing) associated with a particular

site (or site-process combination)

.

Figure 2.1 shows the major physical system details, where it can be seen that

the physical flows represented in the model include the basic concepts

discussed earlier: sources; sites; markets; and transportation linkages.

Figure 2.1 is, however, merely a capsule summary of the overall problem

because it focuses on the management of a single source's wastes. In reality

there are numerous sources, causing the array of linkages to become intertwined.

The first point of interest in the figure is the source of waste, or "waste

generation zone." At this point RRPLAN permits the user to offer two types of source separation for consideration or to ship the mixed MSW directly

to a processing facility. Under the first source separation scenario, it is

assumed that all paper, glass and cans are removed from the waste stream, sent

to a central collection point, and then shipped to the respective market.

This option is referred to as unconditional source separation. Under the

second source separation scenario, paper, glass and cans are always removed

W.E. Walker, "A Heuristic Adjacent Extreme Point Algorithm for the Fixed

Charge Problem," Management Science , Vol. 22 (1976), pp. 587-596.

RRPLAN approaches the forcing issue in a more coherent manner than does WRAP.

wrap's method of single or double column (i.e., activity) forcing are of

questionable efficiency, particularly where a site is linked to three or more sources or other sites. Column forcing operates from within the optimizer,

and is therefore blind in the sense that there is no information within the

optimizer on what the various columns (activities) represent.

7
Figure 2.1 How RRPLAN Addresses the Economics of Resource Recovery

8
but paper is treated separately. As in the previous scenario, cans and glass

are sent to the central collection point and marketed. Paper, on the other

hand, is put to two possible uses which reflect both its fiber value in

recycling and its energy value in combustion. This option is referred to as conditional source separation. The rationale behind this scenario is as follows. If the price of paper exceeds some prespecified price (up to five

such "trigger" prices are available) then it will be marketed for its fiber

value; otherwise the paper will be burned. ^ If multiple options are offered

at a source, the model picks the preferred source-separation option. The

transportation linkages emanating from the source are coded to represent

whether the link is carrying wastes or recoverables . The same coding system

is used throughout the diagram. If source separated paper, cans and glass (or

any other marketable commodity) are to be shipped to the market, then they

flow along the clear linkage. If, on the other hand, mixed MSW or the residue

from source separation is to be shipped, then it flows along the shaded

linkage. Note that all wastes generated enter into transportation. From a source, it is assumed that all wastes are hauled to either a transfer station,

a processing plant, or a landfill. The mode of transportation is assumed to

be in a packer having a capacity of approximately 5 tons. At the sites where

processing occurs, the wastes and/or processed residues can be loaded onto

larger vehicles referred to as vans. These vehicles are used with high volume

transport because they have significantly lower costs per ton mile of haul.

Turning now to the sites where processing takes place, it can be seen that the

transfer station collects the wastes from packers and loads it onto vans for

shipment either to a processing plant or a landfill. The landfill is the

ultimate disposal site, receiving wastes hauled from the sources in packers

and from transfer stations and processing plants in vans. Consider now the

processing plant, where, for purposes of illustration, we shall assume some form of resource recovery takes place. 2 The shaded linkages coming into the

plant require all wastes which arrive to be processed. Similarly, all

commodities which leave the plant must be disposed of (shaded linkage) or sold

(clear linkages).

The facility may have a heat recovery system in which case additional

revenues would be generated from the use of waste paper as a fuel

supplement

.

•It is not necessary that any form of resource recovery take place at a site.

In fact all wastes can be landfilled. Such an option would represent a meaningful baseline against which resource recovery could be compared. A

related point has to do with the number of processing plants which can be

placed in series. RRPLAN puts no constraints on one processing plant

receiving the output of another. Thus process residue from an incinerator

could be reprocessed for use as an additive for road surfacing or for

patching material, or for materials recovery.

9
Each transportation, processing, and marketing activity has associated with it

a set of costs. For transportation, this includes the periodic replacement of

the rolling stock as well as the cost of physically hauling the wastes or recoverables . The facilities where processing takes place require greater

care in cost accounting. For example, there may be site preparation costs

(e.g., access roads) on top of the plant construction costs and the normal

costs of day-to-day operations. Whereas no economies of scale are assumed in

transportation, they are admissible for both facility construction and all

future processing activities. Cost curves representing economies of scale can

be approximated by up to three linear segments, each with a slope and an intercept. In this way the model can capture the basic trade off of savings

from centralized processing versus the costs of additional haul required to

bring it about. In addition to costs, each processing plant can be

capacitated.^ The setting of a capacity could be based on either political or technical constraints. Setting a capacity could thus prevent the model from

shipping to a single site more waste than would be technically or politically

feasible.

Two types of markets are also shown in the diagram. RRPLAN treats the markets

for energy (e.g., steam or electricity) and materials in a much more comprehensive manner than WRAP. RRPLAN can handle four types of market

structure. These structures are: (1) unlimited fixed price markets; (2)

capacitated fixed price markets; (3) declining price markets with no upper

limit; and (4) capacitated declining price markets. An example of an unlimited fixed price market might be an electric utility which will purchase

any amount of electricity from the plant at a flat rate of hi per kWh. An

example of a capacitated declining price market might be a nearby industrial

facility which will purchase process steam for $3 per K (thousand) lbs for

the first 900 M (million) lbs; but will pay only $2 per K lbs for the next 900

M lbs; and can not productively use the steam above 1800 M lbs. The market is

considered saturated at 1800 M lbs. RRPLAN also permits revenue sharing

arrangements among municipalities to be incorporated into the market

analysis

Figure 2.2 provides a capsule summary of the way the RRPLAN model is

organized. As a first step, the values of all major variables are initialized

prior to the reading of any user input data. The input, as will be shown in

detail in the next chapter, is divided into three files. All three files are represented as a single deck of cards on the flowchart. Data from the first

(case study) file are read in a special sequence. These cards include

information which sets control values, provides data on cost and energy

categories, the types of waste and recoverable commodities considered, data on sources, sites, landfills, processing options and markets, as well as directions for extracting data from the packer and van distance files. After

each card is read, the MAIN program checks the critical variables to determine

if they are within tolerance and in the proper sequence. If either of these error types (i.e., out of tolerance or improper sequence) is encountered, a

A plant (or market) which is capacitated has an upper limit on the amount of

wastes (recoverables) that can be processed in a given unit of time (e.g.,

1000 tons per day).

10
message will be written out to the user which should facilitate the process of

finding and correcting it. The program will then stop. If no problem was encountered, the input data are formatted and printed back for the user. In

this way the user can verify inputs as well as distinguish among a series of runs for the same case. The model reads each major type of data from a series

of loops within the MAIN program. The ranges on the loops are specified by

the information on the control card. Once all data from the case study file

have been read, screened, and output, the packer and van distance files will

be read. This portion of the MAIN program matches up the directions from the case study file with the distances^ in the packer and van distance files. If a distance is missing (e.g., the case study file indicated that shipments from

source A to site X were to take place but no distance from A to X was recorded) then the program writes a message to that effect and stops. The

message should enable the user to find the location causing the problem. The

specific problem can then be isolated by reviewing the data contained in the

files. Transportation linkages are input in the case study file and are specified as: (1) source-to-site; (2) site-to-site; and (3) site-to-market.

After the files are read, the source-to-site category is expanded. The degree

of its expansion depends on the source separation options offered and the type

of paper market prevailing in the region.

The application problem, referred to hereafter as the A matrix, is then

constructed from the input data. Each row of the A matrix corresponds to an equation; each column corresponds to an activity. The rows and columns of the

A matrix are defined in tables 2.1 and 2.2, respectively. If a data

inconsistency or sizing error results, a message is written to the user to

facilitate debugging and the program stops. If no error results, the MAIN

program calls the optimizer to solve the problem stored in the A matrix.

The optimizer (or linear programming module) which is contained in the

rectangular box of figure 2,2 consists of numerous subroutines. Those readers

who wish a detailed description of the optimizer are referred to the

Programmer's Manual.

As shown in section 3.3, the distances are measured in terms of miles,

minutes, thousands of dollars per commodity unit, or dollars per ton as selected by the user.

11
ure 2.2 Model Organization

12
Table 2.1 Equations Used by RRPLAN

Equation Type Purpose

Source balance equations Requires all waste generated at each source to be entered into transportation.

Source paper balance equations Requires all source-separated paper to

be entered into transportation.

Site input residue balance equations Generates a residue activity at a site

equal to the residue arriving at the

site.

Site input paper balance equations Generates a paper activity at a site

equal to the paper arriving at the

site.

Site input balance equations for

MSW and other commodities

Requires the total amount of MSW and

other commodities arriving at a site

to equal the amount of processing

activity at the site.

Site output balance equations Generates transportation activities

of a commodity from a site equal to

the process output of the commodity

at that site.

Market input balance equations Generates a total market activity

equal to the amount of the commodity

arriving at the market.

Site capacity constraints Insures that processing at a site does

not exceed capacity.

Land capacity constraints Insures that land use does not exceed

land available at any landfill.

Market bounds Requires the sum of activity at a market in segments less than or equal

to the jth (l<j<5) segment to be less

than or equal to the jth cumulative

bound for the market.

Constraint on artificial

processing activities

Provides an upper bound for artificial

activities

.

13
Table 2.2 Activities Considered by RRPLAN

Activity Purpose

Transportation category 2

Transportation category 3

Transportation category 4

Transportation category 5

Transportation category 6

Transportation category 8

Transportation category 9

Transportation categories 11-15

Residue activities

Paper activities

Process activities

Market activities

Artificial processing activities

NZ

NZX

Site-to-site shipments.

Site-to-market shipments.

Source-to-site shipments of unseparated MSW.

Source-to-site shipments of unconditional

source separated MSW (national paper

market)

Source-to-site shipments of unconditional

source separated MSW (local paper market).

Source-to-site shipments of preseparated

paper (local paper market)

Source-to-market shipments of preseparated

paper (local paper market).

Source-to-site shipments of conditional

source separated MSW (national paper

market with 1 to 5 trigger prices).

Measures thousands of tons per year of

source-separated residue arriving at a site.

Measures thousands of tons per year of

source-separated paper arriving at a site.

Measures number of commodity units processed

per year per linear segment at a site.

Measures number of commodity units purchased

per year per linear segment at a market

Take on epsilon values to preserve

structure.

Relieves infeasibilities associated with an advanced starting point.

Relieves infeasibilities when evaluating a prespecified plan.

14
The final symbol shown in figure 2.2 represents the output of solutions found

by the optimizer. All logic for this portion of the model is contained in the

MAIN program. Since the outputs will be discussed in detail in chapter 4,

only the major points will be touched upon in this section. The first

solution output consists of transportation activities in the solution. These

activities are printed out according to their transportation category number

(see table 2.2). The output includes the category number, the origin

location, the destination location, the commodity shipped, and the number of

commodity units shipped per year (e.g. M lbs of steam).

The second output summarizes the process activity levels. Pass-through

processing (the residue from source separation or source-separated paper) is

listed first followed by a site-by-site description of processing. This

information includes the site name and number, the process name and number,

the location where processing is carried out, the linear segment on the cost

function where processing takes place, the commodity and number of commodity

units processed, and, if appropriate, the percent of capacity at which the

plant operates. Information on dedicated transfer stations^ and landfills are then presented.

The third type of solution output is concerned with the market activities

appearing in the solution. Summary data for each market are output in

sequence. A separate line of output is given for each segment of the market

demand function (there may be up to five market segments). Cumulative sales

and cumulative revenues for each market are also given. Information on sales

and revenue of source-separated paper, cans and glass are then given.

Implicit revenues (e.g., revenues from recoverables not explicitly traded or marketed by the model) are then presented by site. A revenue summary showing

yearly averages for explicit commodities net of haul to the market (e.g. gross revenues minus haul costs) , source separated recoverables net of haul to the

market, implicit (net) revenues and total net revenues is then printed out.

Both discounted and undiscounted values are output for the revenue categories

just mentioned. The fourth solution output is an analytical summary. It

contains such items as the total tons of waste handled, the lifetime cost

(both discounted and undiscounted), an accounting of costs by category and, if

appropriate, information on energy categories.

RRPLAN also includes tracing. Tracing was incorporated into RRPLAN because

three types of splits may occur in the solution due to site capacity

constraints, landfill contraints and market limits. First, sources may be

split among offload sites. Second, sites may be split among the processes

offered. Finally, commodities arising from outputs of a process may be split

among offload sites and markets. Optional routing requests are also included

The model will consider siting a transfer station at each source in order to

reduce the overall costs of the plan. For large communities this may be a wise approach because the savings in haul costs may greatly exceed the

capital costs of the facility. The user has the option to suppress the

dedicated transfer station logic if so desired. The choice of a dedicated

transfer station at a source does not affect the size of the matrix.

15
in tracing. For example, consider a site where wastes can be processed. In

this case, the procedure will trace all site splits, through the output

coefficients of each process, and commodity splits, to a series of sites and

markets. The final tracing output summarizes source and site costs. These

costs include linkage and offload costs for sources and on-site and net offsite costs. The site cost is a projected full-cost tipping fee for each site

in the solution.

2.3 ASSUMPTIONS AND LIMITATIONS

The purpose of this section is to review RRPLAN's major technical underpinnings and to alert potential users to certain issues which, if

ignored, could reduce the usefulness of the model's output. The two most

important assumptions are related to the mathematical structure of the

problem and the concept of "forcing". Two related, but less important,

assumptions are concerned with the allocation of fixed charges (costs) among

the various activities and the model's basic waste generation scenario. All

other topics discussed in this section are associated with limitations on the

size of the problem which RRPLAN can handle.

The model provides for increasing returns to scale (i.e., declining costs —

economies of scale) in the process cost functions, and provides for decreasing

returns to scale in the market revenue functions. The model edit-checks the

latter, but for the former, the user should make sure, for a multiple-segment

process, that: (1) a higher segment has a higher intercept cost; (2) that a higher segment has a lower slope cost; (3) that all intercepts are non- negative (actually the sum of site preparation cost and the intercept should

be non-negative); (4) the crossing point between the first and second segment

functions is at a positive scale, and the crossing point between the second

and third segment functions is to the right of that (i.e., at a greater

scale)

Chapter 5 provides two procedures for the case of a process cost function

which shows first increasing and then decreasing returns to scale. The second

of which is fully consistent with a single-run application for optimal

plan generation.

The forcing concept as used in RRPLAN points out an important difference

between pure and approximate methods of optimization. In pure optimization

the emphasis is on finding a single solution which can be proven to be better

than all alternatives. Approximate optimization methods use rules which

exploit the mathematical structure of the problem but do not necessarily find

the "best" solution. Such methods are referred to as heuristics. The forcing

technique employed by RRPLAN uses such an approach to explore the solution

domain systematically.

16
The choice of the optimization method is dictated by both processing costs and

the required accuracy of the solution. With regard to processing costs, it is

not uncommon that solutions generated by heuristics will cost only a quarter

as much as those generated by a pure optimization technique. Consequently, if

the heuristic is also likely to produce a solution which is nearly optimal,

then the reduced costs to users would probably far outweigh the loss of

precision associated with not finding the "best" solution. In an earlier

study, ^ the original version of the optimizer, which lacked the enhancements

incorporated into RRPLAN, was used to solve a wide range of fixed charge

problems. For this set of case studies, the original heuristic was shown to

be from 2 to 835 times faster than a typical pure optimization method. The

heuristic failed to get the optimum solution in only two of the problems

posed. In both cases however, the minimum cost solution produced by the

heuristic differed by less than 2 percent from the true optimum.

Although RRPLAN uses a fixed charge algorithm to generate a preferred regional

plan, the cost functions of most activities do not have any fixed charges

associated with them. Within the framework of RRPLAN it is assumed that only

site-specific or site-process specific cost functions have fixed charges. It

is not necessary for site-specific or site-process specific costs to contain

any fixed charges. The program overhead required for including them is

available, however. The cost functions associated with all other activities

are precluded from using fixed charges by the program overhead. Thus the cost

functions associated with any transportation activities (e.g., source-to-site

shipments of MSW) consist only of variable costs. This assumption can easily

be justified.

2

Although RRPLAN explicitly allows for cost growth due to inflation, the rate

at which wastes are generated is assumed to remain constant over the entire

planning period.^ This may strike some users as odd, especially if the

region's resources for processing the waste stream are nearly saturated. The no growth scenario does however avoid a major pitfall; namely oversizing a

^W.E. Walker, op." cit.

^The rolling stock associated with transportation activities are mobile (hence

easily moved to and from a market), they have short useful lives, and a large

and active resale market. Acquiring them does not represent a commitment in

the same way as acquiring immobile, inflexible, long useful-life facilities

and equipment. Furthermore, rolling stock is too mobile to build up

economy-of-scale relations with any one link in the model,

^Tonnage growth can only be provided within the context of a dynamic model.

Such models are extremely demanding on matrix size, however. The use of a comparative static approach permits cost growth scenarios to be incorporated

without requiring the addition of any rows or columns. The fixed tonnages

can represent early period tonnages, late period tonnages, or anything in

between.

17
facility on the belief that operation and maintenance costs will be falling over time as the plant draws in even larger volumes of MSW. This may in fact

be the case for some regions. On the other hand, the costs of resource

recovery, and hence any comparative advantage it may have over the status quo, are likely to be highly sensitive to two factors. These factors are the level

of waste processed (which affects both operation and maintenance costs as well

as marketing opportunities) and the capital cost of the facility. If the

facility was oversized based on projected growth rates and these rates were

not experienced (say due to an aggressive source separation program prompted

by local demand) the costs per ton could become excessive. Such problems

could render resource recovery economically unattractive. Techniques for

analyzing growth scenarios through a series of runs within which the overall

planning period is subdivided are discussed in chapter 5.

As a means of protecting the optimizer from ill-formed problems, the model

edit-checks almost all of the variables input by the user. However, special

attention is given to those variables which affect the size of the problem

(through the construction of the A matrix) or the model's cost accounting

system. Since chapter 3 will consist of a line-by-line discussion of the

inputs, including their permissible range, this section will focus on only

those variables which have the greatest impact on the size of the problem.

The A matrix is currently sized for a maximum of 90 rows (equations) and 360

coltimns (activities). The types of equations and activities which make up the

A matrix were defined in tables 2,1 and 2.2; a formula for computing each

dimension of the A matrix is presented and described in chapter 5. The

matrix is designed to be sparse (mostly zeroes) but it may have up to 8100

non-zero elements. If the user wishes to exercise the double column forcing

option, then the maximum size of the A matrix is 50 rows by 100 columns. This

limitation can be relaxed to 90 rows by 360 columns with the use of an override control. Since the site or site-process forcing options are almost

certainly superior to double column forcing, the use of the override is not

recommended for large problems. The input variable names, their purpose and

allowable values are given in table 2.3. It is important to point out that if

several of the key input variables are set at their maximum value during the course of a single run, the maximum size of the A matrix is likely to be

exceeded. It is for this reason that a formula for computing the size of the

A matrix prior to execution is included in chapter 5. With regard to

transportation activities, linkages must be provided from each source and to

each market. If unconditional source separation is offered in a region with a local paper market, then the linkages to the local paper market will be

generated by the model. Process input linkages to each site must be provided

as well as process output linkages designated for shipment to other sites for

additional processing or disposal.

18
Table 2.3 Allowable Ranges for Key Variables Affecting Problem Size

Input Variable Purpose of Minimum Maximum

Name Variable Value Value

NSU Number of sources 1 60

NSI Number of sites 1 50

NPR Number of processes 2^ 30

NSEG(J) Number of segments per process 1 3

NCOF(J) Number of output coefficients^ 0 7 NSP Number of site-process combinations 1 80

NLA Number of landfills 30

NMK Number of markets Od 30

NSGMK(J) Number of segments per market 1 5

Number of transportation activities® 1 300

^The first process must be a new transfer station.

°By products of processing (e.g., incinerated ferrous or ash).

^There must be at least one landfill for each location at which a land-using

process is offered (see section 3.2 for additional details).

^There must be at least one market if source separation is offered and the

region contains a local paper market.

®After expansion (see section 3.3 for additional details).

19
3. INPUT DATA REQUIREMENTS

There are three basic types of data required as inputs in order to run RRPLAN.

The first type specifies the level of detail in the cost accounting system and

the interrelationships among sources, sites/processes, and markets. The

second and third types are distance tables for packer and van hauls,

respectively. Once all data have been collected and collated, each of the

three data types just mentioned may be referred to as a file. Such a file

could represent either a deck of cards which the user would input each time it was desired to run the program, or a mass storage data file which the

operating system would call and read.

3.1 DATA COLLECTION AND MAINTENANCE FOR THE RRPLAN MODEL

by

Patrick W. Cooke

Office of Recycled Materials

National Measurement Laboratory

National Bureau of Standards

In order to insure that the values for the input files are accurate, it was necessary to design a set of data collection worksheets which describes each

part of the data set including how it fits into the overall pattern. It is

important to point out that most data Is either region specific or generic.

For example, the amount of MSW generated by each source and the transportation

network are region specific whereas the techniques used for procesing MSW are generic. This has an important consequence for data collection. Based on past experiences where solid waste management officials cooperated with the

NBS team in order to exercise the model, the common concern focused on the

accuracy of the cost estimates for constructing and operating the processing

facilities. In almost every other case it was concluded that the region

specific data required to run the model could be obtained with only a modest

commitment of time and resources. With regard to the data on construction and

operating costs however, it is possible to utilize information on similar

projects which have been planned or built as a starting point. ^ These generic

data should serve to reveal those processes which are economically the most

attractive. From that point, it then becomes possible to perform a series of

engineering studies where one or more target configurations are explored in

detail. This approach should lead to more successful applications because it serves to allocate engineering funds to areas where they will have the

greatest payoff rather than using a shotgun approach which may not allocate

enough time and energy to get the required detail to differentiate among

competing designs. As more precise data become available, the model can be

rerun, and used to examine the answers to such questions as the effect on the

plan associated with a change in the financing rate or changes in the

composition of the waste stream.

If generic cost data are used it will most likely be necessary to adjust not

only for regional price differentials in the markets for labor services and

building materials, but also to adjust for cost growth over time. All three

types of cost factors are readily available from construction industry

publications.

20
The way in which the information collected on the worksheets would be

organized into a complete input deck is illustrated through reference to a hypothetical case example treating some communities in Northeastern Massachusetts. This example will be used in various capacities throughout the

remainder of the report. The example is deliberately kept simple to reinforce

the major concepts. It is recommended that each user try and reconstruct the

data files for this example and run it prior to performing any studies of

their own. This should permit the users to gain confidence in finding errors due to incorrect alignment, data transpositions, etc., where a known correct

deck can serve as a standard reference point. It will also help the user to see how the various components of the output fit together when viewed as a single unit. A line-by-line description of the input deck is given in order

to promote a more complete understanding of how the model reads, checks and

organizes the input data.

The following discussion presents a hypothetical case designed to test the

program. Figures should not be taken as indicative of any processing or contractual arrangements. The four communities of Lynn, Marblehead, Salem,

and Revere were considered the four sources of MSW for the entire region. The

region also included two waste-to-steam site-processes — one at Saugus, the

other at Worcester. Each of these processes has an associated steam market —

the General Electric Company's River Works Division at Lynn for the Saugus

site and the City of Worcester for the Worcester site. The Saugus site

includes a secondary processor which recovers materials from the residue and an on-site landfill for burying residues left from the two Saugus processes

and for any inputs that can not be accepted by the waste-to-steam facility.

The region has one landfill available — the Amesbury Landfill. There is also an unlimited market available for incinerated ferrous materials in New York

City.

In order to utilize the model effectively, users must research, collect, and

record in an orderly fashion various types of input data. A series of

thirteen data collection worksheets have been developed for aiding this

purpose and are presented in this section of the report. The presentation of

the data collection worksheets is divided into two parts. Each worksheet is

presented along with a brief description on its use in the form of an interpretation or guideline. A technical appendix illustrates how the

worksheets look when completed for the Northeastern Massachusetts case example

.

When completing the worksheets shown as exhibits 3.1 through 3.13, some care must be exercised in order to insure that all units are compatible. The

flexibility given to the user to select the commodities which will be analyzed

by the model requires careful planning regarding transportation flows and

processing and marketing activities. Both costs and net energy are likely to

be sensitive to the commodities being hauled, processed or marketed.

Therefore, in order to provide a common basis for approaching the specific

information on the worksheets, a brief description will be presented which

aims at classifying the data requested on each of the worksheets.

21
The basic units of the model are thousand tons and thousand dollars. Most

cost inputs may be entered in dollars per ton because that is equivalent to

thousand dollars per thousand tons. The two exceptions are:

(1) intercept costs must be entered in thousands of dollars; and

(2) if the user has defined the unit of a commodity in other than

thousand tons, the cost inputs into its process should be in

thousands of dollars per commodity unit.

For better correspondence with cost inputs, it is recommended that the energy

equivalent of the various cost-per-ton inputs be one-thousandth of the energy

unit per ton, with two exceptions corresponding to the two cost exceptions:

(1) intercept energy inputs must be entered in full energy inputs;

and

(2) if the user has defined the unit of a commodity in other than

thousand tons, the energy inputs into a process of which it is

the input commodity should be in full energy units per commodity

unit

The first worksheet relates to general information about various indices, the

composition of MSW, its heat value, collection costs and market prices for source separated commodities. All information related to the discount rate

and the inflation rate is to be recorded in percent per year, whereas

information on the composition of the solid waste stream is to be recorded as what percent of the total a particular component (e.g., glass) accounts for.

All energy values recorded on the first part of Worksheet I are to be in

million BTU (MBTU) per ton. The energy requirements for all other questions

are determined by the energy categories declared under question 13 of the

second part of Worksheet I. The cost categories associated with these costs

and revenues are declared under question 14. Question 9 requires a set of

ratios where the costs of processing a ton of source separated paper and

residue are used as the numerator and the cost of processing a ton MSW as the

denominator.

The second worksheet deals with paper market data. The basic unit of measure

used throughout this worksheet is dollars per ton. The third worksheet deals

with cost categories. The types of information deal with differential

inflation rates in percent per year and a variety of financial data for

capital assets. The fourth worksheet deals with energy categories. Although

no units are specified, users should exercise care when accumulating

commodities into an energy category to insure that the units defined by the

commodities are consistent.

22
The fifth worksheet deals with commodity categories. The definition of

commodities requires special care due to their impact on transportation,

processing and marketing costs. Worksheet VI deals with source data. Data on the rate of waste generation in thousands of tons per year (KTPY) are recorded

along with the cost per ton for hauling source separated recoverables to the

market. All other data deal with source separation options and cost and

energy categories for use in accumulation. Worksheet VII deals with site

data. Here it is important to recognize the relationship between data

recorded on this worksheet with that on IX (processes) and X (site processes).

The model checks for inconsistencies in assignment which will result in an aborted run (see section 5.1). Care should therefore be exercised in order to

match the input commodities. The eighth worksheet deals with landfills, where

the number of acre feet remaining must be recorded.

The process data worksheet (IX) provides for entry of data specific to various

generic processes. Worksheet X defines what processes are to be offered at

what sites, and provides information that is site-process specific. The

market data worksheet (XI) provides for entry of relevant information about

marketing this commodity defined as (explicit) process outputs. It is on these three worksheets where the cost and energy units must be reconciled with

regard to inputs, outputs and interlocational flows.

The last two worksheets deal with cost summation data (XII) and transpartation

data (XIII). In specifying cost summation categories, as with aggregating

commodities into a cost category, users should exercise care to insure that

the resultant figures convey the intended meaning. Transportation data are related closely to the packer and van distance files. As will be shown in

section 3.3, the distances are measured in terms of miles, minutes, thousands

of dollars per commodity unit, or dollars per ton as selected by the user.

Users may have to refer to other sources for certain types of input data which

may not be available in their local jurisdictions. Additional references^ »2

provide possible sources for such generic information.

^Columbia University Urban Technology Center, Resource Recovery Technology

for Urban Decision Makers, January 1976.

-NBSIR 81-2417, Technical Activities 1981; Office of Recycled Materials ; NBS,

November 1981.

23
Exhibit 3.1 Worksheet I: General Information

This worksheet relates to general information and data needed for the study

about various Indices, the composition of MSW and its heat value, collection

costs and market prices for source separated commodities. Percentages by

weight for the different elements in the waste stream should be given

according to how much can be source separated. Residue is defined as anything

that is not recoverable paper, glass, or cans. Figures are generally

available for BTU content of paper, but the BTU value of residue (as defined

above) and MSW as a whole may vary from one area to the next. The average

price at the market for glass, paper and cans is the price received at the

market, with the cost of haul not included. The cost of collection includes

only the cost up to the point the truck is filled (or until the waste

collected is hauled to be processed) , plus the cost of turnaround for

unloading. The source point is the central point of a community which is

considered the source of waste generated for that community for the purposes

of measuring distances. The cost of collection can be considered the cost of

getting the waste to that point from where it will be hauled to the waste

processing facility. The incremental cost of source separation is the cost in

dollars per ton of pre-separated MSW. The cost of source separation includes

the cost of source separation collection for paper, glass and cans if all of

these are to be separated. If a municipality is not source separating all of

these, then the incremental cost of source separation should only include the

cost of the items being source separated. For any source separation program

to be successful, the public must be educated. This added cost should be

included in the incremental cost of source separation.

1. a. Base year for all cost comparisons: . b. Number of years in the planning period: . 2. Discount rate: . 3. Inflation rate: . 4. Waste stream composition (by weight):

a. Paper: . b. Glass: . c. Cans: . d. Residue: . 5. Energy values of key commodities in millions of Btu per ton (when

processed as waste)

a. MSW: . b. Paper: . c. Residue: . 6. Source separation options:

a. No source separation is to be offered for at least one source? . b. Source separation of paper, glass and cans is to be considered for at

least one source? . 7. Expected revenue per ton for source separated commodities when delivered

at the market:

a. Paper: . b. Glass: . c. Cans: . 8. Collection costs:

a. Total cost per ton: . b. Added cost per ton due to source separation: ,

24

1
Exhibit 3.1 Worksheet I: General Information (Continued)

Since RRPLAN provides a mix of inputs for MSW-receiving sites and processes,

plus source-separation residue, plus paper, under the control of the selected

set of source separation alternatives, it becomes necessary to provide a method for generating the correct costs and energy impacts over a range of

input mixes. Two processing ratios, RPRPAP and RPRRES (questions 9. a and

9.b), translate the costs of processing paper and residue to those of

processing MSW. A third term, ALLO (question 9.c), applies only to paper

processing, and reflects the fact that in conditional source separation, paper

will be diverted suddenly, and in large quantities, between the recycled paper

market and the solid waste processing facility, as a function of whether the

current price in the paper market is above or below the trigger price selected

by the model. Therefore it is appropriate for the user to consider what part

of the cost of processing paper as solid waste is generated by the potential

of processing paper, and what part by the actual processing of paper. An ALLO

at its upper limit of 1.0 will generate all of the cost from actual paper

processed through the trigger price system. An ALLO at its lower limit of 0.

will generate all of the cost from paper potentially processed through the

trigger price system.

9. Cost conversion:

a. Ratio of the cost of processing a ton of paper to the cost of

processing a ton of MSW: . b. Ratio of the cost of processing a ton of residue to the cost of

processing a ton of MSW: . c. Allocation of paper processing cost (due to higher capacity

requirements) to actual paper processed as waste: . 10. Energy values of source separated commodities (recycled at the market):

a. Paper: . b. Glass: . c. Cans: . 11. Energy requirements associated with collection:

a. Net energy required to collect a ton of MSW: . b. Additional net energy required due to source separation: . 12. Energy conversions:

a. Energy required per dollar of haul cost to market for paper:

b. Energy required per dollar of haul cost to market for glass:

c. Energy required per dollar of haul cost to market for cans:

13. Energy categories:

a. Paper at the market: . b. c. Cans at the market: . d. e. Collection (net f.

energy): . 14. Cost categories:

a. Paper at the market: . b.

c. Cans at the market: . d. e. Source separation: .

Glass at the market:

BTU differentiation:

Source separation

(net energy)

Glass at the market:

Collection:

25
Exhibit 3.2 Worksheet II: Paper Market Data

The market data for source separated paper requires a worksheet separate from

that used for other markets because paper is treated differently. Source

separated paper may either be sold for its fiber content or burned for its BTU

value, subject to user inputs on source separation options and the market for

recycled paper. This worksheet provides an opportunity to examine the paper

market in detail. Question 1 addresses the scope of the available markets for

selling paper. A local paper market is one that is small enough to be

affected by quantities generated within the model. A national market is not.

Questions 2, 3 and 4 provide data for construction of a price-frequency

distribution for paper sold in a national market. The price-frequency

distribution shows how often the price of paper falls within each price

interval

1. Is the market local or national? . 2. Critical paper market prices:

a. Lower limit on price: . b. Number of intervals in paper price distribution: . c. Size of intervals in paper price distribution (dollars per

ton) : . 3. Paper price distribution:

Interval number 1 2 3 4 5 6 7 8 9 10

Frequency

4. Issues related to the trigger price system:

a. Trigger prices to be considered (dollars per ton):

X* j2» y3* y3* • b. Percent of paper burned for each trigger price (optional):

c. Average price of paper sold in the market for each trigger price

(optional)

1* }2* ^3« }3« •

NOTE: If trigger prices are being used, enter figures for questions 2, 3 and

4. a, leaving questions 4.b and 4.c blank, (for INPAP = 1); or enter figures

for question 4.a-4.c, leaving questions 2 and 3 blank, (for INPAP=0). See

exhibits 3.16, 3.18, and 3.19, for an example of how these data are handled by

the model.

26
Exhibit 3.3 Worksheet III: Cost Category Data

The worksheet for cost category data identifies who pays what and to whom.

The user must assign a name to each category and determine whether the

category is an operating or capital cost » Capital costs should be the costs

of construction or of major equipment. Operating costs include all revenues from processes and everything not considered a capital cost. Maintenance

costs, cost of administration, salaries, etc, would all be included under

operating cost. The weights are used for optimizing in LOPT = 3 and 4 (see

exhibit 3,22). For each type of category a differential inflation rate should

be given. The differential inflation rate is the difference between the

general inflation rate and the inflation rate for that particular category

given as either positive (greater increase in prices) , negative (a lower

increase in prices, or a decrease) or zero (no difference from general

inflation rate) , This information is needed to determine future costs in

constant dollars, and to give future current costs. For capital cost

categories, three additional questions must be answered: useful life of the

category, years to capitalization, and the interest rate. The useful life of

the category is the length of time that the asset, whose cost is represented

by this category, will be useful to the owner for the purpose intended upon

purchase. The number of years to capitalization is the number of years

expected to pay off the loan, bond or whatever method was used to borrow the

money. The years to capitalize may be less than or equal to the useful life.

The interest rate is the expected interest rate on the loan to pay off this

capital category. The interest rate may be a weighted average of 2 or more

interest rates, since there may be two or more loans used in a capital cost

category. Two different loans for one facility may also be represented

through two different capital cost categories.

1. a. Name of this cost category: . b. Number of this cost category: . 2. Is this an operating or capital cost category? _. . 3. Weight given to this cost category: . 4. Differential inflation rate (in per cent per year) for this cost

category: . 5. Answer sections a through d only if this is a capital cost category:

a. Useful life in years: . b. Years to capitalization for the loan: . c. Interest rate for the loan: . d. If this cost category corresponds to capital which was already in use

at the start of the study period (i.e., existing capital), then give

the number of years remaining prior to its need for replacement:

27
Exhibit 3.4 Worksheet IV: Energy Category Data

No energy categories are required in order to use the model. If the user does

not assign one, the model will create an artificial category for accounting

purposes. The name should include the unit of measure of the category. The

weights are used for optimizing in LOPT = 3 and 4 (see exhibit 3.24).

1. a. Name of this energy category:

b. Number of this energy category:

2. Weight given to this energy category:

Exhibit 3.5 Worksheet V: Commodity Category Data

This worksheet provides data on the commodity categories for haul costs and

energy haul requirements. Up to 20 commodity categories may be used in the

model. The first five are for pre-determined source-origin commodities:

1 . MSW in a packer

2. MSW in a van;

3. Source-separated paper in a van;

4. Source-separated paper in a packer; and

5. Source-separated residue in a van. The remaining fifteen may be freely defined by the user.

1. a. Name of this commodity category: . b. Number of this commodity category: . 2. Haul costs (categories may be used for cost sharing)

a. First cost category for hauling this commodity: . b. First cost factor (e.g., cost per ton mile) for hauling this

commodity: . c. Second cost category for hauling this commodity: . d. Second cost factor for hauling this commodity: . 3. Energy haul requirements (energy factors are energy requirements for

haul per ton-mile)

a. First energy category for hauling this commodity: . b. First energy factor for hauling this commodity: . c. Second energy category for hauling this commodity:

d. Second energy factor for hauling this commodity: .

28
Exhibit 3.6 Worksheet VI: Source Data

The information provided on this worksheet relates to where MSW is generated,

the annual amount, and available options for source separation. For purposes

of the model, it is assumed that all MSW generated by households, businesses

and industry was generated at the source point or centroid. A separate

worksheet must be filled out for each source. A source usually is defined as including one or more towns, cities, counties or districts. The source point

or centroid is an exact location within the source area in the geographic or population center that can be used for distance measurements. It may be

convenient to consider as the source point a location where a transfer station

is located or the end of a collection route for a packer. The point chosen is

considered (for the sake of the model) as the point where all collection (and

thus collection costs) end, and where hauling (and thus all haul costs from

the source) begin. The total region being considered by the model may be

broken into as many as sixty different sources. We suggest, however, that

the user not call for more than thirty sources, in order to leave space for

other elements in the model.

1. a. Name of this source: . b. Number of this source: . 2. Depth of tracing requested: . 3. Location of this source: . 4. Thousands of tons per year (KTPY) of MSW generated by this source

5. Source separation options:

a. No source separation: . b. Unconditional source separation: . c. Conditional source separation market paper according to the

following set of triggers:

1* ^2* ^3* ^3* • 6. Marketing considerations:

a. Cost category for haul of paper, glass and cans to the market:

b. Energy category for haul of paper, glass and cans to the market:

c. Cost per ton to haul paper to the market:

d. Cost per ton to haul glass to the market:

e. Cost per ton to haul cans to the market:

ii

29
Exhibit 3.7 Worksheet VII: Site Data

This worksheet provides information on candidates sites. A site is a location

where one or more processes is to be offered. The sites offered may either be

existing or proposed. A worksheet must be completed for each site. The entry

for site preparation costs covers the costs incurred when preparing a site for

a facility. They include roads, fences, and land development costs, but they

do not include costs for construction of the facility itself. In general,

they are costs which are site-specific rather than process-specific or site-process-specific. The information requested on tracing permits the

procedure to trace all site splits to the depth specified, through the output

coefficients of each process, and commodity splits, to a series of sites and

markets

1. a. Name of this site: . b. Number of this site: . 2. Depth of tracing requested: . 3. Location of this site: . 4. Incoming commodity: . 5. Site preparation information:

a. Cost category for site preparation:

b. Total site preparation costs: . c. Energy category for site preparation:

d. Total energy required for site preparation:

6. Capacity considerations:

a. Is the site capacitated? . b. Is landfill possible at the site? .

Exhibit 3.8 Worksheet VIII: Landfill Data

If a land-using process is to be offered at a site, then the information on this worksheet is required for the location of the site. Several sites may be

proposed at the same location; if so, they will be subject to the land

availability entered here for that location. The model does not require the use of landfills as a primary disposal option. But provision for residues

from a resource recovery facility must be made.

1. a. Name of this landfill: . b. Number of this landfill: . 2. Location of this landfill: . 3. Land available at the landfill in acre-feet^:

^An acre-foot is a volume of water (or fill) that will cover an area of one acre to a depth of one foot (i.e., 43,560 cubic feet or approximately 1610

cubic yards)

30
Exhibit 3.9 Worksheet IX: Process Data

The process data worksheet provides for entry of data specific to various generic

processes to be considered (e.g., a waterwall incinerator is a type of process - not

the Fourth Street Incinerator Facility which is a site where that process is used).

The data defined for a single process will apply whenever that process is selected.

If major differences exist between two processes of the same type, each should be

defined as a different process and a full set of information should be provided for

each. In completing questions 6 and 7, the intercepts for the cost and energy

functions should be recorded under the column heading "Fixed"; the slopes of these

functions should be recorded under the column heading "Variable."

1. a. Name of this process: . b. Number of this process: . 2. Input commodity: . 3. Was this process in use at the start of the study period? . 4. If landfill is to performed on site, how many acre feet per

kiloton of input are required for on-site disposal? . 5. Output commodities:

1 2 3 4 5 6 7 Name

Coefficient

6. Cost allocations:

a. Number of first cost category: _

b. Number of second cost category:

c. Estimated costs for this process:

First Segment Second Segment Third Segmemt

Fixed Variable Fixed Variable Fixed Variable

First Cost Category

Second Cost Category

7. Energy requirement:

a. Number of first energy category:

b. Number of second energy category:

c. Estimated energy for this process:

First Segment Second Segment Third Segment

Fixed Variable Fixed Variable Fixed Variable

First Energy Category

Second Energy Category

31
Exhibit 3.10 Worksheet X: Site-Process Combination Data

This worksheet defines what processes are to be offered at what sites, and

provides information that is site-process specific. The capacity limit assumes that this process is the only one selected at this site. The revenues

are net of costs and energy requirements for haul of recoverables to the

market

1. a. Name of candidate site: . b. Number of candidate site: . 2. a. Name of candidate process: . b. Number of candidate process: . 3. Capacity limit: . 4. Linear segments offered: 1. ; 2. ; 3. . 5. Implicit revenues for recoverables not defined through output

coefficients

a. Number of cost category for process revenues: . b. Process revenues (dollars per ton input): . c. Number of energy category: . d. Energy value of implicit revenues produced by this site-process

combination:

32
Exhibit 3.11 Worksheet XI: Market Data

The market data worksheet provides for the entry of relevant information about

marketing those commodities defined as (explicit) process outputs. An example

of a commodity could be process steam or electricity which is sold to an industrial plant or nearby factory. A market data worksheet should be

completed for each commodity-location combination. Cost categories as used

here show where revenues accrue. Revenues may be thought of as negative

costs

1. a. Name of this market: , b. Number of this market: . 2. Location of this market: . 3. Commodity being sold: . 4. Market structure:

a. Market bounds (measured in cumulative commodity units):

1* }3* ^A* y3* • b. Number of first cost category: . c. Number of second cost category: . d. Revenue per unit by segment:

1 2 3 4 5

First Cost Category

Second Cost Category

5. Energy allocation:

a. First energy category for the energy value of the commodity in the

market: . b. First energy value of the commodity in the market: . c. Second energy category for the energy value of the commodity in the

market: . d. Second energy value for the commodity in the market: .

33
Exhibit 3.12 Worksheet XII: Cost Summation Data

This worksheet permits the user to differentiate operating costs and capital

costs for any of the inputs, including the weights for optimization, and then

to aggregate them into a single cost summation category for purposes of

reporting costs. The user may enter up to ten 2-digit cost category numbers

and may list a cost category in more than one cost summation category. The user may want to employ separate categories to divide public versus private

costs or municipal versus county or state costs (see exhibit 4.13). The way

they are divided does not affect the solution or the system summary of costs.

The model does, however, print implications of the solution in terms of the

cost categories and the cost summation categories for the convenience of the user.

1. a. Name of this cost summation category:

b. Number of this cost summation category:

2. Cost categories to be aggregated:

a. First cost category: . b. Second cost category: . c. Third cost category: . d. Fourth cost category: . e. Fifth cost category: . f. Sixth cost category: . g. Seventh cost category: . h. Eight cost category: . i. Ninth cost category: . j. Tenth cost category: .

34
Exhibit 3.13 Worksheet XIII: Transportation Data

There are three types of transportation linkages (see exhibits 3.37 through

3.39). A type 1 linkage represents a shipment of one of the first five

commodities from a source to a site. Each card defines a linkage from a source to a site which can serve as an offload. There must be at least one type 1 linkage per source. A type 2 linkage represents a shipment of a process output to another site. For each site, there must be at least one type 1 or_ 2 linkage offloading at that site. A type 3 linkage represents a shipment of a recoverable from a site to a market. There must be at least one input link to each market. There must be at least one type 2 or_ 3 linkage per

commodity/origin location combination. Directions for setting up the distance

files are given in section 3.3. (Distance measures are: (a) miles;

(b) minutes; (c) thousands of dollars per commodity unit; or (d) dollars per

ton.

)

Type 1: Source-to-Slte

1. Origin (source number): . 2. Destination (site number): .

Type 2: Site-to-Site

1. Origin (location number of site): . 2. Destination (site number): . 3. Commodity nvimber of the output hauled along this linkage:

Type 3: Site-to-Market

1. Origin (location number of site): . 2. Destination (market number): . 3. Commodity number of the recoverable hauled along this

linkage: ,

35
3.2 CASE STUDY INPUT FILE

The purpose of this section is twofold. First, and foremost, it is intended

to give a line-by-line description of the various inputs which make up the case study file. Second, it is intended to serve as a crosswalk between the

data collected on the worksheets described in the previous section and the

three input files. The emphasis in this section will be on the case study

file referred to here as ACASE and shown as exhibit 3.14.

Two levels of descriptive detail will be used to systematically lay out the

Inputs to the model. The first level consists of a quick walk through of

exhibit 3.14, relating each section of the file to a worksheet. Several

simplifications will then be discussed prior to an in-depth analysis of each

basic type of data input.

A cursory review of exhibit 3.14 reveals a sequence of numbers going down the

left hand side of the page. These line numbers show the position of each type

of data within the file. Just to the right of the line number is a two digit

number referred to as the read KEY.^ This number is incremented within the

MAIN program and, along with information provided on the first card, controls

how all of the case study inputs are read.

It is important to point out that a good deal of the information on the first

card was not requested directly on any of the worksheets described in the

previous section. There are two reasons for this approach. First, there are five run controls which treat such technical issues as the last phase for the

solution, the type of forcing to be used, the forcing override options, the

types of outputs desired from the optimizer and finally the optimizing mode

itself. Since these controls are the same for all users, they are not data in

the same sense as rates of waste generation, mileages, processing costs, etc.

It was therefore decided to postpone their discussion until section 5.1 where

guidelines for setting up a run are given. Second, practical experience has

led us to the conclusion that when the decision to collect data is made, the

ntimber of commodities, cost and energy categories, sources, sites, landfills,

processes, site-process combinations, markets, and cost summation categories,

is not known with any degree of certainty. As the data collection process

progresses, questions arise, become resolved and generate new questions and/or

issues. Thus the set of data which ends up actually being analyzed by RRPLAN

may differ in detail, focus or analytical strategy from that envisioned

originally.

The cards which define the paper price distribution are the only ones which

do not have a read KEY. Line number 8 in the ACASE file defines the paper

price distribution for the case study.

36
Exhibit 3.14 Listing of a Typical Case Study File

1 01310030 20 1983 10. 0000 6. 84244552 931 20.

2 02 TEST CA.SE

3 03 23.211 .8 8. 57. 5. 0 6.0 25 .49 15. 1 1 20 1 2. 11131 1 .8 4 04 1 8 1.1 .25 .20 1 5

5 0520. 12.0 12 .0 15.2 2 1 10. 1

- 999,

6 06 15. 20. 25.

7 07 5 5. 12. 8 10. 20. 30. 20. 10. 9 08 1 MUNICIPAL COST 0 1 . 2. 10 08 2 CONTRACTOR OP COST 0 1 . -3

1 1 08 3 CONTRACTOR CAP COST 1 1 . 1 . 20 1 5 9

12 08 4 SITE PREP COST 1 1 . 0. 50 1 5 9

13 09 1 FUEL INPUT, MBTU - 1 . 14 09 2 STEAM OUTPUT, MBTU - 1 , 15 10 1 MSW IN PKR, KTON 1 1 . 1 0 1 20 1 0. 16 10 2 IVtSW IN VAN, KTON 1 1 . 1 0 1 25 1 0.

1 7 10 3 S-S PAPER, KTON 1 .

7

1 0 1 15 1 0. 18 10 4 S-S RES IN PKR. KTON 1 1 . 1 0 1 20 1 0. 19 10 5 S-S RES IN VAN, KTON 1 1 . 1 0 1 20 1 0. 20 10 6 STEAM, MLBS 2 1 . 2 0 2 1 2 0. 21 10 7 PROC RESIDUE, KTON 2 .8 2 0 1 20 1 0. 22 10 8 INCIN FERROUS, KTON 2 .7 2 0 1 10 1 0. 23 119 1 LYNN 1 340. 01 1 1

1

1 1 2.0 1.5 1 . 2 24 119 2 MARBLEHEAD 2 7. 1

1

2.2 1.7 1 . 4 25 119 3 SALEM 3 120. 01 1 1

1

2.1 1.6 1 . 3

26 119 4 REVERE 4 40. 01 1 .9 1.4 1 . 1 27 129 1 SAUGUS PROC 05 2 10 1 41000. 1250

28 129 2 WORCESTER PROC 06 2 00 2 41 000. 1250

29 129 3 AMESBURY LANDFILL 07 7 01 1 4200. 150.

30 129 4 SAUGUS SECONDARY PR 05 7 01 1 4400. 1 100

31 13 7 A^^ESBURY LANDFILL 99800.

32 13 5 SAUGUS LANDFILL 84000.

33 14 1 TRANS STA 201 30 2 3 1 1 34 15 1 21 .0 35 16 1120. 20. .8 0. 1 .8 1 .8 .5 0. 36 16 1240. 35. .7 0. .50 61 .4 0. 37 16 1360. 55. .6 0. .26 24 .3 0. 38 14 2 WATER-WALL INCIN 21310 2 3 1 1 39 15 2 65.48 7.15 8.07

40 16 21300. 0. 70 0. 7.3 0 2. 0.

41 14 3 WATER-WALL INCIN 20320 2 3 1 1 42 15 3 65.48 7.15 8.07

43 16 31300. 224.16 80 0. 7.3 7 .0 2. 0. 44 16 321000. 1096.64 170. 0. 5.0 4 .236 1 .5 0. 45 14 4 LANDFILL 70031 1

.

653 2 3 1 1 46 15 4 47 16 4140. 40. 1 . 6 0. 3.6 3 .6 1 .0 0. 48 16 4280. 70. 1 . 4 0. 1 .0 1 .22 .8 0.

49 16 43120. 110. 1 . 2 0. .52 48 .6 0.

50 14 5 SECONDARY PROC 70021 .82 2 3 1 1 51 15 5 52 16 5140. 40. 1 . 6 0. 2. 2 1 .0 0. 53 16 5280. 70. 1 . 4 0. 1 . 1 0.8 0. 54 17 1 2 360. 1.0 1 2 1 . 2 55 17 2 1 1 1 1 2 10 .0 56 17 2 3 1.0 1 1 2 1 . 3

57 17 3 4 1 1 2 10 .0

37
Exhibit 3.14 Listing of a Typical Case Study File (Continued)

58 1745 3.0 1 211.0

59 18 1 GE LYNN STEAM 5 6 2 12 1 225.4 -.2

60 19900. 1800. 1 61 203.0 2.0 0. 0. 1 62 18 2 NEW YORK FERROUS 8 8 1 1 2 110. 2 -.2

63 19 2

64 2011.86 0. 2 65 18 3 WORCESTER STEAM 9 6 2 12 1 225.4 -.2

66 192000. 4000. 3

67 208.0 6.0 0. 0. 3

68 21 1 CONTRACTOR COST 2 2 3

69 22

70 22

71 22

72 22

73 22

74 22

75 22

76 22

77 22

78 22

79 22

80 22

81 22

82 22

83 22

84 22

85 22

86 23

1 1 1

1 1 2

1 2 1

1 2 2

1 3 1

1 3 2

1 4 1

1 4 2

2 2 6 1 2 7 5 3

2 7 5 4

2 7 6 3

2 7 6 4

3 6 5 1 3 8 5 2 3 6 6 3

3 8 6 2

38
Keeping this in mind, it can be asserted that most of the data on line numbers

1 through 8 come from the first two worksheets or are based on counting the

number of each of the Worksheets III through XII which were filled out. The

relationship between Worksheets III through XIII and the entries on line

numbers 9 through 86 can be brought into sharp focus , and will be described in some detail. Referring to line numbers 9 through 12 and 13 and 14 in exhibit

3.14, similarities between the information recorded on Worksheet III and IV

respectively begin to emerge. In this case. Worksheet III would be filled out

four times, one time for each distinct cost category; whereas, Worksheet IV

would be filled out twice, once for FUEL INPUT and once for STEAM OUTPUT. The

relationship between specific items on the worksheet and in the file become

explicit when exhibits 3.15 through 3.39 are examined in the latter part of

this section.

Continuing with exhibit 3.14, line numbers 15 through 22 correspond to

information on the eight commodity categories to be analyzed in the run. Recall that the first five commodity categories are reserved for source generated wastes. There would be one copy of Worksheet V filled out for each

commodity category (i.e., the number of commodity categories defined on card 1 would be 8)

On lines 23 through 26 and 27 through 30 are recorded the data on the four sources and four sites, respectively. Each source would have one Worksheet VI

filled out for it; and each site a Worksheet VII. Data on the two landfills

would then be recorded from Worksheet VIII.

Information on each process begins at line 33. Notice that the read KEY

ranges in value from 14 to 16, reflecting the three types of process data.

All data for a given process (e.g., a new transfer station) are recorded on Worksheet IX. In this case, since there are 5 processes, it would be

necessary to fill out five such worksheets. Note that the first process must

always be a new transfer station, since the model attempts to reduce the

overall costs of the plan by siting a transfer station. at each source's

centroid.

Proceeding down the file, on lines 54 through 58, where the read KEY is equal

to 17, is recorded the site-process combination data collected on Worksheet X.

Information on each market is similar to that for processes in the sense that

all data on a given market is recorded on a single questionaire , Worksheet XI.

Data on cost summation categories follows; it is based on the information

recorded on Worksheet XII.

Lines 69 through 86 contain information on transportation linkage. To the

right of the read KEY is the transportation category. The three

transportation categories are: (1) source-to-site; (2) site-to-site; and (3)

site-to-market. The first transportation category is expanded once all data

have been input. The degree of expansion, as explained in section 3.3, is

determined by the various combinations of run controls and source controls

governing the source separation options.

39
The order in which all data from the ACASE file are read, is sununarized in

table 3.1. For all card types, except the paper market price distribution

function data, the column labeled KEY corresponds to the entries in the first

two columns of the card. All cards within numbered card types must be entered

sequenced by number (e.g. cost category 1, cost category 2, etc.). Except for

process cards (KEY 14, 15, and 16) and market cards (KEY 18, 19, and 20), all

cards must be entered in order of card type. Transportation cards (KEY 22)

must be sequenced by category type (i.e., all category 1 cards should be

entered before the first category 2 card, and all category 2 cards should be

entered before the first category 3 card) . The transportation data must be

followed by a single card with 23 entered as the read KEY. Figure 3.1

illustrates how the ACASE file would appear in practice. All card types are labeled. If the input status for a particular card type is conditional, the

card is so labeled. The figure also illustrates which card types are composed

of a single card and which card types require a block of cards.

The remainder of this section consists of a series of exhibits. Each exhibit

has two parts. In part A, the names of the data inputs and their sequence are given. The schematic representation of the card is keyed into the earlier

discussion of exhibit 3.14 and the 13 worksheets. The first entry gives the

line number in the file whereas the second gives the read KEY. The titles of

the exhibits are designed to facilitate reference to the worksheets where

variable names can be matched to the questionaire. Part B contains a brief

description of the data on the card and its purpose. Also included is a table

summarizing such critical factors as the type of variable, where exactly it

should appear on the card and its maximum and minimum values.

A brief description of FORTRAN variables will now be presented for those

readers who may not be familiar with the variable specifications used in that

language. A variable is a symbolic name that identifies a single storage area

in which its current value can be found. The size of this storage area and

the type of value to be put in that area are determined by the type declared

implicitly or explicitly for that variable. The allowed types for variables

are: integer, single or double precision real, single or double precision

complex, character or logical. RRPLAN uses integer, single precision real and

character variables as inputs. All variables which appear in the tables

contained in part B of the exhibits are defined as: (1) In, refers to an n- digit integer; (2) Fj.k, refers to a j-digit single precision real number, the

value of j is the total field size, with k digits to the right of the decimal

point (obviously j is greater than or equal to k)^; and (3) mAp, refers to a

If a decimal is not entered in an F field, it is implied by the value of k,

and occupies no space in the field. If a decimal is entered in an F field,

it is used as entered (overriding the value of k) but it occupies one space

in the field.

40
Table 3.1 User Input Groups

KEY Group Type Input Status

01 Control Card Required always as first record.

02 Title Card Required always as second record.

03 General Information Required always as third record.

04 Source Energy Card Required always as fourth record.

05 Energy Factors Card Required always as fifth record.

06 Paper Market Data Card Conditional: omit if KSEP0=2 or NTRIG=0.

07 Paper Market Price Conditional: omit if KSEP0=2 or NTRIG=0.

Distribution Function Header — Paper Market Price Conditional: omit if KSEP0=2 or NTRIG=0,

Distribution Function Data or INPAP=0.

08 Cost Category Data Required: NCC cards must be entered

sequenced by number.

09 Energy Category Data Conditional: omit if NEC=0, otherwise

NEC cards must be entered sequenced

by number.

10 Commodity Category Data Required: NCO cards must be entered

sequenced by number.

11 Source Data Required: NSU cards must be entered

sequenced by number.

12 Site Data Required: NSI cards must be entered

sequenced by number.

13 Landfill Data Conditional: omit if NLA=0, otherwise

NLA cards must be entered. No sequence

required, but referred sequence is by

location.

14 First Process Card Required.^

15 Second Process Card Required.*

16 Third Process Card Required. ^^

17 Site-Process Combination Data Required: NSP cards must be entered. No

sequence required, but preferred

sequence is by process number within

site number.

18 First Market Card Conditional: omit if NMK=0.^

19 Second Market Card Conditional: omit if NMK-0.*^

20 Third Market Card Conditional: omit if NMK=0.^

21 Cost Summation Data Conditional: omit if NCS=0, otherwise

NCS cards must be entered sequenced by

number.

22 "ransportation Data Required: sequencf«i category type.

23 End Card for Transportation Required always as last record.

File

^Enter the first, second and third process cards sorted by process number

(major) and card type (minor). Enter NPR sets.

^The number of third process cards in the set must be equal to NSEG(K), the

number of linear segments for the Kth process, entered on the first process

card.

^Enter the first, second and third market cards sorted by market number

(major) and card type (minor). Enter NMK sets.

41
42
character string with m blocks of p characters (i.e., mp characters). All

integer variables must be right justified (i.e., the units digit must be in

the right-most column of the field). For example, if the variable

specification was given as 13 and the first column on the card which the

variable could occupy was 9, then the value must be entered in columns 9, 10

and 11 of the card. If it was desired to enter a value of 17, then the 7 would be placed in column 11 and the 1 in column 10. Column 9 would be left

blank. If 1 were entered in column 9 and 7 in column 10, then the number

would be read as 170. There is no need to right justify real variables if a decimal point is entered, since the position of the decimal overrides the

specification declared on the read statement. In any event, the user should

exercise some care to insure that all columns are properly aligned.

In the discussion which follows, all inputs from one card to another are treated as if they are independent. This simplification was made for ease in

exposition because it facilitates a thorough analysis of all data which appear

on a given card. Within the program, however, some data have more attributes

than are indicated on the exhibits. For example, in exhibit 3.31 data on the

slope and intercept of the cost functions for processing facilities are presented. In this exhibit, these slopes are referred to as CSLO(l), CSL0(2),

CINT(l) and CINT(2), respectively. (Two cost categories are needed because

economies of scale may exist in both construction and operation and

maintenance costs.) In reality (i.e., within the program) there are two

separate slope and intercept pairs for each process and each linear segment

within that process. The slopes and intercepts as used within the program,

therefore correspond to three dimensional arrays. It was felt that accounting

for all such pieces of information might lead to confusion. For those readers

who may wish to delve into the source code or just get a better grip on the

way in which all of the input data fits together, a technical appendix has

been prepared. The appendix lists each input, defines its purpose, provides

dimensions for each array, as well as the read KEY and location of the

variable on the 80 column card. Additional detail may be found in the

Programmer's Manual.

If the reader studies exhibits 3.15 through 3.39 and follows the instructions

on the summary tables which appear in part B of each exhibit , even though some

of the terms have multi-attributes, problems in setting up the case study file

should not be encountered. This statement can be justified by noting that it

would be possible to have a completely separate naming convention for all

variables when input and when used in data manipulations within the program.

Such a convention was not practical for RRPLAN due in part to the number of

inputs. As pointed out earlier, users should attempt to reconstruct the data

for the ACASE file and run it on their operating system to reinforce their

understanding of model. This should be done prior to an analysis of any

"real" data with the model.

43
Exhibit 3.15 Case Study File; Program Controls and General Data

Part A; Variable Definitions and Sequencing

nTFOD

tmmnme CHica

DMTiutccnu ramUT COHTMK CTIIUIMUU*

l.tOUAKfl

ocoieikno

tTATMHI COHTWIi

(•. COMIOCII;

t, lUPMUI)

MITUtFUtMU

SOLUTIOW COWTROL

(•. turmu wrcNiiunaH;

1, MOVIM MTOnUTHM)

AMMUAL WrUTION

RATE N « TOR VCAR

AVERAGE

PRICE OF

PAPER AT THE MARKET

IN t PER TON

NUMBER OF COST

SUMMATION

CATEGORIEf

LAST LOCATION NUMBER

NUMBER OF

SITE-PROCESS COMBINATIONS

44
Exhibit 3.15 Case Study File: Program Controls and General Data

Part B, Interpretation

The first card in the case study file sets the controls for the optimizer

based on the values contained in the six entries LPHASE, NFORC, KOVER, KOPT,

LOPT, KFOUT and KSUPP. The economic scope of the analysis is determined to a great degree by NYR, IFIRST, DISCO and FLATO and to a lesser extent by ISQR,

IXDED and PRPAP. All other entries serve to define end points of do loops

through which much of the remaining data is read in.

Variable Variable First Minimum Maximi

Name Specification Column Value Value

KEY 12 1 1 1 LPHASE 11 3 3 4

NFORC 11 4 1 2

KOVER 12 5 0 99

KOPT 11 7 1 3

LOPT 11 8 0 4

NYR 12 12 1 ,99

IFIRST 14 15 _ —

DISCO F8.4 19 0.0 50.0

ISQR 11 27 0 1 IXDED 11 28 0 1 KFOUT 11 29 0 9

KSUPP 11 30 0 1 FLATO F8.4 31 0.0 50.0

NCO 12 40 5 20

NCC 12 43 1 50

NEC 12 46 0 20

NSU 12 49 1 60

NSI 12 52 1 50

NPR 12 55 2 30

NSP 12 58 1 80

NLA 12 61 0 30

LSTLO 13 64 1 999

NMK 12 68 0 30

NCS 12 71 0 10

PRPAP F5.2 75

45
Exhibit 3.16 Case Study File: General Information

Part A: Variable Definitions and Sequencing

PERCENTAGE

OF SOURCE

SEPARABLE

PAPER IN THE WASTE

STREAM

PERCENTAGE

OF CANS

INTME WASTE

STREAM

PERCENTAGE

OF GLASS

IN THE WASTE

STREAM

NUMBER OF MBTU/TON

AVAILABLE FROM

RESIDUE

PERCENTAGE

OF RESIDUE

IN THE WASTE

STREAM

PRICE OF

GLASS AT THE MARKET

IN S/PER TON

NUMBER OF MBTU/TON

AVAILABLE FROM PAPER

PRICE OF CANS AT THE MARKET

IN S/PER TON

COST CATEGORY NUMBER FOR

GLASS AT THE MARKET

COST CATEGORY NUMBER FOR CANS AT THE MARKET

TOTAL COST

OF COLLECTION

IN S/PER TON

COST CATEGORY

FOR

INCREMENTAL COST

OF

INCREMENTAL COST OF SOURCE

SEPARATION

IN $/PER TON

COST CATEGORY

FOR TOTAL COST OF

COLLECTION

RUN CONTROL

SPECIFYING WHETHER

UNCONDITIONAL MSW IS AVAILABLE AT ANY SOURCE

RUN CONTROL SPECIFYING WHETHER UNCONDITIONAL SOURCE SEPARATION IS AVAILABLE AT ANY SOURCE AND WHAT TYPE OF PAPER MKT. EXISTS

RATIO OF

THE COST OF PROCESSING

RESIDUE TO

THE COST OF

PROCESSING MSW (PER TON)

RATIO OF THE COST OF PROCESSING PAPER TO THE COST OF PROCESSING MSW (PER TON)

COST CATEGORY

FOR THE

PAPER MARKET

INPUT MODE

FOR PAPER

PRICE DISTRIBUTION

NUMBER OF

TRIGGERS USED WITH CONDITIONAL SOURCE

SEPARATION

46
Exhibit 3.16 Case Study File: General Information

Part B: Interpretation

This card establishes values for the composition of the solid waste stream and

the BTU content of its components. Information on collection costs, potential

revenues and their associated cost allocations are given as well as controls

which govern the use of the source separation options for the rest of the

analysis. Two variables which appear on this card have an effect on the

turnaround time for unloading. Both terms, CSPA and CSPB, will be discussed

in detail is section 3.3. The standard turnaround time for unloading a packer

should be included in the total cost of collection, CSPA, in the form of a cost per ton. Since the unit of measure of source-separation alternatives is

(thousand) tons of preseparated MSW, there is a saving in turnaround cost in

that the packer, now actually carrying source-separation residue, carries a larger payload if measured in equivalent preseparated MSW. This saving should

be reflected in the value of CSPB. The last entries provide information on the

relative costs of processing source separated paper and residue.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 3 3

PCPAP F5.2 3 0.0 90.0

PCGLA F5.2 8 0.0 90.0

PCCAN F5.2 13 0.0 90.0

PCRES F5.2 18 0.0 100.0

BTRES F6.3 23 0.0

BTPAP F6.3 29 0.0

PRGLA F5.2 35 0.0 1000.0

PRCAN F5.2 40 0.0 1000.0

ICCGLA 12 45 1 NCC^

ICCCAN 12 47 1 NCC

CSPA F5.3 49 0.0 1000.0

I SPA 12 54 1 NCC

CSPB F5.3 56 0.0 1000.0

ISPB 12 61 1 NCC

KMIX 11 63 0 1 KSEPO 11 64 0 2

NTRIG 11 65 0 5

INPAP 11 66 0 1 ICCPAP 12 67 1 NCC

RPRPAP F4.3 69 0.05 20.0

ALLO F4.3 73 0.0 1.0

RPRRES F4.3 77 0.05 20.0

^The value of NCC is entered on the control card (see exhibit 3.15).

47
Exhibit 3.17 Case Study File: Source Energy Data

Part A: Variable Definitions and Sequencing

ij $/ i/ i/ i/

04 m .8 1

ENERGY

CATEGORY

FOR

COLLECTION

OF MSW

ENERGY

CATEGORY FOR

INCREMENTAL

ENERGY REQUIRED

FOR SOURCE

SEPARATION

i/

CONVERSION FACTOR

FOR HAUL OF PAPER TO

MARKET (ENERGY

PER $ OF HAUL COST)

.20

CONVERSION FACTOR

FOR HAUL OF CANS TO

MARKET (ENERGY

PER $ OF HAUL COST)

NET ENERGY NET INCREMENTAL CONVERSION FACTOR

REQUIRED TO ENERGY REQUIRED FOR HAUL OF GLASS TO

COLLECT FOR SOURCE MARKET (ENERGY

MSW SEPARATION PER $ OF HAUL COST)

48
Exhibit 3.17 Case Study File: Source Energy Data

Part B: Interpretation

The entries shown In part A of exhibit 3.17 establish the values governing all

net energy calculations. KSPA and ESPA set the value for net energy in

collection and its allocation; whereas KSPB and ESPB determine how much

additional net energy is required for source separation. RPAH» RGLH and RCAH

are factors to convert the dollar value of haul costs to an equivalent amount

of net energy.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 4 4

KSPA 12 4 1 NEC*

ESPA F10.3 6

KSPB 12 16 1 NEC

ESPB F10.3 18

RPAH F10.5 28

RGLH F10.5 38

RCAH F10.5 48

^The value of NEC is entered on the control card (see exhibit 3.15).

49
Exhibit 3.18 Case Study File:

Part A: Variable Definitions

Energy Factors

and Sequencing

ENERGY VALUE OF PAPER IN THE MARKET

ENERGY CATEGORY

FOR PAPER

IN THE MARKET

ENERGY

VALUE OF GLASS IN THE MARKET

ENERGY CATEGORY

FOR GLASS

IN THE MARKET

ENERGY

VALUE OF CANS IN THE MARKET

ENERGY CATEGORY

FOR CANS

IN THE MARKET

ENERGY CATEGORY

FOR BTU

DIFFERENTIATION

FLAG FOR

USER DEFINED

FIXED COST

DRIVER CONTROL

-999.

USER DEFINED

VARIABLE COST DRIVER

FLAG FOR

USER DEFINED

VARIABLE COST

DRIVER CONTROL

NUMBER OF

MBTU/PER TON

OF MSW

THE USER

DEFINED FIXED COST DRIVER

50

i
Exhibit 3.18 Case Study File: Energy Factors

Part B: Interpretation

The energy values of source separated paper, glass and cans at the market and

the appropriate energy categories are given on this card. The thermal content

of MSW in millions of BTU are given as well as an energy category for BTU

differentiation among MSW and source separated paper and residue. The last

four entries are concerned with the forcing procedure. They permit the user

to set controls for overriding the fixed and variable cost forcing values set

internally. Some care should be exercised in using the override. It is

recommended that either no override or the combination entered on part A of

exhibit 3.18 be used prior to any experimentation with other values.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 5 5 EPAPM F10.5 3

IPAPM 12 13 1 NEC

EGLAM F10.5 15

IGLAM 12 25 1 NEC

ECANM F10.5 27

ICANM 12 37 1 NEC

BTMSW F10.5 39 0.05

IBTU 12 49 1 NEC

IFDRV 11 52 0 1 FDRIV F5.2 53

IVDRV 11 58 0 1 VDRIV F5.2 59

51
Exhibit 3.19 Case Study File: Trigger Prices for Paper

Part A: Variable Definitions and Sequencing

FIRST TRIGGER

PRICE FOR THE

PAPER

DISTRIBUTION

($ PER TON)

THIRD TRIGGER

PRICE FOR THE

PAPER

DISTRIBUTION

($ PER TON)

SECOND TRIGGER

PRICE FOR THE

PAPER

DISTRIBUTION

($ PER TON)

52
Exhibit 3.19 Case Study File: Trigger Prices for Paper Market

Part B: Interpretation

The user has the option to specify a set of trigger prices below which paper

will be burned for its BTU value and above which it will be sold for its fiber

content. The PTRIG(l) through PTRIG(5) entries establish these trigger

prices. The value of NTRIG given on card 3 determines how many trigger prices

the user must input. Note that the trigger prices must be increasing. The

model will select which trigger price and burn ratio are most appropriate for

each source where they are offered. The user may also specify how much paper

is burned and what the average sale price is through the values of BTRIG and

ATRIG by setting INPAP equal to zero on card 3. Both sets of values must be

increasing functions reflecting the fact that as the trigger price is

increased, more paper will be burned and the average value at the market will

be higher. If the user desires to input a frequency distribution for the

price of paper, then these values are computed internally. In such a case,

INPAP should be set to one on card 3 and no values should be entered for

either ATRIG or BTRIG.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 6

PTRIG( 1

)

F5.2 7

PTRIG(2) F5.2 12

PTRIG(3) F5.2 17 a PTRIG(4) F5.2 22

PTRIG(5) F5.2 27

BTRIG( 1

)

F4.1 32

BTRIG(2) F4.1 36

BTRIG(3) F4.1 40 b

BTRIG(4) F4.1 44

BTRIG(5) F4.1 48

ATRIG(l) F5.2 52

ATRIG(2) F5.2 57

ATRIG(3) F5.2 62 c ATRIG(4) F5.2 67

ATRIG(5) F5.2 72

6

a PTRIG(K)> PTRIG(K-l) + 0.001 for K>1.

b BTRIG(K)> BTRIG(K-l) + 0.0001 for K>1

c ATRIG(K)> ATRIG(K-l) + 0.0001 for K>1

53
Exhibit 3.20 Case Study File: Critical Paper Market Prices

Part A: Variable Definitions and Sequencing

(J

7 07 5 5. 12.

NUMBER OF

INTERVALS

IN PAPER PRICE

DISTRIBUTION

SIZE OF

THE INTERVALS

IN PAPER PRICE

DISTRIBUTION

($ PER TON)

LOWER

LIMIT OF

PAPER PRICE

DISTRIBUTION

($ PER TON)

54
Exhibit 3.20 Case Study File: Critical Paper Market Prices

Part B: Interpretation

The data provided on this card define the size and shape of the paper price

distribution. The minimum price is given by BOTTOM, whereas the maximum price

is equal to the product of NOINT and SIZINT plus BOTTOM. The minimum width of

the interval on the paper price distribution is defined so that all trigger

prices are contained within the body of the paper price distribution (i.e.,

the maximum price as computed above must be greater than the highest trigger

price)

Variable

Name

Variable

Specification

First

Column

Minimum

Value

Maximum

Value

KEY 12 1 7 7 NOINT 13 3 1 200

SIZINT F5.3 6 a 1000

BOTTOM F5.2 11 0. b

a (PTRIG(NTRIG) - BOTTOM) /NOINT.

b Greater than or equal to PTRIG(l).

55
Exhibit 3.21 Case Study File: Paper Market Price Distribution

Part A: Variable Definitions and Sequencing

FREQUENCY

WITHIN FIRST

INTERVAL OF

PAPER DISTRIBUTION

FREQUENCY

WITHIN THIRD

INTERVAL OF

PAPER

DISTRIBUTION

8 10. 20. 30. 20. 10.

FREQUENCY

WITHIN FIFTH

INTERVAL OF

PAPER

DISTRIBUTION

FREQUENCY

WITHIN SECOND

INTERVAL OF

PAPER

DISTRIBUTION

FREQUENCY

WITHIN FOURTH

INTERVAL OF

PAPER

DISTRIBUTION

56
Exhibit 3.21 Case Study File: Paper Market Price Distribution

The paper price distribution is entered as frequencies over ranges of price in

the market. The distribution is normalized before use so that the frequencies

may be entered in any form that is convenient to the user (e.g., percentages

of time at various price ranges, or number of days per year in the various

price ranges)

.

Variable

Name

Variable

Specification

First

Column

Minimum

Value^

Maximum

Value

FREQ( 1

)

F5.3 1 0.0

FREQ(2) F5.3 6 0.0

FREQ(3) F5.3 11 0.0

FREQ(4) F5.3 16 0.0

FREQ(5) F5.3 21 0.0

FREQ(6) F5.3 26 0.0

FREQ(7) F5.3 31 0.0

FREQ(8) F5.3 36 0.0

FREQ(9) F5.3 41 0.0

FREQ(IO) F5.3 46 0.0

FREO(ll) F5.3 51 0.0

FREQ(12) F5.3 56 0.0

FREQ(13) F5.3 61 0.0

FREQ(14) F5.3 66 0.0

FREQ(15) F5.3 71 0.0

FREQ(16) F5.3 76 0.0

NOINT

a I FREO(i) > 0

i=l

57
Exhibit 3.22 Case Study File: Cost Category Data (Operating)

Part A: Variable Definitions and Sequencing

MUNICIPAL COST

IDENTIFICATION

NUMBER OF

THE COST

CATEGORY

IDENTITY CODE

OPERATING "0" OR CAPITAL "1

" COST

DIFFERENTIAL

ANNUAL

INFLATION RATE

FOR THIS CATEGORY

(% PER YEAR)

NAME OF THE

COST

CATEGORY

WEIGHT GIVEN

TO THIS

CATEGORY

58
Exhibit 3.22 Case Study File: Cost Category Data (Operating)

Part B: Interpretation

This is one of the NCC cost category cards; it deals with annual recurring

costs referred to within the model as operating costs. The name of the cost

category is stored in the alphanumeric variable NAMCC. IDCC is set equal to zero indicating that this is an operating cost. Operating costs include such

categories of costs as collection and transportation as well as facility

operation and maintenance and revenues. Operating costs are inflated from the

first year of the study period to the year in which they ocur through use of

the differential inflation rate DIFLAT. Note that the WTCC and DIFLAT entries

are not edit checked. Values for WTCC are only necessary if LOPT is equal to

3 or 4 in which case positive weights for WTCC are required. At least one weight must be non zero for LOPT equal to 3. In the LOPT equal to 4 case, the

weights are not checked, but the model will select among alternatives randomly

if at least one weight is not entered. The LIFU, LIFC, RATE and NREM entries

are not required for an operating cost, so they should be left blank.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 8 8

ICC 12 5 1 NCC^

NAMCC 5A4 8

IDCC 11 29 0 0

WTCC F8.3 31

DIFLAT F5.3 39

LIFU^ 13 45

LIFC^ 13 49

RATE^ F4.1 52

NREM^ 12 56

^The value of NCC is entered on the control card (see exhibit 3.15).

Input value is irrelevant. Used only for capital costs.

59
Exhibit 3.23 Case Study File: Cost Category Data (Capital)

Part A: Variable Definition and Sequencing

IDENTIFICATION NUMBER OF

THE COST CATEGORY

CONTRACTOR CAP COST

IDENTITY CODE: OPERATION "O" OR

CAPITAL "1" COST

NAME OF THE COST CATEGORY

WEIGHT GIVEN TO THIS CATEGORY

15

YEARS FOR

CAPITALIZATION OF THE LOAN

FOR THIS COST CATEGORY

USEFUL

LIFE IN YEARS FOR CATEGORY

DIFFERENTIAL ANNUAL

INFLATION RATE FOR THIS CATEGORY

20

INTEREST RATE FOR

THE LOAN

FOR THIS COST CATEGORY

YEARS REMAINING FOR EXISTING CAPITAL FOR

THIS COST CATEGORY

60
Exhibit 3.23 Case Study File: Cost Category Data (Capital)

Part B: Interpretation

Capital items are assumed to be purchased at the beginning of every useful

life cycle, displaced by years of life remaining for existing assets (NREM)

,

with the cost being inflated by the differential inflation rate (DIFLAT) from

year 1 to the year of purchase (LIFU+1). A standard amortization calculation

is used to generate an annual cost for each year of the capitalization of the

loan based on the values of RATE and LIFC, That cost is deflated by the

general inflation rate from the year of the cost to the year of purchase to

represent the decline in constant dollars through time after the cost is

"fixed" in current dollars by the act of purchase.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 8 8

ICC 12 5 1 NCC

NAMCC 5A4 8

IDCC 11 29 1 1 WTCC F8.3 31

DIFLAT F5.3 39

LIFU 13 45 1 200

LIFC 13 49 1 LIFU

RATE F4.1 52 0.05 80.0

NREM 12 56 0 99

61
Exhibit 3.24 Case Study File: Energy Category Data

Part A: Variable Definitions and Sequencing

IDENTIFICATION

NUMBER

FOR ENERGY

CATEGORY

NAME OF

ENERGY

CATEGORY

WEIGHT

GIVEN TO

THIS ENERGY

CATEGORY

62
Exhibit 3.24 Case Study File: Energy Category Data

Part B; Interpretation

This is one of NEC energy cards. If NEC was listed as zero on card 1, then no cards are to be entered; the model will however, create an artificial energy

category for accounting purposes. All energy categories represent net energy;

thus a negative energy value in the analytical summary (see section 4.1)

implies a net requirement for energy. In entering energy requirements,

however, the user should enter positive data since the sign is changed within

the model for all such inputs. Note that WTEC should have weights entered

which are negative or zero whether the category is expected to show a net

energy surplus or a net energy requirement. Recall that for LOPT equal to 3 or 4 at least one weight should be non zero. For better correspondence with

cost inputs, it is recommended that the energy equivalent of the various

cost-per-ton inputs be one-thousandth of the energy unit per ton, with two

exceptions. First, intercept energy inputs must be entered in full energy

units. These include CEPREP (see exhibit 3.27) and the intercepts, EINT (see

exhibit 3.31), on the third process card. Second, if the user has defined the

unit of a commodity in other than thousand tons, the energy inputs into a process of which it is the input commodity including ESLO (see exhibit 3.31)

and EREVSP (see exhibit 3.32) should be in full energy units per unit defined

for the commodity.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 9 9

lEC 12 5 1 NEC

NAMEC 5A4 8

WTEC F8.3 29

63
Exhibit 3.25 Case Study File: Commodity Category Data

Part A: Variable Definitions and Sequencing

MSW IN PKR, KTON

NAME OF COMMODITY

CATEGORY

FIRST COST CATEGORY

FOR HAgUNG

THE COMMODITY

SECOND COST CATEGORY

FOR

HAULING THE COMMODITY

FIRST ENERGY CATEGORY

FOR HAULING THE COMMODITY

SECOND

ENERGY CATEGORY

FOR HAULING

THE COMMODITY

COST FACTOR

FOR

FIRST COST CATEGORY

COST FACTOR

FOR SECOND COST CATEGORY

COST FACTOR

FOR

FIRST ENERGY CATEGORY

COST FACTOR

FOR SECOND ENERGY CATEGORY

64
Exhibit 3.25 Case Study File: Commodity Category Data

Part B: Interpretation

The commodity is numbered by the assignment of ICO on the commodity category

cards; there are NCO such cards. The first five commodities must be:

1 . Mixed MSW in a packer in KTPY (kilotons per year)

;

2. Mixed MSW in a van in KTPY;

3. Source-separated paper in a van in KTPY;

4. Source-separated residue in a packer in KTPY; and

5. Source-separated residue in a van in KTPY.

Once identified, commodities are referenced by number for site inputs, process

inputs and outputs, market inputs, and transportation. Haul cost multipliers

CCOCC(l) and CC0CC(2) and energy multipliers CCOEC(l) and CC0EC(2) are also

entered on this card. These multipliers are applied against the packer and van distance file entries (van applies if ICO is other than 1 or 4)

.

Variable

Name

Variable

Specification

First

Column

Minimum

Value

Maximum

Value

KEY 12 1 10 10

ICO 12 5 1 NCO^

NAMCO 5A4 8

ICOCC(l) 12 28 1 NCC

CCOCC(l) F8.3 30 0.00001 1000.0

IC0CC(2) 12 38 1 NCC

CC0CC(2) F8.3 40 0.0 1000.0

ICOEC(l) 12 48 0 NEC

CCOEC(l) F8.3 50

IC0EC(2) 12 58 0 NEC

CC0EC(2) F8.3 60

^The value of NCO is entered on the control card (see exhibit 3.15).

65
Exhibit 3.26 Case Study File: Source Data

Part A: Variable Definitions and Sequencing

SOURCE ROUTING REQUEST CONTROL

(0, NONE; 1-9, LEVEL)

NAME

OF THE SOURCE

LOCATION NUMBER

OF THE SOURCE

IDENTIFICATION NUMBER

FOR THE SOURCE

KILOTONS PER YR. OF MSW

GENERATED

BY THE SOURCE

WHETHER

UNCONDITIONAL MSW

(0, NO; 1, YES)

IS AVAILABLE AT THE SOURCE

WHETHER

UNCONDITIONAL SOURCE

SEPARATION

(0, NO; 1, YES)

IS AVAILABLE AT THE SOURCE

FIRST TRIGGER FOR

CONDITIONAL SOURCE

SEPARATION

SECOND

TRIGGER FOR

CONDITIONAL SOURCE

SEPARATION

$ PER TON

TO HAUL

GLASS FROM

SOURCE TO

MARKET

$ PER TON

TO HAUL

PAPER FROM

SOURCE TO MARKET

ENERGY CATEGORY

FOR HAULING

GLASS, PAPER, CANS FROM

SOURCE TO MARKET

COST CATEGORY

FOR HAULING

GLASS, PAPER, CANS FROM SOURCE TO MARKET

THIRD TRIGGER FOR

CONDITIONAL SOURCE

SEPARATION

$ PER TON

TO HAUL CANS FROM

SOURCE TO

MARKET

66
Exhibit 3.26 Case Study File: Source Data

Part B: Interpretation

This is one of NSU source cards. The second entry is designed for tracing

activities to be carried out once the optimization has been completed. The

next four entries show the source number, its name, location and rate of waste

generation (in KTPY) . All remaining entries are associated with the source separation options to be considered for this source. Any combination of the seven entries IMIXSU through ITRIGS(5) may be offered. It is important to

note that the source controls are dominated by the run controls (KMIX,KSEPO

and NTRIG) listed on card 3. If the run control indicates that the

alternative was not to be considered, then the corresponding source control

will be read but will not be functional.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 11 11

ISUREQ 11 3 0 9

ISU 12 5 1 NSU^

NAMSU 5A4 8

LSU 13 29 1 LSTLO^

TSU F10.3 32 0.001

IMIXSU 11 43 0 1 ISEPSU 11 44 0 2

ITRIGS(l) 11 45 b

ITRIGS(2) 11 46 b

ITRIGS(3) 11 47 b

ITRIGS(4) 11 48 b

ITRIGS(5) 11 49 b

IHCSEP 12 50 .0 NCC

IHESEP 12 52 0 NEC

CHPA F5.2 54 0.0 1000.0

CHGL F5.2 59 0.0 1000.0

CHCA F5.2 64 0.0 1000.0

^The values of NSU and LSTLO are entered on the control card (see exhibit

3.15).

bif NTRIG=0 on the general information card (see exhibit 3.16), then no value

should be entered. If NTRIOl, then ITRIGS(K)=0 or 1 for K<NTRIG. ITRIGS(K)

should be left blank for K>NTRIG.

67
Exhibit 3.27 Case Study File: Site Data

Part A: Variable Definitions and Sequencing

SITE ROUTING REQUEST CONTROL

(0, NONE: 1-9, LEVEL)

LOCATION NUMBER

OF THE

SITE

IDENTIFICATION NUMBER

FOR THE

SITE

NUMBER

OF THE

INPUT COMMODITY

WHETHER

THE SITE HAS A

CAPACITY

LIMIT

(0, NO; 1, YES)

SITE PREPARATION COSTS ($K)

COST CATEGORY

FOR THE

SITE PREPARATION COSTS

NUMBER OF PROCESSES

OFFERED

AT THE

SITE

ENERGY REQUIRED FOR SITE PREPARATION

ENERGY CATEGORY

FOR THE

SITE PREPARATION

(ENERGY

REQUIREMENT)

WHETHER A

LANDFILL IS OFFERED AT THE

SITE

(0, NO; 1, YES)

68
Exhibit 3.27 Case Study File: Site Data

Part B: Interpretation

This is one of NSI site cards. As for sources, the second entry is designed

for tracing activities to be carried out once the optimization has been

completed. The next three entries show the site number, name and location.

The incoming commodity is then given. With the exception of a site receiving

MSW, each site must receive exactly one commodity. For an MSW receiving site,

enter 2 for INCO. If the site is not MSW receiving, INCO must be greater than

5, since the first five commodities are various forms of source generated

waste. The next three entries determine if the site is capacitated, has land

available for landfilling, and the number of processes to be considered. The

last four entries deal with site preparation activities. It is Important to

note that site preparation costs, CCPREP, are in thousands of dollars and site

preparation energy requirements, CEPREP, are denominated in the units declared

on the energy category data cards (see exhibit 3.24).

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 12 12

ISIREQ 11 3 0 9

ISI 12 5 1 NSI^

NAMSI 5A4 8

LSI 13 29 1 LSTLO

INCO 12 34 2 NCO

ICAP 11 37 0 1 ILAND 11 38 0 1 NPROSI 12 39 1 NPR^

ICPREP 12 42 0 NCC

CCPREP F10.3 44

lEPREP 12 54 0 NEC

CEPREP F10.3 56

^The values of NSI and NPR are entered on the control card (see exhibit

3.15).

69
Exhibit 3.28 Case Study File: Landfill Data

Part A: Variable Definitions and Sequencing

LOCATION

NUMBER OF

LANDFILL

NAME

OF THE

LANDFILL

LAND

AVAILABLE

AT THE

LANDFILL

IN ACRE-FEET

70
Exhibit 3.28 Case Study File: Landfill Data

Part B: Interpretation

The model allows for numerous ways for combining processing and landfilling.

J. LIX o ^CL L \1 defines a landfill, gives its location and an estimate of the land

CLV CiJ. J.CL.U X.XZ in acre feet. If the number of landfills, NLA, was listed as zero

r\n r*£jf/1 1 KJLL ^ClLU. X , then no cards of this type should be entered. There should be one card per location at which ILAND on the site card was coded equal to one.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 13 13

LLA 13 4 1 LSTLO

NAMLA 5A4 8

AVLA F10.3 29 0.001

71
Exhibit 3.29 Case Study File: Process Data (1)

Part A. Variable Definitions and Sequencing

IDENTIFICATION NUMBER

FOR THE PROCESS

TRAN STA

NAME

OF THE PROCESS

NUMBER

OF THE

INPUT COMMODITY

WHETHER

THE PROCESS IS EXISTING

(0, NEW; 1, EXISTING)

NUMBER OF OUTPUT

COEFFICIENTS FOR PROCESS

FIRST COST CATEGORY

FOR PROCESS COSTS

SECOND COST

CATEGORY

FOR PROCESS COSTS

WHETHER THE PROCESS USES AN ON-SITE LANDFILL

SECOND

ENERGY CATEGORY

FOR PROCESS ENERGY

REQUIREMENT

FIRST ENERGY CATEGORY

FOR PROCESS ENERGY

REOUIREMENTS

NUMBER OF

LINEAR SEGMENTS

FOR PROCESS

72
Exhibit 3.29 Case Study File: Process Data (1)

Part B: Interpretation

Each process is identified by a process number, IPR, and by its position in

the sequence 1 through NPR. With the exception of a process receiving MSW,

each process must receive exactly one commodity. The process input commodity,

INCOP, should be coded as 2 if it is MSW receiving; otherwise a value greater

than 5 must be entered. If the process was in existence at the beginning of

the study period lEXIS should be coded as 1. The number of commodities output

and the number of segments in the cost function are then given. The next two

entries establish a means for recording landfill activites. The last four

variables are designed for allocating costs (and energy) among two distinct

categories (e.g., for costs, operation and maintenance versus capital). It is

important to note that the first process must be a transfer station

characterized by both inputting and outputting MSW. A single output

coefficient of 1.0 is required. This first process is used by the model in

performing the dedicated transfer station analysis. Other transfer station

processes may also be entered and assigned any process number greater than

one.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 14 14

IPR 12 5 1 NPR

NAMPR 5A4 8

INCOP 12 29 1 NCO

lEXIS 11 31 0 1 NCOF 11 32 0 7

NSEG 11 33 1 3

ILACOF 11 34 .0 1 USLA F8.5 35 a ICPR(l) 12 44 1 NCC

ICPR(2) 12 46 1 NCC

lEPR(l) 12 48 0 NEC

IEPR(2) 12 50 0 NEC

a If ILACOF = 0, then the value is irrelevant.

If ILACOF = 1, then the minimum is 0.0001.

73
Exhibit 3.30 Case Study File: Process Data (2)

Part A: Variable Definitions and Sequencing

IDENTIFICATION

NUMBER

OF THE

PROCESS

OUTPUT

COEFFICIENT

FOR FIRST

OUTPUT

COMMODITY

FIRST OUTPUT

COMMODITY

74
Exhibit 3.30 Case Study File: Process Data (2)

Part B: Interpretation

For each process there is a card identifying which commodities are output and

in what quantity as a function of the amount of waste processed. All outputs

are measured in the units appropriate for that commodity (e.g., MBTU per unit

input). As pointed out earlier, for the first process (a transfer station)

there must be a single output of MSW coded as 2 with an output coefficient of

1.0. This card must be entered even if the process does not produce an output

(e.g., disposal in a landfill). In such a case the only entries are KEY and

KEMP; they serve as placeholders. Enter a number of ICOF(K) and COF(K) equal

to NCOF read on KEY 14.

Variable

Name

Variable

Specification

First

Column

Minimum

Value

Maximum

Value

KEY 12 1 15 15

KEMP 12 5 1 NPR

ICOF(l) 12 8 1 NCO

COF(l) F8.5 10 a IC0F(2) 12 18 b NCO

C0F(2) F8.5 20 a IC0F(3) 12 28 b NCO

C0F(3) F8.5 30 a IC0F(4) 12 38 b NCO

C0F(4) F8.5 40 a IC0F(5) 12 48 b NCO

C0F(5) F8.5 50 a IC0F(6) 12 58 b NCO

C0F(6) F8.5 60 a IC0F(7) 12 68 b NCO

C0F(7) F8.5 70 a

^If NCOF=

0.00001

0,

for

then no value should be entered.

K <NCOF. COF(K) should be left

If NC0F>1, then COF(K)>

blank for K>NCOF.

'If NCOF=0, then no value should be entered. If NC0F>1, then IC0F(K)>1 for

K<NC0F. ICOF(K) should be left blank for K>NCOF.

75
Exhibit 3.31 Case Study File: Process Data (3)

Part A: Variable Definitions and Sequencing

IDENTIFICATION NUMBER

OF PROCESS

FIRST COST

CATEGORY

INTERCEPT

FIRST ENERGY

CATEGORY

INTERCEPT

FIRST COST

CATEGORY

SLOPE

FIRST ENERGY

CATEGORY

SLOPE

NUMBER

OF

LINEAR SEGMENTS

BEING CONSIDERED

SECOND COST

CATEGORY

INTERCEPT

SECOND ENERGY

CATEGORY

INTERCEPT

SECOND COST

CATEGORY

SLOPE

SECOND ENERGY CATEGORY

SLOPE

76
Exhibit 3.31 Case Study File: Process Data (3)

Part B: Interpretation

Basic processing costs are entered on the third process card as slopes and

Intercepts for each linear segment of the process. There are NSEG such cards

for each process. Two cost categories (and two energy categories) are provided, enabling the user to handle a wide variety of nonllnearltles In the

cost (and net energy) functions. The basic units of the model are thousand

tons and thousand dollars. Most cost Inputs may therefore be entered In

dollars per ton because that Is equivalent to thousand dollars per thousand

tons. There are two exceptions. First, Intercept costs must be entered In

thousands of dollars. These Include the site preparation cost, CCPREP (see

exhibit 3.27), and the two cost function intercepts, CINT(l) and CINT(2).

Note that there will be NSEG values for CINT(l) and CINT(2). Second, if the

user has defined the unit of a commodity in other than thousand tons, the cost

Inputs into its process. Including CSLO(l) and CSL0(2) and REVSP (see exhibit

3.32), of the commodity, must be in thousands of dollars per defined unit of

the commodity.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 16 16

KEMP 12 5 1 NPR

LEMP 11 7 1 3

CINT(l) F9.2 8

CINT(2) F9.2 17

EINT(l) F9.2 26

EINT(2) F9.2 35

CSLO(l) F9.6 44

CSL0(2) F9.6 53

ESLO(l) F9.6 62

ESLO(l) F9.6 71

77
Exhibit 3.32 Case Study File: Site-Process Combination Data

Part A: Variable Definitions and Sequencing

(J

17

NUMBER

OF THE

SITE BEING

CONSIDERED

CAPACITY

LIMIT OF

THE PROCESS

AT THAT SITE

NUMBER

OF THE

PROCESS BEING

CONSIDERED

WHICH LINEAR

SEGMENTS OF

THE PROCESS

WILL BE OFFERED

AT THAT SITE

PROCESS

REVENUES

COST

CATEGORY

FOR

PROCESS

REVENUES

ENERGY

VALUE

FROM THE

PROCESS

ENERGY

CATEGORY

FOR ENERGY

FROM THE

PROCESS

78
Exhibit 3.32 Case Study File: Site-Process Combination Data

Part B: Interpretation

The SIPROC or site-process cards offer processes at sites. There are NSP such

cards. Each SIPROC offers one process at one site. An existing process

(lEXIS = 1, see exhibit 3.29) must be the only process at its site, but any

number of new processes may be offered at the same site. Implicit revenues

net of haul to the market for recoverables not specified through output

coefficients and commodities are entered on this card. A similar arrangement

is made for the energy values of recoverables not declared elsewhere. Due to

the location of a site, its intended shipments, or for other reasons, it may

be necessary to set an upper limit on capacity. The entry on CAPSP limits the

scale of operations for the process activities at a candidate site. CAPSP assumes that the entire site is devoted to this process. The offering of an ISPSEG(K) at a site-process does not limit the scale of activity at the site.

However, since each ISPSEG(K) generates a column in the matrix, these controls

give the user an opportunity to eliminate unnecessary columns which may render

certain scales of operation economically unattractive at the site.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 17 17

ISPSI 12 5 1 NSI

ISPPR 12 9 1 NPR

CAPSP F9.3 12 a REVSP F9.3 22

ISPSEG(l) 11 32 b 1 ISPSEG(2) 11 33 b 1 ISPSEG(3) 11 34 b 1 IREVSP 12 36 0 NCC

KREVSP 12 39 0 NEC

EREVSP F10.5 41

a If ICAP(ISPSI) = 1, then the minimum is 0.0001.

If ICAP(ISPSI) = 0, then the value is irrelevant.

b ISPSEG(K) will be functional only if K<NSEG on the first

process card. The sum of functional ISPSEG(K) for a site-process combination must be greater than or equal to 1.

79
Exhibit 3.33 Case Study File: Market Data (1)

Part A: Variable Definitions and Sequencing

GE LYNN STEAM 5 6 2 1 2 1 2 2 5.4 -.2

NAME

OF THE MARKET

LOCATION NUMBER

OF THE MARKET

NUMBER OF THE COMMODITY

FOR THE MARKET

FIRST ENERGY CATEGORY

FOR THE ENERGY

VALUE OF THE COMMODITY IN THE MARKET

SECOND ENERGY CATEGORY

FOR THE ENERGY

VALUE OF THE COMMODITY IN THE MARKET

FIRST ENERGY

VALUE OF THE COMMODITY IN THE MARKET

NUMBER OF BOUNDS

FOR THE MARKET

NUMBER OF SEGMENTS

FOR THE MARKET

SECOND COST CATEGORY

FOR THE COMMODITY IN THE MARKET

SECOND

ENERGY

VALUE OF

THE COMMODITY

IN THE MARKET

FIRST COST

CATEGORY

FOR THE COMMODITY IN THE MARKET

80
Exhibit 3.33 Case Study File: Market Data (1)

Part B: Interpretation

Markets are specified by commodity and location. Markets may be either

limited or unlimited, and fixed price or declining price. A declining price

market is represented by piecewise linear approximation with up to five linear

segments per market. An unlimited fixed price market may be recorded here

explicitly, or represented implicitly by a net revenue on the SIPROC cards

(see exhibit 3.32). Energy values of the commodity in the market are also

entered on this card. For the two energy categories, the value is assumed to

be constant with respect to the marketed quantity, in contrast to the revenue,

which may decline with respect to the marketed quantity in up to five

segments. No cards of this type should be entered if NMK was coded as 0 on the control card (see exhibit 3.15). A limited market has NBDMK = NSGMK. An

unlimited market has NBDMK = MSGMK-1. A fixed price market has NSGMK = 1.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 18 18

IMK 12 5 1 NMK

NAMMK 5A4 8

LMK 13 29 1 LSTLO

ICOMK 12 34 1 NCO

lECMK(l) 12 38 0 NEC

IECMK(2) 12 42 0 NEC

ICCMK(l) 12 46 1 NCC

ICCMK(2) 12 50 1 NCC

NSGMK 11 53 1 5

NBDMK 11 54 NSGMK

NSGMK

ECMK( 1

)

F10.5 55

ECMK(2) F10.5 65

81
Exhibit 3.34 Case Study File: Market Data (2)

Part A: Variable Definitions and Sequencing

FIRST BOUND

OF THE

MARKET

SECOND BOUND

OF THE

MARKET

IDENTIFICATION

NUMBER

OF THE

MARKET

82
Exhibit 3.34 Case Study File: Market Data (2)

Part B: Interpretation

This card allows the user to trace a declining price market structure setting

up to five cumulative bounds above which the revenue per unit is reduced.

This card must be entered even if the market is unlimited. In such a case,

the only entries are KEY and KEMP; they serve as placeholders. No cards of

this type should be entered if NMK was coded as 0 on the control card. Enter

a number of BDMKA(K) equal to NBDMK on the first market data card. BDMKA(K)

for values of K greater than NBDMK will be read but not used. Enter the

BDMKA(K) in cumulative units of the commodity.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 19 19

BDMKA( 1

)

F7.2 3

BDMKA(2) F7.2 10

BDMKA(3) F7.2 17 a BDMKA(4) FY. 2 24

BDMKA(5) F7.2 31

KEMP 12 75 1 NMK

a BDMKA(K) > BDMKA(K-l) + 0.0001 K>1.

83
Exhibit 3.35 Case Study File: Market Data (3)

Part A: Variable Definitions and Sequencing

FIRST REVENUE

COEFFICIENT FOR THE COMMODITY IN THE MARKET

WITHIN THE

FIRST BOUND

FIRST REVENUE

COEFFICIENT FOR THE COMMODITY IN THE MARKET

WITHIN THE SECOND BOUND

SECOND REVENUE

COEFFICIENT FOR THE COMMODITY IN THE MARKET

WITHIN THE

FIRST BOUND

SECOND REVENUE

COEFFICIENT FOR THE COMMODITY IN THE MARKET

WITHIN THE SECOND BOUND

IDENTIFICATION NUMBER

OF THE MARKET

84
Exhibit 3.35 Case Study File: Market Data (3)

Part B: Interpretation

The two sets of segments CFMKA and CFMKB allow for revenue sharing

arrangements. The user should enter NSGMK market revenue coefficients for

each set. For a single segment market, enter the pro rated price in CFMKA(l)

and CFMKB(l), respectively. The sum of the two values must equal the price

per commodity unit. For a multi-segment market, enter the marginal revenue

per segment (i.e., the slope of the total revenue function along that

segment) . Note that the price in the market is equal to the average revenue

(i.e, total revenue divided by the quantity sold). The successive units must

be a decreasing function of the interval number. The units of revenue are assumed to be thousands of dollars per commodity unit . No cards of this type

should be entered if NMK was coded as 0 on the control card.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 20 20

CFMKA(l) F7.3 3 0.0

CFMKA(2) F7.3 10 a CFMKA(3) F7.3 17 a CFMKA(4) F7.3 24 a CFMKA(5) F7.3 31 a CFMKB(l) F7.3 38 0.0

CFMKB(2) F7.3 45 b

CFMKB(3) F7.3 52 b

CFMKB(4) F7.3 59 b

CFMKB(5) F7.3 66 b

KEMP 12 75 1 NMK

a CFMKA(K) < CFMKA(K-l) K>1

.

b CFMKB(K) < CFMKB(K-l) K>1.

85
Exhibit 3.36 Case Study File: Cost Summation Data

Part A: Variable Definitions and Sequencing

68 21 1

IDENTIFICATION

NUMBER OF

COST SUMMATION CATEGORY

CONTRACTOR COST

NAME OF

COST SUMMATION

CATEGORY

NUMBER

OF COST

CATEGORIES

TO BE

SUMMED

SECOND

COST

CATEGORY

FIRST COST

CATEGORY

86
Exhibit 3.36 Case Study File: Cost Summation Data

Part B: Interpretation

This card permits the user to aggregate data from two or more cost categories

for purposes of reporting costs. There are NCS such cards. No cards of this

type should be entered if NCS was coded as 0 on the control card. Cost

summation categories may be used to make explicit what portion of the total

costs of the plan are being borne by the municipality, by a contractor

providing services to the municipality, or represent cash flows into or out of

the region. A cost category may be listed in more than one cost summation

category without causing double-counting in the system costs.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 21 21

ICS 12 5 1 NCS

NAMCS 5A4 8

NCCS 12 28 1 10

ICCS(l) 12 32 1 NCC

ICCS(2) 12 36 a NCC

ICCS(3) 12 40 a NCC

ICCS(4) 12 44 a NCC

ICCS(5) 12 48 a NCC

ICCS(6) 12 52 a NCC

ICCS(7) 12 56 a NCC

ICCS(8) 12 60 a NCC

ICCS(9) 12 64 a NCC

ICCS(IO) 12 68 a NCC

^For K>NCCS no value should be entered; otherwise the minimum value is 1

.

87
Exhibit 3.37 Case Study File: Transportation Data (1)

Part A: Variable Definition and Sequencing

SOURCE

TO

SITE

LINK

TYPE OF

TRANSPORTATION

LINKAGE

SOURCE

NUMBER

88
Exhibit 3.37 Case Study File: Transportation Data (1)

Part B: Interpretation

This, is the first of three types of transportation linkage cards. A value of

ITTYP equal to 1 defines this as a source-to-site linkage. For each source,

the user offers one linkage to each site which can serve as an offload. The

origin is specified by the source number coded into ITRCO; the destination is

specified by the site number coded into ITRSM. The locations associated with

the origin-destination pair are used to read the two distance files (see

section 3.3). There must be at least one ITTYP 1 card per source, and at

least one ITTYP 1 or 2 card offloading per site.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 22 22

ITTYP 11 4 1 1 ITRCO 12 7 1 NSU

ITROG^ 13 10

ITRSM 12 15 1 NSI

a Input value is irrelevant.

89
Exhibit 3.38 Case Study File: Transportation Data (2)

Part A: Variable Definition and Sequencing

SITE

TO

SITE

LINK

77 22 2 2 6 1

TYPE OF

TRANSPORTATION

LINKAGE

COMMODITY

NUMBER

DESTINATION

SITE NUMBER

ORIGIN

LOCATION

NUMBER

90
Exhibit 3.38 Case Study File: Transportation Data (2)

Part B: Interpretation

This is the second of the three types of transportation linkage cards. A

value of ITTYP equal to 2 difines this as a site-to-site linkage. The

commodity number is entered as ITRCO; the origin location number is entered as ITROG, and the destination site number is entered as ITRSM. There must be at

least one ITTYP 2 or 3 card entered per commodity/origin location combination.

There must be at least one ITTYP 1 or 2 card per site as destination.

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY 12 1 22 22

ITTYP 11 4 2 2

ITRCO 12 7 1 NCO

ITROG 13 10 1 LSTLO

ITRSM 12 15 1 NSI

91
Exhibit 3.39 Case Study File: Transportation Data (3)

Part A: Variable Definition and Sequencing

SITE

TO

MARKET

LINK

TYPE OF

TRANSPORTATION

LINKAGE

COMMODITY

NUMBER

82 22 3 6 5 1

DESTINATION

MARKET

NUMBER

ORIGIN

LOCATION

NUMBER

92
Exhibit 3.39 Case Study File: Transportation Data (3)

Part B: Interpretation

This is the third of the three types of transportation linkage cards. A value

of ITTYP equal to 3 defines this as a site-to-market linkage. There must be

at least one input link to each market as destination. There must be at least one ITTYP 2 or 3 card per commodity/origin location combination. Enter

commodity as ITRCO. Enter origin location number as ITROG; enter destination

market number as ITRSM. If NMK was coded as 0 on card 1 then no type 3 cards

should be entered.^ All other interpretations are the same as for site-to-site

shipments

Variable Variable First Minimum Maximum

Name Specification Column Value Value

KEY

ITTYP

ITRCO

ITROG

ITRSM

12

II12

13

12

14

7 10

15

22

3

1

1

1

22

3

NCO

LSTLO

NMK

^The end of the transportation file is marked by a single card with

KEY = 23, and no other entry. (See card number 86 in exhibit 3.14.)

93
3.3 PACKER AND VAN DISTANCE FILES

Each source of waste generation, transfer or processing site, disposal site,

and market is identified by a three-digit location code.^ Two point-to-point

distance files are input in either a triangular or square matrix format, one for packers and one for vans. The files establish a measure of distance

between each pair of three-digit location codes in miles, minutes, or cost.

These distance files are used by RRPLAN to calculate haul costs for various

kinds of transportation activities. The packer distance file is read on unit

7 whereas the van distance file is read on unit 8. Each file must be fully

sorted by major record and within a major record by minor record. In a triangular file, the major record is the higher numbered location and the

minor record is the lower numbered location. In a square file, the major

record is the destination location number and the minor record is the origin

location number. Setting the control variable ISQR to zero indicates that

triangular distance files are to be input whereas a value of one indicates

that square files are to be input.

Three different kinds of expansion are performed on the transportation file by

RRPLAN; they are: (1) location expansion, (2) source separation expansion;

and (3) haul cost expansion. The file itself, as entered on card type 22, is

skeletal. In the first expansion, RRPLAN extracts location numbers from the

source, site, and market cards and adds them to the transportation file for sources and for destination sites and markets. In the second expansion, the

category 1 transportation portion of the transportation file (source-to-site

shipments) is expanded to provide categories 4, 5, 6, 8, 9, and 11 through 15

in accordance with the source separation options entered as run controls

(KMIX, KSEPO, NTRIG) on card 3 and as source controls (IMIXSU, ISEPSU, ITRIGS) on card type 11.^ As mentioned earlier, the run controls dominate the source controls. In the third expansion, haul costs are calculated for each entry in

the expanded transportation file. This step uses the distance files for

packers and for vans, and the haul cost multipliers, CCOCC(l), CC0CC(2),

CCOEC(l), and CC0EC(2), entered on the commodity category cards.

If the distances in the files are recorded in miles, the haul cost multipliers

should be dollars per ton-mile appropriate to the commodity. If the distances

are in minutes, the haul cost multipliers should be dollars per ton-minute to

the commodity. If the distances are in costs, said costs should be in the

form of cost per ton over the distance between the two locations for the haul

No location numbers are required for markets associated with implicit revenues (as declared on a SIPROC card)

The activities corresponding to the expanded transportation file were described in section 2.2 and presented in summary form in table 2.2.

94
of MSW, by packer for the packer file and by van for the van file. In that

case, the haul cost (CCOCC(l) + CC0CC(2)) should be 1.0 for commodity number 1 (haul of MSW in a packer) and for commodity number 2 (haul of MSW in a van)

.

Other haul costs should be the ratio of the haul cost for that commodity to

the haul cost of MSW. The denominator is haul cost of MSW in a van for all

other commodities except number 4 which is source separation residue hauled

in a packer. Energy requirements for haul are respectively per mile, per

minute, and per dollar of MSW haul cost.

Turnaround times pose a number of difficulties which require some attention

when constructing the case study and distance files so as to avoid double

counting. Standard turnaround times are entered in CSPA (card 3), CSPB (card

3), and in CSLO(l) and CSL0(2) (card type 16). Non-standard turnaround times are entered in CSLO(l) and CSL0(2) for processes other than truck transfer

stations and in the packer distance file. Both issues will be discussed in

this section, although particular emphasis will be placed how each issue is

related to the two distance files.

The standard turnaround time for unloading a packer should be included in the

total cost of collection, CSPA, in the form of a cost per ton. The correct

cost is the cost per minute for the packer, with its crew, multiplied by the

number of minutes of standard time for unloading, divided by the average load

of the packer in tons. The standard time, for both packer and van, is the

time for unloading at a transfer station. Since the unit of measure of

source-separation alternatives is (thousand) tons of preseparated MSW, there

is a saving in turnaround cost in that the packer, now actually carrying

source-separation residue, carries a larger payload if measured in equivalent

preseparated MSW. The saving is (1 - PCRES) multiplied by the standard

turnaround cost per ton, where PCRES is the percent of source separation

residue by weight (card 3). This saving is subtracted from the incremental

cost of source separation to obtain CSPB for entry on card 3.

The cost per ton of standard turnaround time for loading a van at the transfer

station and unloading it at a selected offload (selected by the user to be the

standard) should be added to the slope costs, CSLO(l) and CLS0(2), for each

linear segment for each truck transfer process. The cost may be added to

either CSLO(l) or CSL0(2), or split between the two. To add the cost to both

would double-count it. Note again that the cost should be entered for each

linear segment for each truck transfer process.

Non-standard turnaround costs should be added to CSLO(l) or CSL0(2) for each

segment of the associated process (i.e., any process except a truck transfer

station) . The costs to be added include the total turnaround cost for loading

all outputs in dollars per input of the process plus either of the following:

95
(i) if MSW-receiving, the actual turnaround cost per ton of MSW for

unloading a van minus the standard turnaround cost for unloading a van; or

(ii) if not MSW-receiving, the actual turnaround cost per unit of the

input commodity for unloading a van (in thousands of dollars per

input unit)

The van distance files have been kept clean of turnaround cost since these

files are the basis for costing the haul of many different commodities. In

order to keep the van files clean, an adjustment was made in the slopes of

non-transfer processes to cover non-standard turnaround costs for vans. The

form of the turnaround cost added to the slopes controls the form of the

turnaround cost for packers, which is entered into the packer distance file.

The following turnaround cost should be added to entries in the packer

distance file in the case where the standard turnaround cost does not apply to

either packer or van or both:

The incremental packer offload turnaround cost (actual minus standard)

minus the incremental van offload cost (actual minus standard) , all

divided by a unit translation factor.^

If the file is in a triangular format and the packer distance file adjustment

is to be made for an offload, it should be entered for the corresponding

location number as either origin or destination in the file because each

location pair appears once in the file.

If the user wishes to differentiate a non-standard turnaround time adjustment

for two or more site-process combinations, or a source and a site, at the same location, two or more location numbers may be entered into the packer distance

file to represent the same real location. The sites and sources may then be

keyed to the two location numbers. The non-standard turnaround cost is

probably most significant for both packers and vans for offload at a landfill.

The unit translation factor is 1. if the units of the distance file are costs

per ton (i.e., the turnaround cost is already commensurate with the costs in

the distance file) ; the unit translation factor is cost per minute or cost

per mile if the units of the distance file are minutes or miles, in order to

convert the turnaround cost into equivalent minutes or miles. Note that the

packer distance file will not need to be modified at all if both packers and vans use standard turnaround times since in that case the cost goes to zero

in the above formula.

96
The triangular format distance files are arranged so that the entry for each

pair of points appears only once. This is accomplished by arbitrarily

defining the lower location number as the origin and the higher destination

number as the destination. Exhibits 3.40 and 3.41 are examples of triangular

distance files for packers and vans, respectively. Note that all entries are in dollars per ton. The distance tables shown in the exhibits are examples of

complete linkage between each destination and all origins with lower location

numbers. Although complete linkages will not cause any error if the said

linkage was not declared on one of the type 22 transportation data cards, for

regions with a large number of locations the files may become unwieldly. The

general organization of the files is described in table 3.1.

Table 3.2 Structure of the Packer and Van Distance Files

Card Variable First

Type Specification Column Purpose

First 20A4 1 Title^

Second^

Third^

Destination (1)^ 11 1 Flagd

Destination (1) 13 3 Destination Location No,

Destination (2)^ 13 2 First origin

Destination (2) F6.2 6 First distance

Destination (2) 13 14 Second origin

Destination (2) F6.2 18 Second distance

Destination (2) 13 26 Third origin

Destination (2) F6.2 30 Third distance

Destination (2) 13 38 Fourth origin

Destination (2) F6.2 42 Fourth distance

Destination (2) 13 50 Fifth origin

Destination (2) F6.2 54 Fifth distance

Destination (3) 11 1 Flag^

^Enter up to 80 alphanumeric characters to identify the distance file;

identification should include whether packer or van, and the name of the

region.

^No entries are to be made on these cards.

^Enter a block of cards for each destination, with the destination, blocks

ordered by destination location numer. Each destination block has one first

card (Destination( 1 ) )

, one or more middle cards (Destination(2) ), and one last card (Destination (3)). The Destination (2) cards should be used to

enter origin/distance pairs, five pairs per card, in order of origin

location number, leaving no gaps. The last pair in each destination block

should be the diagonal element, origin equals destination, with a value of

0.

^The first entry must be coded as 1.

^The entry must be coded as 2.

97
Exhibit 3.40 Typical Distance File for Packers

1 DISTANCE FILE FOR PACKERS IN DOLLARS PER TON

2

34 1 1 5 1 0.

6 2

7 1 2 8 1 2

2 0. g 2 10 1 3

11 1 .5 2 1 .4 3 0. 12 2 13 1 4 14 1 1 .5 2 3. 3 3.5 4 0. 15 2 16 1 5 17 1 1 . 2 2.5 3 2.0 4 1 . 18 2 19 1 6

20 1 30. 2 32. 3 31 . 4 29.

21 6 0.

22 2 23 1 7 24 1 15. 2 16. 3 14. 4 18.

25 6 33. 7 0. 26 2 27 1 8

28 1 50. 2 51 . 3 49. 4 50.

29 6 38. 7 60. 8 0. 30 2 31 1 9

32 1 30. 2 32. 3 .31 . 4 29.

33 6 0. 7 33. 8 38. 9 0. 34 2

5 0,

5 30.

5 16.

5 50.

5 30.

98
Exhibit 3.41 Typical Distance File for Vans

1 DISTANCE FILE FOR VANS IN DOLLARS PER TON

23

4 1 1 5 1 0.

6 2

7 1 2 8 1.6 2 0. 9 2 10 1 3

11 1.5 2 .45 30.

12 2 13 14

14 1.5 2 1. 3 1.1 4 0. 15 2 16 15

17 1.3 2 .8 3 .6 4 .3 5 0. 18 2 19 16

20 110. 2 11. 3 10.5 4 9.5 5 10.

21 6 0.

22 2 23 1 7 24 15. 25. 4 35. 46. 55.

5

25 6 11. 7 0. 26 2 27 1 8

28 1 16. 2 16.2 3 15.8 4 16. 5 16. 29 6 13. 7 20. 8 0. 30 2 31 1 9

32 110. 2 11. 3 10. 4 9.8 5 10. 33 60. 711. 813. 90.

34 2

99
Declaring transportation linkages requires careful handling by the user, A

fully linked region would allow the model great freedom in finding the best

solution, but it might be costly in problem size (due to greater computational

effort). On the other hand, if there is too little linkage, the user may

inadvertently predetermine the solution. At the extreme of minimum linkage,

the solution coming out of the run will be in essence what the user entered.

Minimum linkage, togeth.-^r with the use of one SIPROC per site, is the

technique needed to enter a specified solution for evaluation (LOPT = 4),

Similarly, minimum linkage for one or more sources is the technique needed for

"locking in". The user should however avoid predetermining the solution

through too-little linkage in an optimizing run.

100
4. THE RRPLAN OUTPUT AND ITS INTERPRETATION

The model's output reports can be classified into three basic categories.

These outputs present a detailed summary of all inputs from the three data

files, a complete characterization of each component of the preferred regional

plan, and an in-depth analysis of all commodity flows and their associated

cost implications. All output reports described in this chapter will be based on the example developed in chapter 3. Although this example is simple with

regard to the size of the region, almost all options available to the user have been incorporated into it. The associated output is therefore

sufficiently comprehensive to serve as a guide to interpreting the model's

output in a general situation.

The description given in the chapter will go through each of the output

reports on an item-by-item basis. Emphasis will be placed on relating each

output to the relevant portions of the three data files. It is important for users to recognize that some interpretation and qualification is necessary

when analyzing the preferred regional plan. The description presented in this

chapter will attempt to address these issues for the example at hand. Readers

should treat this as illustrative but not limit the interpretation of their own studies to the topics discussed in this chapter. In order to provide

guidance for analyzing the solutions produced by the model in a general

setting, the next chapter will establish criteria based on experience in model

building and consultation among user groups for setting up and critiquing the

model's results.

It is also important to point out that if the user were to run the case study

described in the previous chapter, the paging would differ from that presented

in this chapter. This is due to the fact that several output reports were sufficiently short that they could be presented on the same page. Therefore,

in order to conserve on space, two or more output reports from the case example may have been combined into a single exhibit. It is important to note

that the order in which all reports are presented in the exhibits is exactly

the same as they are output. Since it is the logical sequence of the output

and not the paging which is important for interpretation, it is anticipated

that users who first test their knowledge of the model by running the case example will have no difficulty in making the crosswalk between the example

presented in this chapter and their own case study.

4.1 INPUT SUMMARY

The seven exhibits presented in this section serve to characterize the problem

as it was sent to the optimizer. In some cases, they reflect modification to

the input data made by the model in order to maintain a satisfactory

accounting system^ as well as modifications to the transportation activities

associated with the source separation options.

Recall that no energy categories are required in order to use the model. The

internal accounting system will however, create an artificial category if none is entered in order to handle certain variables in a consistent manner

(e.g., the BTU content of MSW, paper and source separation residue).

101
Exhibit 4.1 summarizes the Inputs read on KEYs 1 through 7. These data

correspond to Inputs coded onto cards 1 through 8 of the ACASE file (see

exhibit 3.14). The first part of the output consists of the descriptive

title, TEST CASE, based on the Input read from card 2 of the ACASE file. The

controls for the optimizer (LPHASE, NFORC, KOPT) are then output followed by

the key economic variables. In this example, the analysis extends from 1983

until 2002 and uses an Inflation rate of 6 percent and a discount rate of 10

percent. The objective of the analysis Is to minimize the discounted cost of

the regional plan over the 20 year study period.

The next block of information beginning with NUMBER OF COST CATEGORIES and

ending with NUMBER OF COST SUMMATION CATEGORIES, establishes the counts for

those Inputs read on KEYs 8 through 21. The numbers are used internally as endpolnts for do loops where each set of data Is read in. The large block of

Information appearing beneath the title SOURCE DATA corresponds to the inputs

read from cards 3 through 8 in the ACASE file. Outputs based on card 3 (read

KEY 3) data present Information on the composition of the waste stream by

weight, the BTU values of source separation products, market Information on cans and glass, collection costs, run controls governing source separation

options, and cost allocation formulas for source separation residue and paper

(both as processed waste and for potential capacity requirements when diverted

into the waste stream due to the trigger price system) . Information from card

4 (read KEY 4) on net energy (both in collection and source separation) and

energy-to-cost ratios for paper, glass and cans is then output. Data on energy factors (read KEY 5) follow; they show the BTU content of MSW, an energy category for BTU differentiation, the energy value in the market for

paper, glass and cans and the forcing override controls specified by the user.

The remainder of the information on the output report deals with the paper

price distribution. Information which specifies the number, size of the

Intervals in the paper price distribution as well as the minimum price based on data read on KEY 7 is first output. Data from cards 6, 7 and 8 in the

ACASE file are then used to compute the average price of paper and, for each

trigger price, how much paper is burned and what the average price of paper

marketed is expected to be. Notice that as the trigger price is raised, more paper is burned and that the average price of the paper marketed rises. This

result is directly related to the shape of the paper price distribution.

Since increasing the trigger price forces more paper to be diverted back into

the waste stream for burning^ , the paper which is left must therefore be

marketed at a price which is, on the average, higher than those associated

with lower trigger prices.

This is true by definition of the trigger price as that price below which the

paper will be burned for its BTU value and above which it will be sold for

its fiber content.

102
Exhibit 4.1 Input Summary of Program Controls and General Information

R R P L A N

A MODEL FOR REGIONAL RESOURCE RECOVERY PLANNING

WITH LIMITED AND DECLINING PRICE MARKETS, WITH USER-DEFINED COST CATEGORIES AND ENERGY CATEGORIES

TEST CASE

LAST PHASE = 3 NUMBER OF FORCES = 1 OUTPUT OPTION (KOPT) = 3 NUMBER OF YEARS IN RUN = 20 FIRST YEAR = 1933 DISCOUNT RATE « 10.00 PERCENT PER YEAR. GENERAL INFLATION RATE = 6.00 PERCENT PER YEAR.

OPTIMIZING MODE: MINIMIZE DISCOUNTED COST NUMBER OF COST CATEGORIES A NUMBER OF ENERGY CATEGORIES: 2 NUMBER OF COMMODITIES: 8 NUMBER OF SOURCES: 4 NUMBER OF SITES: 4 NUMBER OF LANDFILLS: 2 NUMBER OF PROCESSES: 5 NUMBER OF S I TE- PROCESSES : 5 NUMBER OF MARKETS: 3 NUMBER OF COST SUMMARIZATION CATEGORIES: 1

6.000

15.00

SOURCE DATA

PCT PAPER BY WEIGHT: 23.20 PCT GLASS BY WEIGHT:

PCT CANS BY WEIGHT: 8.00 PCT RESIDUE BY WEIGHT: MILLION BTUS PER TON OF RESIDUE: 5.000

PRICE OF GLASS AT MARKET, S/TON: 25.49

COST CATEGORIES - PAPER: 1 GLASS: 1 TOTAL COST OF COLLECTION - CATEGORY: 1 INCREMENTAL COST OF SOURCE SEPARATION - CATEGORY: 1 COST IN $/PRESEPARATED TON: 2.000

KMIX = 1 KSEPO " 1 INPAP = 1 NUMBER OF TRIGGERS = 3 RPRPAP = .800 ALLO = .600 RPRRES = .900 TOTAL NET ENERGY OF COLLECTION - CATEGORY: 1 REO: .800 INCREMENTAL NET ENERGY OF SEPARATE COLLECTION PER PRESEPARATED TON - CATEGORY: 1 REO: ENERGY TO COST RATIOS FOR HAUL TO MARKET - PAPER: .25000 GLASS: .20000 CANS:

1 1 . 80 57.00 MILLION BTUS PER TON OF PAPER: PRICE OF CANS AT MARKET , S/TON

:

CANS: 1 COST IN S/TON MSW: 20.000

. 1 00

. 1 5000 MBTUS FOR MSW: 5.20000

ENERGY VALUES IN THE MARKET

PAPER: 20.00000 cN CAT: FORCING CONTROLS - IFDRV: 1 NUMBER OF INTERVALS: 5

EN CAT FOR BTU DIFFERENCE:

1 GLASS: 2.00000 EN CAT: 1 CANS:

FDRIV: 10.00 IVDRV: 1 VDR I V : - 999 . 00

SIZE OF INTERVAL: 5.000 BOTTOM OF FIRST INTERVAL

2.00000 EN CAT: 12.00 AVERAGE PRICE OF PAPER: 24.50

TRIGGER NO. 1 IS: 15.00 PERCENT OF BURN IS: AVERAGE PRICE OF PAPER SOLD IS: 25.29

6.7

TRIGGER NO. 2 IS: 20.00 PERCENT OF BURN IS: AVERAGE PRICE OF PAPER SOLD IS: 27.03

24.4

TRIGGER NO. 3 IS: 25.00 PERCENT OF BURN IS: 53.3 AVERAGE PRICE OF PAPER SOLD IS: 29.69

103
Exhibit 4.2 summarizes the inputs read on KEYs 8, 9 and 10. These data

correspond to inputs coded onto cards 9 through 22 in the ACASE file. Note

that all entries on the exhibit are individually ntimbered. Whenever the

appropriate category is to be referenced within the program, it is that number

which is used.

The first four lines of output correspond to the four cost categories; these

input data were read from lines 9 through 12 of the ACASE file. Each cost

category is printed out on a single line in sequence along with its

descriptive name. The IDCC column indicates if the respective category is an operating (0) or a capital (1) cost. Since the objective of this run is to

minimize discounted costs, all weights are shown equal to 1.^ The next column

gives the differential rate of inflation. The values may be positive,

negative or zero; they are used for both operating and capital costs. The

remaining four columns of the cost category output are used only for capital

costs. The zero values recorded under municipal and contractor operating

costs are thus merely placeholders. Data on useful life, the years to

capitalize the loan, the interest rate of the loan and the life remaining for

existing capital occupy the last four columns. The NREM, years remaining for

existing capital, is used to schedule the replacement of all capital items in use at the beginning of the study period.

Data on the two energy categories are then output, followed by the data on the

eight commodity categories (line numbers 15 through 22 of the ACASE file) . As

mentioned earlier, the first five commodities are fixed. Each commodity

category has two haul cost and two energy multipliers. The multipliers are applied against the packer (APKR) and van (AVAN) distance files to get the

cost of handling the commodity and its associated net energy demand. The

integers listed beneath the four columns labeled CAT refer to the two cost and

two energy categories. For example, the haul cost of MSW in a packer is

allocated against municipal operating cost, whereas the cost of transporting

incinerated ferrous is allocated against contractor operating cost.

Exhibit 4.3 summarizes the inputs read on KEYs 11, 12 and 13. These data

correspond to inputs coded onto cards 23 through 32 in the ACASE file.

Information on the four sources (read KEY 11) is printed out in two parts.

The first part gives the source number and name followed by a location number.

As mentioned earlier, once all worksheets have been filled out, it will be

necessary to assign a location number to each source, site, landfill and

market. It is this number which appears under the column labeled LOC; it is

also used as a reference point in computing distances for hauling wastes to a site for processing. The rate of waste generation in thousands of tons per

year (KTPY) appears in the next column. The last seven columns of the output

Recall that cost weights are only required for optimizing modes 3 and 4. In

all other cases, the weights needed to calculate the value of the objective

function are automatically set within the MAIN program.

104
Exhibit 4.2 Input Summary for Cost, Energy and Commodity Category Data

COST CATEGORIES

NO. NAME lOCC WEIGHT DIFLAT LIFU LIFC INT RATE NREM

1 MUNICIPAL COST 0 1 ,000 2.000 0 0 .000 0

2 CONTRACTOR OP COST 0 1 .000 •3.000 0 0 .000 0

3 CONTRACTOR CAP COST 1 1 .000 1 .000 20 15 9.000 0

4 SITE PREP COST 1 1 .000 .000 50 15 9.000 0 ENERGY CATEGORIES

NO. NAME WEIGHT

1 FUEL INPUT, MBTU -1 .000

2 STEAM OUTPUT, IWSTU -1.000

COMMODITY CATEGORIES

WITH HAUL COST MULTIPLIERS AGAINST DISTANCE TABLES

JO NAME 1ST COST CAT 2ND COST CAT 1ST ENERGY CAT 2ND ENERGY

1 MSW IN PKR, KTON 1 .000 .000 .200 1 ,000

2 MSW IN VAN, KTON 1 .000 .000 .250 1 .000

3 S-S PAPER, KTON .700 .000 .150 1 .000

4 S-S RES IN PKR, KTON 1 .000 .000 .200 1 .000

5 S-S RES IN VAN. KTON 1 . 000 .000 ,200 1 ,000

6 STEAM, MLBS 1 .000 2 ,000 2 1 ,000 2 ,000

7 PROC RESIDUE, KTON .800 2 .000 2 .200 1 ,000

8 INCIN FERROUS, KTON .700 2 .000 2 .100 1 .000

CAT

105
Exhibit 4.3 Input Summary for Source, Site and Landfill Data

SOURCES

NO. NAME

LYNN MARBLEHEAD

SALEM

REVERE

LOC

1234

TONS. KTPY 340.000

7.000

120.000

40.000

MIX

0

10

0

SEP

1

1

1

1

TRIG 1

10

10

3 4

1 0

0 0

1 0

0 0

SOURCES. CONTINUED COST AND ENERGY FACTORS FOR HAUL OF SOURCE-SEPARATED RECOVERABLES TO MARKET

NO. COST CAT ENERGY CAT COST - PAPER GLASS CANS

1 1 1 2.00 1 .50 1 .20

2 1 1 2.20 1 .70 1 .40

3 1 1 2.10 1 .60 1 .30 4 1 1 1 .90 1 .40 1.10

SITES

NO. NAME LOC INCO

1 SAUGUS PROC 5 2 2 WORCESTER PROC 6 2 3 AMESBURY LANDFILL 7 7 4 SAUGUS SECONDARY PR 5 7

ICAP I LAND NO PROC SITE PREP COST CAT SITE PREP EN

1 0 1 1000.000 4 250.00000

0 0 2 1000.000 4 250.00000

0 1 1 200.000 4 50.00000

0 1 1 400.000 4 100.00000

CAT

1

1

1

1

LANDFILLS

NO. NAME LOC

1 AMESBURY LANDFILL 7 2 SAUGUS LANDFILL 5

LAND AVAILABLE 99600.000

84000.000

106
show the source controls for the separation options. Recall that the source controls are dominated by the values of the run controls (KMIX, KSEPO, NTRIG)

.

In this case, Marblehead is the only source which will consider shipping mixed

MSW, All sources are listed as considering unconditional source separation,

and Lynn and Salem are listed as considering conditional source separation

with three possible trigger prices. Each source also had a series of factors

input for calculating the costs of hauling source separated recoverables to

the market. These figures, along with their respective cost and energy

allocations, are presented under the title of SOURCES, CONTINUED.

The next piece of information output is concerned with the data on the four

sites. These input data were read from line numbers 27 through 30 of the

ACASE file. Note that each site is numbered sequentially. Examining the

location numbers, however, reveals that two sites are co-located (the Saugus

processor and the Saugus secondary processor). The first two sites receive

MSW whereas the last two receive process residue (see the values recorded

under the INCO column) . Two sites are required at Saugus because each site is

coded as receiving a single commodity.^ The ICAP and ILAND columns show which

sites are capacitated (ICAP = 1) and which have landfill available (ILAND =

1). The number of processes offered at the site is recorded under the column

heading NO PROC. The last four columns show site preparation costs in

thousands of dollars, its respective cost category number, the net energy

required in site preparation, and its respective energy category number. The

last bit of information is concerned with data on landfills; these data were recorded on cards 31 and 32. The two landfills are listed in sequence and

named. The location number of the landfill and the land available in acre

feet occupy the last two columns.

The output report shown as exhibit 4.4 summarizes the inputs read on KEYs 14,

15, 16 and 17. These data correspond to inputs coded onto cards 33 through 58

in the ACASE file. Information on each process is output in sequence followed

by the site-process combination data.

The first line of output for each process shows the process number and name,

the incoming commodity number, an indication if the process is new or existing, the number of outputs, the number of linear segments, whether the

process is land using or not, the acre feet of land required per unit (e.g.,

kilo ton) of input and the respective cost and energy categories. For those

processes which produce outputs, the next line of output lists the

commodity number and how many commodity units are produced per unit of input

to the process. For example, the new water wall incinerator described under

Recall that MSW receiving sites and only such sites can receive more than one commodity; they can receive any of the first five commodities.

107
Exhibit 4.4 Input Summary for Process and Site-Process Combination Data

PROCESS INPUTS

NO. NAME INCOP I EX IS NCOF ^SEG I LACOF USLA COST CAT

1 ST own FN TAT* 1 ST

1 TRANS STA 2 0 1 3 Q 2 3 OUTPUT COEFFICIENTS: COMMODITY COEFFICIENT

2 1 .00000 SEG 1 INTERCEPTS 1ST COST 20.00 2ND COST 1 O 1 PKJ o M ri CM . uu SEG 1 SLOPES 1ST COST 1 .800000 2ND COST 1 800000 1ST EN 500000 2ND EN .000000 SEG 2 INTERCEPTS 1ST COST 40.00 2ND COST 35.00 1ST EN .70 2ND EN .00 SEG 2 SLOPES 1ST COST .500000 2ND COST O 1 \J\J\J\J 1 d 1 EN ^ IN u CM . uuuuuu

SEG 3 INTERCEPTS 1ST COST 60.00 2ND COST 55 00 1 O 1 EN . ou o w r> PM SEG 3 SLOPES 1ST COST .260000 2ND COST 1 d 1 own . UUUUUU

NO. NAME INCOP lEXIS NCOF T 1 Amp USLA CAT : 1 ST own P Kl /* A T . Znu LN r

1 ST

2 WATER-WALL INCIN 2 1 3 Q nnnnn 2 3 OUTPUT COEFFICIENTS: COMf^lODITY COEFFICIENT

6 5.48000

7 . 15000

8 .07000 SEG 1 INTERCEPTS 1ST COST 300. 00 2ND COST .00 1ST EN 70.00 2ND EN .00 SEG 1 SLOPES 1ST COST 7.300000 2ND COST 000000 1ST EN 2 000000 2ND EN .000000

NO. NAME INCOP lEXIS NCOF NSEG I LACOF USLA COST CAT: 1ST 2ND EN CAT: 1ST

3 WATER-WALL INCIN 2 0 3 2 0 00000 2 3 1 OUTPUT COEFFICIENTS: COMMODITY COEFFICIENT

6 5.43000

7 . 15000

8 . 07000

SEG 1 INTERCEPTS 1ST COST 300.00 2ND COST 224. 16 1ST EN 80.00 2ND EN .00 SEG 1 SLOPES 1ST COST 7.300000 2ND COST 7 000000 1ST EN 2 000000 2ND EN .000000 SEG 2 INTERCEPTS 1ST COST 1000.00 2ND COST 096.64 1ST EN 170.00 2ND EN .00 SEG 2 SLOPES 1ST COST 5.000000 2ND COST 4 236000 1ST EN 1 500000 2ND EN .000000

NO. NAME INCOP lEXIS NCOF NSEG I LACOF USLA COST CAT ; 1ST 2ND EN CAT: 1ST

4 LANDFILL 7 0 0 3 1 1 65300 2 3 1 SEG 1 INTERCEPTS 1ST COST 40.00 2ND COST 40.00 1ST EN 1 . 60 2ND EN .00 SEG 1 SLOPES 1ST COST 3.600000 2ND COST 3 600000 1ST EN 1 000000 2ND EN .000000 SEG 2 INTERCEPTS 1ST COST 80.00 2N0 COST 70.00 1ST EN 1 .40 2ND EN .00 SEG 2 SLOPES 1ST COST 1 .000000 2ND COST 1 220000 1ST EN .800000 2ND EN .000000

SEG 3 INTERCEPTS 1ST COST 120.00 2ND COST 1 10.00 1ST EN 1 .20 2ND EN .00 SEG 3 SLOPES 1ST COST .520000 2ND COST 480000 1ST EN 600000 2ND EN .000000

NO. NAME INCOP lEXIS NCOF NSEG I LACOF USLA COST CAT: 1ST 2ND EN CAT: 1ST

5 SECONDARY PROC 7 0 0 2 1 82000 2 3 1 SEG 1 INTERCEPTS 1ST COST 40.00 2ND COST 40.00 1ST EN 1 .60 2N0 EN .00 SEG 1 SLOPES 1ST COST 2.000000 2ND COST 2 000000 1ST EN 1 000000 2ND EN .000000 SEG 2 INTERCEPTS 1ST COST 80.00 2ND COST 70.00 1ST EN 1 .40 2N0 EN .00 SEG 2 SLOPES 1ST COST 1 .000000 2ND COST 1 000000 1ST EN .800000 2ND EN .000000

2ND

1

2N0

1

2ND

1

SIPROCS

SITE PROCESS CAPACITY IN KTPY NET REVENUE SEGMENTS: 12 3 EN VAL OF REVENUE REVENUE COST CAT EN CAT

1 2 360.000 1.000 1 0 0 .20000 2

2 1 .000 .000 1 1 1 .00000 2

2 3 .000 1.000 1 1 0 .30000 2

3 4 .000 .000 0 1 1 .00000 2 4 5 .000 3.000 1 0 0 1.00000 2

108
process number 3 produces 5.48 million pounds of steam, 0.15 kilotons of

process residue, and 0.07 kilotons of incinerated ferrous per kiloton of MSW

input

.

The next block of data shows , for each linear segment , the values of the

intercepts and slopes for each cost and energy category. For example, process

3 experiences economies of scale in both operation and capital costs, as shown

by the increasing values of the intercept terms and decreasing values of the

slopes. Recall that the intercepts correspond to the fixed charges whereas

the slopes correspond to variable costs. Thus, as plant size is increased,

the cost per ton of MSW declines (i.e., it is less expensive on a per ton

basis to build and process a 1000 ton per day plant than to have two 500 ton

per day plants). In this case, the costs opposite the label 1ST COST

correspond to contractor operating costs and those opposite 2ND COST to

contractor capital costs. Only one energy category (FUEL INPUT) is used for

all five processes. The output presented in exhibit 4.4 corresponds to the

following line numbers in the ACASE file for each process: (1) 33 through 37;

(2) 38 through 40; (3) 41 through 44; (4) 45 through 49; and (5) 50 through

53.

The site-process (SIPROC) output follows closely the data on the upper portion

of the exhibit. The site number and process number are given first. For

example, a landfill operation (process number 4) is located at site number 3.

Only the first site, Saugus, is capacitated, having a rated capacity of 360

KTPY. The next column gives the implicit revenues associated with the siteprocess combination. Thus for every ton of MSW processed at the existing

water wall incinerator located at Saugus, $1 of revenue is produced. The next

three columns show which linear segments are to be offered. Notice that it is

not necessary to offer all segments. For example, if landfill operations are

to take place at site 3, they are expected to exceed the level defined by the

cutoff point between the first and second segment of the process. The last

three columns show the energy value of the net revenue and to which cost and

energy category the net revenue accrues.

Exhibit 4.5 summarizes the inputs read on KEYs 18, 19, 20 and 21. These data

correspond to inputs coded onto cards 59 through 68 of the ACASE file. Data

on each of the three markets are first output. The first line of each market

output shows the market number, its name and location, which commodity

category the market receives, and to which cost and energy categories revenues

are to the allocated. For example, the GE facility at Lynn is located next to

the Saugus processor from which it receives steam. The next two lines of

output deal with how revenues are to be shared among the two parties listed

under 1ST REV and 2ND REV on the first line of the market report. For

example, GE pays^ the contractor (cost category number 2) $3,000 per million

These figures are illustrative and not meant to be indicative of any

contractual arrangements which may be in effect at the site.

109
pounds for the first 900 million pounds of steam and $2,000 per million pounds

for the next 900 million pounds of steam; the municipality receives no revenues from the sale at either level of market activity. Note that the

market has two segments and two bounds (900 and 1800 million pounds of steam).

The output presented in exhibit 4.5 corresponds to the following line numbers

in the ACASE file for each market: (1) 59 through 61; (2) 62 through 64; and

(3) 65 through 67.

The last line of output is concerned with the cost summation categories. In

this case, the single cost summation category groups contractor operating and

contractor capital cost into a single category designated as contractor cost.

Exhibits 4.6 and 4.7 summarize the inputs read on KEY 22. These data

correspond to inputs coded onto cards 69 through 85 in the ACASE file. Each

of the 15 transportation categories are output in sequence.^ The first

transportation category was input on cards 69 through 76 and corresponds to

the source-to-site shipments of solid waste prior to expansion. The columns

show the category number, the source number, the source location number, the

site number, the site location number, and the distance for packers and vans,

respectively. No values are entered into the distance columns because the

information printed out is not used by the model in its current form but is

expanded. The degree of expansion is dependent on the values coded into the run controls and the source controls.

The next two blocks of output correspond to category 2 transportation, siteto-site, and category 3 transportation, site-to-market; these data were read

from cards 77 through 81 and 82 through 85, respectively. Each of these

outputs show the category number, the number and name of the commodity

shipped, the site from which the shipment originates, the destination site or market, its location, and the distance measure for vans. Note that packers

are not used for site-to-site and site-to-market shipments of wastes and

recoverables; the distance for packers are thus not shown.

The remaining outputs on exhibits 4.6 and 4.7 correspond to source-to-site

shipments after expansion. ^ Transportation category 4 and 5 represent

unconditional MSW and unconditional source separation, respectively. Recall

from the output on exhibit 4.3 that only Marblehead considered the shipment of

mixed MSW. All communities considered unconditional source separation

however, as reflected in the output for transportation category 5. Note that

packer costs are approximately three times those of vans on a per ton basis.

Transportation categories 7 and 10 are not used.

If a local paper market exists in the region, then category 9 transportation

is defined as shipments of paper from the source to the local paper market.

110
Exhibit 4.5 Input Summary for Market and Cost Summation Category Data

MARKETS

NO. NAME

1 GE LYNN STEAM

LOC COMMOO CATEGORIES- 1ST

5 6 REV 2ND

2 REV1 1ST EN 2ND

2 EN 1ST EN COEF

1 5.40000

2ND EN COEF

-

. 20000

1ST REVENUE-COEF BOUND 3.000 SOO.OO

COEF BOUND COEF 2.000 1800.00 .000 BOUND

.00 COEF.000 BOUND

.00 COEF

.000 BOUND.00 NO SEG NO

2 BOUNDS2 2ND REVENUE-COEF BOUND

.000 900.00

COEF BOUND

.000 1800.00 COEF

.000 BOUND

.00 COEF.000 BOUND

.00 COEF.000 BOUND.00 NO SEG NO2 BOUNDS2

NO. NAME

2 NEW YORK FERROUS

LOC COMMOD CATEGORIES- 1ST

8 8 REV 2ND

2 REV1 1ST EN 2ND1 EN 1ST EN COEF1 .20000 2ND EN COEF

- . 20000

1ST REVENUE-COEF BOUND

11.860 .00 COEF.000 BOUND

.00 COEF

.000 BOUND

.00 COEF

.000 BOUND.00 COEF.000 BOUND.00 NO SEG NO1 BOUNDS

0 2ND REVENUE-COEF

.000 BOUND.00 COEF BOUND

.000 .00 COEF

.000 BOUND

.00 COEF

.000 BOUND.00 COEF.000 BOUND.00 NO SEG NO1 BOUNDS

0

NO. NAME

3 WORCESTER STEAM

LOC COMMOD CATEGORIES- 1ST

9 6 REV 2ND

2 REV

1 1ST EN 2ND

2 EN 1ST EN COEF

1 5.40000

2ND EN COEF

- . 20000

1ST REVENUE-COEF BOUND 8.000 2000.00

COEF BOUND 6.000 4000.00COEF

.000 BOUND

.00 COEF.000 BOUND.00 COEF

. 000 BOUND.00 NO SEG NO BOUNDS

2 2N0 REVENUE-COEF BOUND

.000 2000.00

COEF BOUND

.000 4000.00

COEF

.000 BOUND

.00 COEF.000 BOUND

.00 COEF.000 BOUND.00 NO SEG NO

2 BOUNDS

2 COST SUMMATION CATEGORIES

NO. NAME

1 CONTRACTOR COST

NO COST CAT CATEGORIES

2 2 3 0

111
Exhibit 4.6 Transportation Activities Considered by the Model:

Site-to-Site, Site-to-Market , and Source-to-Site

(Excluding Conditional Source Separation)

TRANSPORTATION ACTIVITIES

TOTAL NUMBER OF TRANSPORTATION ACTIVITIES IS TRANSPORTATION ACTIVITY LISTING:

39 INCLUDING CATEGORIES 1 THROUGH 15

CATEGORY CATEGORY

1 TRANSPORTATION: SOURCE ORIGIN BEFORE EXPANSION IN THOUSAND TONS PER YEAR SOURCE

1

12

23

3

44

ORIGIN LOC

1

12

23

34

4

DEST SITE

12

12

12

12

DEST LOC

5

6

56

56

56

DIST PKR

.00 .00

.00

.00

.00

.00

.00 .00

DIST VAN

.00

.00

.00

.00

.00

.00

.00

.00

CATEGORY 2 TRANSPORTATION : SITE TO SITE IN THOUSAND COMMODITY UNITS PER YEAR CATEGORY COMMODITY ORIG LOC DEST SITE DEST LOC DIST PKR DIST VAN

2 2 6 1 5 .00 10.00

2 7 5 3 7 .00 5.50

2 7 5 4 5 .00 .00

2 7 6 3 7 .00 11 .00

2 7 6 4 5 .00 10. 00 CATEGORY 3 TRANSPORTATION: SITE TO MARKET IN THOUSAND COMMODITY UNITS PER YEAR CATEGORY COMMODITY ORIG LOC DEST MKT DEST LOC DIST PKR DIST VAN

3 6 5 1 5 .00 .00

3 8 5 2 8 .00 16 .00

3 6 6 3 9 .00 .00

3 B 6 2 8 .00 13 .00

CATEGORY 4 TRANSPORTATION: SOURCE ORIGIN AFTER EXPANSION; UNSEPARATED MSW (MIXED SOLID WASTE) IN THOUSAND TONS CATEGORY SOURCE ORIG LOC DEST SITE DEST LOC DIST PKR DIST VAN PER YEAR

4 2 2 1 5 2.50 .80 4 2 2 2 6 32.00 11.00

CATEGORY 5 TRANSPORTATION: SOURCE ORIGIN AFTER EXPANSION; UNCONDITIONAL SOURCE SEPARATION

WITH NATIONAL PAPER MARKET. IN THOUSAND PRESEPARATED TONS PER YEAR CATEGORY SOURCE ORIG LOC DEST SITE DEST LOC DIST PKR DIST VAN

5 1 1 1 5 1 .00 .30

5 1 1 2 6 30.00 10. 00

5 2 2 1 5 2.50 .80 5 2 2 2 6 32.00 1 1 .00 5 3 3 1 5 2.00 .60 5 3 3 2 6 31 .00 10.50

5 4 4 1 5 1 .00 .30 5 4 4 2 6 29.00 9.50

112
Exhibit 4.7 Transportation Activities Considered by the Model:

Conditional Source Separation Options

CATEGORIES 11 - 15 TRANSPORTATION;: SOURCE ORIGIN AFTER EXPANSION;

CONDITIONAL SOURCE SEPARATION FOR TRIGGER EQUAL TO CATEGORY NUMBER MINUS 10; IN THOUSAND PRESEPARATEO TONS PER YEAR

CATEGORY SOURCE ORIG LOC DEST SITE DEST LOC DIST PKR DIST VAN

1

1

1 1 1 5 1 .00 .30

11 1 1 2 6 30.00 10.00

11 3 3 1 5 2 .00 .60

11 3 3 2 6 31 .00 10. 50

CATEGORIES 11-15 TRANSPORTATION: S CONDITIONAL SOURCE SEPARATION FOR TR

IN THOUSAND PRESEPARATEO TONS PER YE CATEGORY SOURCE ORIG LOC

12 1 1 12 1 1 12 3 3 12 3 3

OURCE ORIGIN AFTER EXPANSION:

IGGER EQUAL TO CATEGORY NUMBER MINUS 10; ARDEST SITE DEST LOC DIST PKR DIST VAN

1 5 1 .00 .30

2 6 30.00 10.00

1 5 2.00 .60

2 6 31.00 10.50

CATEGORIES 11-15 TRANSPORTATION: SOURCE ORIGIN AFTER EXPANSION;

CONDITIONAL SOURCE SEPARATION FOR TRIGGER EQUAL TO CATEGQ-RY NUMBER MINUS 10; IN THOUSAND PRESEPARATEO TONS PER YEAR

CATEGORY SOURCE ORIG

13 1 13 1 13 3

13 3

LOC DEST SITE DEST LOC DIST PKR DIST VAN

1 1 5 1 .00 .30

1 2 6 30.00 10.00

3 1 5 2 .00 .60

3 2 6 31 .00 10. 50

113
This underscores the rationale behind the consideration of a dedicated

transfer station for each source. Transportation categories 11 through 13

outputs are shown in exhibit 4.7. There is one such conditional source separation category for each trigger price offered. Note that only Lynn and

Salem are considering conditional source sepration (see exhibit 4.3).

4.2 ANALYSIS OF THE REGIONAL PLAN

The outputs presented in the previous section serve to define the problem as it is sent to the optimizer. From this information a preferred regional plan

will be selected. The outputs discussed in this section answer the three

major questions associated with the preferred plan. First, how should wastes

and recoverables be routed among sources, sites and markets? Second, which

processes should be carried out , where should they be located , and how should

the facility be sized? Third, which commodities should be marketed, where

should they be marketed, and in what quantities?

The first of the six exhibits shown in this section consists of a capsule

summary of the preferred regional plan. The first block of entries serves merely to define the study; it is Identical to the top portion of exhibit 4.1.

The information on the solution is contained under the heading THE PHASE 3

SOLUTION. For this case, a value of $62.5 million was revealed as optimal.

The subsequent output reports will analyze the preferred plan in detail. The

last two rows of the output show the size of the A matrix as sent to the

optimizer. In this case, the A matrix contained 30 rows (constraints) and 54

colximns (activities). Of the 1620 entries in the A matrix only 153 were

non-zero.

The transportation activities in the solution are shown in exhibit 4.9. In

reviewing the output, it may be useful to first reexamine exhibits 4.6 and

4.7. In these exhibits data on 31 transportation activities were presented

(the 8 activities associated with transportation category 1 have been excluded

since they are not a part of the optimization problem). Exhibit 4.9 contains

only seven transportation activities however, which shows how the preferred

routing dominates the complete linkage offered. The source-to-site shipments

are summarized under the expanded transportation categories (5 through 11).

They will be discussed after the activities associated with transportation

categories 2 and 3 have been summarized.

Site-to-site shipments are summarized under category 2 transportation

activities. For the case at hand, all wastes originate from the Saugus

processor and are sent to the Saugus secondary processor. The only commodity

shipped is process residue, which produces a net revenue of $3 per ton (see

the SIPROC entry on exhibit 4.4). The model shows an activity of

approximately 45 KTPY. The residue from the secondary processor is then

landfllled (see the values coded beneath ILACOF and USLA for process 5 in

exhibit 4.4).

114
Exhibit 4.8 Abbreviated Form of the Solution

SUMMARY OF RRPLAN OPTIMIZATION

MATRIX SIZE

NUMBER OF ROWS = 30 NUMBER OF COLUMNS = 84 NUMBER OF NON-ZERO ELEMENTS OF THE (A) MATRIX = 153 THE ACTUAL SIZE OF THE (A) MATRIX IS 306 ELEMENTS

RRPLAN

A MODEL FOR REGIONAL RESOURCE RECOVERY PLANNING

WITH LIMITED AND DECLINING PRICE MARKETS. WITH USER-DEFINED COST CATEGORIES AND ENERGY CATEGORIES

TEST CASE

LAST PHASE 3 NUMBER OF FORCES = 1 OUTPUT OPTION (KOPT) = 3

• NUMBER OF YEARS IN RUN = 20 FIRST YEAR = 1983 DISCOUNT RATE ' 10.00 PERCENT PER YEAR. GENERAL INFLATION RATE 6.00 PERCENT PER YEAR.

OPTIMIZING MODE; MINIMIZE DISCOUNTED COST

THE PHASE 3 SOLUTION

SOLUTION VALUE IS: 62545.766602

NUMBER OF ROWS IS: 30 NUMBER OF COLUMNS IS: 54 NUMBER OF NON-ZERO COEFFICIENTS IS: 153

lis
Exhibit 4.9 Transportation Activities in the Solution

TRANSPORTATION ACTIVITIES IN THE SOLUTION

CATEGORY 2 TRANSPORTATION ACTIVITIES: SITE TO SITE ACTIVITY LEVELS IN COMMODITY UNITS PER YEAR

CAT ORIGIN LOCATION DESTINATION SITE LOC COMMODITY ACTIVITY LEVEL

2 5 4 SAUGUS SECONDARY PR 5 7 PROC RESIDUE. KTON 44.6486 CATEGORY 3 TRANSPORTATION ACTIVITIES: SITE TO MARKET

ACTIVITY LEVELS IN COMMODITY UNITS PER YEAR

CAT ORIGIN LOCATION DESTINATION MARKET

3 5 1 GE LYNN STEAM

3 5 2 NEW YORK FERROUS

LOC COMMODITY

5 6 STEAM. MLBS

8 3 INCIN FERROUS. KTON

ACTIVITY LEVEL

1631 .1592 20.8361 CATEGORY 5 TRANSPORTATION ACTIVITIES: SOURCE TO SITE UNCONDITIONAL SOURCE SEPARATION WITH NATIONAL PAPER MARKET

ACTIVITY LEVELS IN THOUSAND TONS OF PRESEPARATEO MSW PER YEAR

CAT ORIGIN SOURCE LOC DESTINATION SITE LOC

5 2 MARBLEHEAD 2 1 SAUGUS PROC 5 5 4 REVERE 4 1 SAUGUS PROC 5

COMMODITY

4 S-S RES IN PKR. KTON

4 S-S RES IN PKR. KTON

ACTIVITY LEVEL 7.0000

40.0000

TRAN STA

CATEGORIES 11 THROUGH 15 TRANSPORTATION ACTIVITIES: CONDITIONAL SOURCE SEPARATION SUBJECT TO TRIGGER PRICE; TRIGGER PRICE = CAT - 10 ACTIVITY LEVELS IN THOUSAND TONS OF PRESEPARATEO MSW PER YEAR

CAT ORIGIN SOURCE LOC DESTINATION SITE LOC COMMODITY ACTIVITY LEVEL TRAN STA

11 1 LYNN 1 1 SAUGUS PROC 5 5 S-S RES IN VAN. KTON 340.0000

11 3 SALEM 3 1 SAUGUS PROC 5 5 S-S RES IN VAN. KTON 120.0000

116
Shipments of recoverables are summarized under trnasportation category 3

activities. Two commodities are shipped to two different markets. On the

first line, a shipment of 1630 million pounds of steam per year from the

Saugus processor to the GE plant at Lynn is indicated. (Explicit revenues from the sale are summarized on exhibit 4.11.) The second line of the output

indicates that approximately 20 KTPY of incinerated ferrous is shipped from

the Saugus processor to scrap dealers in New York City.

Transportation category 5 shows that Marblehead and Revere should practice

unconditional source separation and ship the source separation residue to the

Saugus processor. Since these communities are fairly small, the use of a dedicated transfer station was not found to be cost effective, as indicated by

the zero entry under TRAN STA SEG.

Lynn and Salem, on the other hand, should practice conditional source separation and ship the source separation residue and whatever paper is to be

burned to the Saugus processor. The solution produced by the model shows that

selecting the lowest trigger price, a value of $15 per ton, will have the most

favorable effect on the cost of the plan. Thus some paper will be burned in

order to generate greater revenues from steam sales. Since Lynn and Salem

produce much larger amounts of MSW than the two other communities, the use of

dedicated transfer stations was found to be cost-effective. The one in Lynn

operates within the third linear segment of the cost function; the one in

Salem operates within the second linear segment. ^ Note that Saugus is the

offload point for all source-to-site shipments.

Exhibit 4.10 summarizes the processing activities appearing in the solution.

The first two activities deal with the processing of source separation residue

and source separated paper when processed as waste. Since all four sources were separating their wastes, the activity level is equal to the product of

the total annual waste stream of 507 KTPY and the portion of the waste stream

which corresponds to source separation residue (57%). A value of

approximately 289 KTPY results. Processing of source separated paper is

somewhat more complex since it is based on the trigger price system. The

amount of source separated paper processed as waste is in this case equal to

the product of 0.232 (the proportion of paper in the waste stream by weight)

by 0.067 (the burn rate for the first trigger price) by 460 (the amount of MSW

generated by Lynn and Salem combined in KTPY). The figure shown on the output

is slightly over 7 KTPY.

In order to calculate the costs of the transfer stations, it would be

necessary to use the process cost information shown on exhibit 4.4. Once the

appropriate segment number has been identified, as in exhibit 4.9, the

operating and capital costs can be easily calculated. For this example, the

operating costs of the station would be equal to the 1ST COST INTERCEPT plus

the product of the SLOPE and equivalent tonnage (see exhibit 4.14 in section

4.3). The capital costs of the station would be calculated in the same manner.

117
Exhibit 4.10 Processing Activities in the Solution

PROCESS ACTIVITY LEVELS PASS-THROUGH PROCESSING OF SOURCE SEPARATION RESIDUE

SITE ACTIVITY LEVELS IN THOUSAND TONS OF RESIDUE PER YEAR

1 SAUGUS PROC 288.9901 PASS-THROUGH PROCESSING OF SOURCE • SEPARATED PAPER SITE ACTIVITY LEVELS IN THOUSAND TONS OF PAPER PER YEAR

1 SAUGUS PROC 7. 1148 PROCESSING ACTIVITIES IN COMMODITY UNITS INPUT PER YEAR

SITE PROCESS LOC SEG INPUT COMMODITY ACTIVITY LEVEL CAP PCT CAP

1 SAUGUS PROC 2 WATER-WALL INCIN 5 1 2 MSW IN VAN, KTON 297.6568 360.000 82.68

4 SAUGUS SECONDARY PR 5 SECONDARY PROC 5 1 7 PROC RESIDUE, KTON 44.6487

DEDICATED TRANSFER STATIONS; TONNAGE IN THOUSAND TONS PER YEAR SOURCE LOC SEG UNSEPARATED TONS SOURCE SEP RESIDUE TONS TOTAL TONS

1 LYNN 1 3 .00 193.80 193.80

3 SALEM 3 2 .00 68.40 68.40 LANDFILL ACTIVITIES MEASURED IN ACRE-FEET

LANDFILL LOC LAND REQUIRED LAND AVAILABLE PERCENT USED LAND REMAINING

AMESBURY LANDFILL 7 .0000 99300.0000 .00 99800.0000

SAUGUS LANDFILL 5 732.2389 84000.0000 .87 83267.7617

118
The next block of information on the output, shows the annual processing

activities for the water wall incinerator at Saugus and the secondary

processor. The water wall incinerator operates on the first segment^ and

receives MSW as its input. Since the process receives a mixture of source separation residue and source separated paper, the activity level shown

deviates slightly from the sum of the two activities discussed previously.

This is due to a distinction between "real" tons and "equivalent" tons. Real

tons are equal to the weight of the wastes processed. Equivalent tons

translate real tonnage into a number which reflects the cost of processing.

Since the costs of processing source separation residue and paper differ, it

is necessary to reduce them to an equivalent amount. Tliis point will become

more clear when exhibit 4.14 is examined in section 4.3. Using equivalent

tons as the processing measure, the final entry shows that the plant operates

at approximately 83 per cent of its rated capacity (360 KTPY) . As indicated

on the previous output, the secondary processor receives the residue from the

water wall incinerator. The 44 KTPY processing activity resulting from the

shipment is shown on this output (all wastes arriving at a site must be

processed)

The next block of information is an explicit summary of dedicated transfer

station activity. The output identifies not only the sources where such

stations are located but also the linear segment on which the process should

operate. Shipments of both MSW and source separation residue are given along

with the total tonnage from all sources processed.

The final output on exhibit 4.10 shows the landfill activity. All landfills

are summarized, even if it is not cost-effective to include them in the plan.

The landfill located at Saugus is used to dispose of those elements which

cannot be reclaimed from the process residue. For the case at hand, 732 acre feet of the landfill's capacity are required over the 20 year study period.

This corresponds to less than 1 percent of the total land available at the

site.

The market activities in the solution are summarized in exhibit 4.11. The

output is divided into three parts. The first part deals with market

activities of explicit commodities (i.e., commodities defined by the user, see exhibit 4.2). The second part deals with marketing of source separation

recoverables . The third part deals with implicit revenues which are created

whenever processing takes place (see exhibit 4.4 where such revenues are defined for each SIPROC)

There is only one segment for this process (see exhibit 4.4 process number

2).

119
Marketing activities of explicit coitimodities are summarized in two ways. The

first summary table shows the market number, name, location, and segment

within which sales are taking place. Information on the commodity sold

includes the volume of sales in commodity units per year, the cumulative sales

(by segment), and, if appropriate, the market limit and the percent of the

limit for cumulative sales. Each market segment in the solution occupies a line of output. The second summary table provides information on revenues. These are gross revenue figures so that haul costs must be deducted to get

net revenues. As for the previous table, the market number and name,

commodity number and name and market segment number are listed. The revenue summary includes the average revenue^ in thousands of dollars per year, the

cumulative revenue, the discounted revenue^, and the cumulative discounted

revenue. For example, the GE Lynn plant is sold steam in two blocks. The

first 900 million pounds of steam produce a gross revenue of slightly over $2

million, whereas the next shipment of approximately 730 million pounds

produces an additional gross revenue of $1.1 million. Cumulative sales are therefore 1630 million pounds of steam for a gross revenue of approximately

$3.1 million.

Marketing activities of source separation recoverables are summarized in a similar fashion. First, the total sales of glass, cans and paper in KTPY are presented. The gross revenue (both undiscounted and discounted) associated

with each recoverable are then output. The last piece of information shows

the net revenues associated with any SIPROCs in the solution. This output

lists the site number and name, the process number and name, the input

commodity number and name, the revenue per unit of input (e.g., KTPY) declared

on the SIPROC card, and the average undiscounted and discounted revenue per

year.

Exhibit 4.12 summarizes revenue by market type. This summary is useful

because the output data on explicit commodity sales and sales of source separation recoverables represented gross rather than net revenues. Exhibit

4.12 consists of two parts. The first part summarizes gross revenues, haul

The average revenue calculation is based on a multiplier which incorporates

the differential inflation rate for the respective cost category but to which

no discount rate is applied.

The discounted revenue calculation is based on a multiplier which

incorporates both the differential inflation rate for the respective cost

category and the discount rate to reflect the time value of money.

120
Exhibit 4.11 Marketing Activities in the Solution

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121
costs and net revenue by market type^ , sunnnlng them to get a total net average

undlscounted revenue figure. The second part is identical to the first except

that all figures are discounted . All entries in both sections are in

thousands of dollars per average year.

The analytical summary of the output is presented as exhibit 4.13. The output

reports shown as exhibits 4,9 through 4.12 were designed to lay out specific

attributes and illustrate the mechanics of implementing the plan. This output

report is designed to show how the preferred regional plan looks in a more general setting. The output is divided into four parts. The first part

consists of a lifetime summary by key factor. These factors include summaries over the study period and per year for: tonnage; undiscounted and discounted

costs; and net energy. Figures on the cost per ton (which incorporate all

cash flows positive and negative) and the net energy per ton are also output.

The differential effects of the plan on the various segments of the community

are laid out under the heading COST CATEGORY SUMMARY, Data on each cost

category are summarized on a line of output. Costs are labeled as operating

or capital. Four column headings to the right show whether the cost is:

(1) LIFETIME COST; (2) COST PER YEAR; (3) LIFETIME DISCOUNTED COST; or

(4) DISCOUNTED COST PER YEAR. All costs are in thousands of dollars. In the

printout some costs may be positive (disbursements) and some negative (net

revenues). For example, contractor operating cost is shown as -1207. This means that on the average the operation of the Saugus plant produces $1.2

million dollars of revenue annually net not only of haul cost but also of

operating costs. ^ Note that lifetime costs are equal to the product of the

annual costs and the length of the study period.

The three market types are: explicit commodity; source separation

recoverables; and implicit markets. Revenues from implicit markets are already net of haul, so only one line of output is necessary. The reader

should note the distinction between market type as used above and market

structure as used in section 2.2. All sales and haul costs within a particular market type are aggregated in the printout.

Operating costs (in thousands of dollars) or the plant can be calculated by

noting that total revenue for the plant are 3681 (3353 + 328) . Since the

cost per year was listed as -1207, this would imply that annual operating

costs are 2474 (3681 -1207).

122
Exhibit 4.12 Revenue Summary by Market Type

REVENUE SUMMARY IN THOUSAND DOLLARS PER AVERAGE YEAR. UNDISCCUNTED

3353. MARKET VALUE OF EXPLICIT COMMODITIES

355. HAUL COST TO MARKET

2998. NET REVENUE OF EXPLICIT COMMODITIES

5976. MARKET VALUE OF SOURCE SEPARATION RECOVERABLES

441 . HAUL COST TO MARKET

5535. NET REVENUE OF SOURCE SEPARATION RECOVERABLES

328. IMPLICIT REVENUES NET OF HAUL COST TO MARKET

8861 . TOTAL REVENUE PER AVERAGE YEAR

REVENUE SUMMARY IN THOUSAND DOLLARS PER AVERAGE YEAR, DISCOUNTED

1715. MARKET VALUE OF EXPLICIT COMMODITIES

182. HAUL COST TO MARKET

1533. NET REVENUE OF EXPLICIT COMMODITIES

2635. MARKET VALUE OF SOURCE SEPARATION RECOVERABLES

195. HAUL COST TO MARKET

2440. NET REVENUE OF SOURCE SEPARATION RECOVERABLES

168. IMPLICIT REVENUES NET OF HAUL COST TO MARKET

4141. TOTAL REVENUE PER AVERAGE YEAR

123
Exhibit 4.13 Analytical SuDimary of the Soluti

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124
The cost summation categories are printed out next. In this case, they show

the total cost to the contractor for operation of the Saugus plant. As

before, a negative value implies that the plant can operate at a profit. Cost

summation categories are a convenient way for aggregating costs so that it

becomes clear who benefits and who bears the burden of the program. The final

piece of information is entitled ENERGY CATEGORY SUMMARY, This output lists

the energy category number and name along with the lifetime net energy and the

net energy per year. In this output, a negative value implies a net input of

energy is required,

4,3 POST-OPTIMALITY ANALYSIS

The outputs presented in this section constitute a post-optimality analysis of

the preferred regional plan. The term post-optimality is used above because

the analysis which generates the output reports shown as exhibits 4,14 through

4,20 is done after the solution returns from the optimizer. Such an analysis

permits the user to trace through some of the implications associated with the

siting, marketing and routing details of the solution presented in the

previous section. There are three types of post-optimality analysis described

in this section. They focus on splits, routing requests, and cost analyses,

respectively. It was mentioned earlier that because of site capacity

constraints, landfill constraints, and market limits, transportation and siteprocess splits can occur. Exhibit 4,14 summarizes the post-optimality

analysis of the three types of splits. The first block of data on exhibit

4,14 is concerned with source tonnage splits. These splits occur because

sources may be split among offload sites; even if this is not true, some tonnage may go to source separation recoverables markets. By examining the

upper portion of the exhibit, it can be seen that four lines of output are required for each source. For example, Lynn ships glass, cans and paper to

the recoverables market and source separation residue plus a small fraction of

paper to the Saugus processor. The fourth line shows that all 340 KTPY are allocated, 1 The remaining columns in the output help to define the difference

between real tonnage and percentages and equivalent tonnage and percentages.

The real ton column is equal to the product of the real percent and the source tonnage recorded under ALLOC TNS. For example, 11.80 percent of Lynn's solid

waste is glass sent to the market; this corresponds to the 40.12 KTPY figure

recorded under REAL TNS. Equivalent tons are used as inputs to processes and

reflect the real cost of processing the solid waste stream. The values of the

equivalent tonnage are based on the values of RPRPAP, ALLO, and RPRRES, input

by the user and shown in exhibit 4.1. In this case, for Lynn, the 23.2

percent of the waste stream accounted for by paper was split; 21,65 percent

went to the national paper market and 1.55 percent went to the Saugus

processor to be burned. The 57 percent of the waste stream corresponding to

Full preseparation tonnage is allocated to the offload site of the residue.

125
Exhibit 4.14 Analysis of Splits Occurring in the Solution:

Tonnage, Site/SIPROC, Location-Commodity

Source

SOURCE TONNAGE SPLITS

LINK COSTS FOR SITE OFFLQADS INCLUDE ALLOCATED REVENUES AND HAUL COSTS OF GLASS, CAN. AND NATIONAL PAPER MARKETS.

ALL TONS IN THOUSAND TONS PER YEAR. ALL COSTS IN UNDISCOUNTED COST PER TON, AVERAGED OVER YRS OF RUN. ALLOC TNS AND ALLOC PCT ARE PRESEPARATED 000 TONS AND PCT, ALL ALLOCATED TO OFFLOAD OF RESIDUE (USED FOR LINK COSTS), REAL TNS AND REAL PCT ARE 000 TONS AND PCT AS DELIVERED TO SITES AND MARKETS.

EOUV TNS AND EOUV PCT ARE 000 TONS AND PCT IN EQUIV MSW (EOUIV IN PROCESSING COST) (USED FOR OFFLOAD COSTS). SOURCE OFFLOAD ALLOC TNS ALLOC PCT REAL TNS REAL PCT EOUV TNS EOUV PCT LINK

1 LYNN GLASS MARKET 40 12 1 1 80

1 LYNN CANS MARKET 27 20 8 00

1 LYNN PAPER MARKET 73 62 21 65

1 LYNN 1 SAUGUS PROC 340 00 100 00 199 06 58 55 202 19 59 47 16

2 MARBLEHEAD GLASS MARKET 83 1 1 80

2 MARBLEHEAD CANS MARKET 56 8 00

2 MARBLEHEAD PAPER MARKET 1 62 23 20

2 MARBLEHEAD 1 SAUGUS PROC 7 00 100 00 3 99 57 00 3 59 51 30 19

3 SALEM GLASS MARKET 14 16 1 1 80

3 SALEM CANS MARKET 9 60 8 00

3 SALEM PAPER MARKET 25 98 21 65

3 SALEM 1 SAUGUS PROC 120 00 100 00 70 26 58 55 71 36 59 47 17

4 REVERE GLASS MARKET 4 72 1

1

80

A REVERE CANS MARKET 3 20 8 00

4 REVERE PAPER MARKET 9 28 23 20

4 REVERE 1 SAUGUS PROC 40 00 100 00 22 80 57 00 20 52 51 30 16

SITE/SIPROC SPLITS; TONNAGE IN THOUSAND TONS OR COMMODITY UNITS PER YEAR

SITE COMMODITY PROCESS

1 SAUGUS PROC

4 SAUGUS SECONDARY PR

2 MSW IN VAN, KTON

7 PROC RESIDUE. KTON

2 WATER-WALL INCIN

5 SECONDARY PROC

TONNAGE PCT OF SITE TONNAGE

297.7 100.00

44 . 6 1 00 . 00

LOCATION-COMMODITY SPLITS: COST PER TON IS UNDISCOUNTED, AVERAGED OVER YRS OF RUN.. ACT IS ACTIVITY LEVEL IN THOUSAND TONS OR COMMODITY UNITS PER YEAR.

LOC COMMODITY OFFLOAD: SITE MKT NAME ACT PCT LOC-COMM TNS LINK COST/TON

5 7 PROC RESIDUE. KTON 4 0 SAUGUS SECONDARY PR 44.6 100.00 .00

5 6 STEAM, MLBS 0 1 GE LYNN STEAM 1631.2 100.00 .00

5 8 INCIN FERROUS. KTON 0 2 NEW YORK FERROUS 20.8 100.00 17.03

126
source separation residue was sent to Saugus. Since ALLO is defined as the

allocation of paper processing cost of actual paper processed as waste versus potential paper processed as waste when the trigger price system is used (as

for Lynn and Salem) , the computation of equivalent tonnage becomes more complex because potential processing costs must be accounted for. From

exhibit 4,1 it can be seen that the appropriate values are: RPRPAP, 0.8;

ALLO, 0.6; RPRRES, 0.9. The equivalent tonnage of residue is thus 0.9 times

0.57 times 340 or 174.42. The equivalent tonnage of paper actually processed

as waste is 0.8 times 0.0155 times 340 or 4.22. The equivalent tonnage of

paper potentially processable as waste is 0.8 times 0.2155 times 0.4 (i.e.,

1-ALLO) times 340 or 23.56. The total equivalent tonnage is therefore 202.2;

the associated equivalent percentage is 59.47. The link cost is defined as the cost per ton of collection, source separation and haul to the offload site

minus net revenues due to the sale of source separation recoverables in the

market. The link cost for Lynn is $16.51 per ton.

The SITE/SIPROC splits are then output. Sites may be split among the

processes offered due to constraints at other sites or at the market. The

printout provides information on the site, the commodity, the process, the

tonnage and the percent of the site tonnage. The two sites which were in the

solution did not experience splits as can be seen by the entry of 100.0 under

the heading, PCT OF SITE TONNAGE. Each site is shown as a single line of

output

.

Location-commodity splits are then output. A location from which a process

output commodity is shipped may be split among offload sites and markets. The

printout provides information on the origin location number, the commodity,

the destination site or market, the activity level, the percent of locationcommodity tons, and the link cost per ton. The shipments from the Saugus

processor did not experience splits, as can be seen by the entry of 100.0

under the heading PCT LOC-COMM TNS. Each location commodity is shown as a single line of output.

Exhibit 4.15 summarizes each of the four site routing requests. For a single

site at a level, the procedure will trace routing through the SITE/SIPROC

splits, through the output coefficients of each process, and through the

location-commodity splits, to a series of sites and markets. At each point in

the routing, the activity level as a ratio of the activity level at the

subject site is maintained. The end-point sites are assigned a continuation

number for the next level, and the continuation factor (ratio of activity

level at the end-point of the level to subject site activity level) is

recorded to be used at the start of the next level for that site. There is no corresponding continuation for markets.

127
Exhibit 4.15 Analysis of Site Routings for the Four Sites Considered

by the Model

SITE ROUTING REQUEST

SUBJECT SITE: 1 SAUGUS PROC PROCESS

2 WATER-WALL INCIN COEFFICIENTS: COMMODITY

6 STEAM, MLBS

INPUT COMMOD:

PCT OF SITE 100.00

2 MSW IN VAN, CONT ACT LEV 297. 7KTON LOC CONT FACTOR

1 . 0000000

5 ACT LEVEL: 297.657

CONT FACTOR 5.4799999

OFFLOAD: SITE MKT

0 1 CONT FACTOR

. 1500000

COEFFICIENT CONT ACT LEV 5.480000 1631.2 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR

100.00 1631 .2 5.4799999

COMMODITY COEFFICIENT CONT ACT LEV

7 PROC RESIDUE, KTON .150000 44.6 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT 100.00 44.6 .1500000 4 0 COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

8 INCIN FERROUS, KTON .070000 20.8 .0700000 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT 100.00 20.8 .0700000 0 2

NAME

GE LYNN STEAM

CONT IN NO

NAME SAUGUS CONTIN SECONDARY PR

NO

1

NAME

NEW YORK FERROUS CONTIN NO

LEVEL NUMBER: 2 CONTIN NO 1 SITE

4 SAUGUS SECONDARY PR PROCESS

5 SECONDARY PROC

INPUT COMMODITY LOC

7 PROC RESIDUE, KTON 5 PCT OF SITE CONT ACT LEV 100.00 44.6

CONTINUATION ACT LEV 44.6

CONT FACTOR

. 1500000

CONTINUATION FACTOR .1500000

SITE ROUTING REQUEST

SUBJECT SITE: 2 WORCESTER PROC INPUT COMMOD: 2 MSW IN VAN. KTON LOC 6 ACT LEVEL: .000 SUBJECT SITE IS NOT IN THE SOLUTION

SITE ROUTING REQUEST

SUBJECT SITE: 3 AMESBURY LANDFILL INPUT COMMOD: 7 PROC RESIDUE. KTON LOC 7 ACT LEVEL: .000 SUBJECT SITE IS NOT IN THE SOLUTION

SITE ROUTING REQUEST

SUBJECT SITE: 4 SAUGUS SECONDARY PR PROCESS

5 SECONDARY PROC

INPUT COMMOD: 7 PROC RESIDUE, KTON LOC

PCT OF SITE CONT ACT LEV CONT FACTOR 100.00 44.6 1.0000000

5 ACT LEVEL: 44 . 649

128
All sites at one level will be analyzed before any sites at the next

continuation level. Thus the list of continuation sites will be generated in

order of the sites from which they were generated at the preceding level. A

single site may well appear more than once at a single level; but if so, the

site at each appearance would be assigned a different continuation number, and

would probably also show a different continuation factor.

For a subject site, the following is reported: subject site number and name;

input commodity number and name; location; activity level. For a continuation

site, the following is reported: level number; continuation number; site

number and name; commodity number and name; location; continuation activity

level; continuation factor. For each process at the site, the following is

reported: process number and name; percent of site; continuation activity

level; continuation factor. All coefficients for a process are analyzed

before the next process is analyzed. For each coefficient , the following is

reported: commodity number and name; coefficient; continuation activity

level; continuation factor. All location-commodity splits for a coefficient

are analyzed before the next coefficient is analyzed. For each locationcommodity split , the following is reported: percent of location-commodity;

continuation activity level; continuation factor; offload site or market

number and name; continuation number.

Exhibits 4.16 through 4.19 are source routing requests. For a source (first

level only) the procedure will trace routing and continuation factors only to

the first offload, using the equivalent percents of the source splits. Since

all four exhibits are very similar, only the first which deals with Lynn will

be discussed in any detail. Once at the site, the offload tonnage from the

source, measured as the process CONT ACT LEV on the output, will be traced

through the process(es). For example, 202.2 MSW equivalent tons arrive at

Saugus from Lynn at level 1. Processing this waste at level 2 produces: (a)

1108 million pounds of steam per year for sale to the GE plant at Lynn; (b)

30.3 KTPY of process residue sent to site 4 (Saugus secondary processor) with

a continuation to level 3; (c) 14.2 KTPY of incinerated ferrous for delivery

to New York City scrap dealers. At level 3, the Saugus secondary processor

either disposes of the wastes or markets recoverables from it through the

implicit market (revenue) defined on the SIPROC card; there is no continuation

to level 4.

Exhibit 4.20 consists of a source cost analysis and a site cost analysis. The

two sets of results are somewhat interdependent, so it will be necessary to

move from one block of data to the other. The source cost analysis shows for

each source, its name location and tonnage, the offload point, the allocation

percentage, the link cost, the equivalent percentage, the offload cost and the

total source cost per ton. All terms but the last two were discussed as a part of the output report shown as exhibit 4.14. The two new terms, offload

cost and total source cost per ton are closely related to the tipping fee

calculation summarized under the site cost analysis. The two crucial factors

129
Exhibit 4.16 Analysis of Source Routings for Lynn, Massachusetts

SOURCE ROUTING REQUESTS

FIRST MEASURE IS USED TO GENERATE LINK COSTS;

<;FrOND MEASURE IS USED TO GENERATE OFFLOAD COST. Al"^ErJI5taGES AREPERCENTAGES OF TOTAL SOURCE TONS BEFORE SEPARATION.

i

SOURCE ROUTING REQUEST '

SOURCE: 1 LYNN LOCATION 1 SOURCE TONS (KTPY): 340.000

'

OFFLOAD: SITE MKT NAME PRESEP TONS PRESEP PCT OFFLOAD TONS OFFLOAD PCT CONTIN NO

1 0 SAUGUS PROC 340.0 100.00 202.2 59.47 1

LEVEL NUMBER: 2 CONTIN NO 1 SITE INPUT COMMODITY LOC CONTINUATION ACT LEV CONTINUATION FACTOR

1 SAUGUS PROC 2 MSW IN VAN. KTON 5 202.2 .5946640

PROCESS PCT OF SITE CONT ACT LEV CONT FACTOR

2 WATER-WALL INCIN 100.00 202.2 .5946640

COEFFICIENTS:

COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

6 STEAM, MLBS 5.480000 1108.0 3.2587585

LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN

100.00 1108.0 3.2587585 0 1 GE LYNN STEAW

COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

7 PROC RESIDUE, KTON .150000 30.3 .0891996

LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN' 100,00 30.3 .0891996 4 0 SAUGUS SECONDARY PR COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

8 INCIN FERROUS, KTON .070000 14.2 .0416265

LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN

100.00 14.2 .0416265 0 2 NEW YORK FERROUS

LEVEL NUMBER: 3 CONTIN NO 1 SITE

4 SAUGUS SECONDARY PR PROCESS

5 SECONDARY PROC

INPUT COMMODITY LOC

7 PROC RESIDUE KTON 5 PCT OF SITE CONT ACT LEV 100.00 30.3

CONTINUATION ACT LEV 30.3

CONT FACTOR

.0891996

CONTINUATION FACTOR .0891996

130
Exhibit 4.17 Analysis of Source Routings for Marblehead, Massachusetts

SOURCE ROUTING REQUEST

SOURCE: 2 MARBLEHEAD LOCATION 2 SOURCE TONS (KTPY): 7.000

OFFLOAD: SITE MKT NAME PRESEP TONS PRESEP PCT OFFLOAD TONS OFFLOAD PCT CONTIN NO

1 0 SAUGUS PROC 7.0 100.00 3.6 51.30 1

LEVEL NUMBER: 2 CONTIN NO 1 SITE INPUT COMMODITY LOG CONTINUATION ACT LEV CONTINUATION FACTOR

1 SAUGUS PROC 2 MSW IN VAN, KTON 5 3.6 .5130000 PROCESS PCT OF SITE CONT ACT LEV CONT FACTOR

2 WATER-WALL INCIN 100.00 3.6 .5130000 COEFFICIENTS: COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

6 STEAM, MLBS 5.480000 19.7 2.8112399 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN NO 100.00 19.7 2.8112399 0 1 GE LYNN STEAM COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

7 PROC RESIDUE, KTON .150000 .5 .0769500 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN NO

100.00 .5 .0769500 4 0 SAUGUS SECONDARY PR 1 COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

8 INCIN FERROUS, KTON ,070000 .3 .0359100 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN NO 100.00 .3 .0359100 0 2 NEW YORK FERROUS

LEVEL NUMBER: 3 CONTIN NO 1 SITE INPUT COMMODITY LOC CONTINUATION ACT LEV CONTINUATION FACTOR

4 SAUGUS SECONDARY PR 7 PROC RESIDUE KTON 5 .5 .0769500 PROCESS PCT OF SITE CONT ACT LEV CONT FACTOR

5 SECONDARY PROC 100.00 .5 .0769500

131
Exhibit 4.18 Analysis of Source Routings for Salem, Massachusetts

SOURCE ROUTING REQUEST

SOURCE: 3 SALEM LOCATION 3 SOURCE TONS (KTPY): 120.000

OFFLOAD: SITE MKT NAME PRESEP TONS PRESEP PCT OFFLOAD TONS OFFLOAD PCT CONTIN NO

1 0 SAUGUS PROC 120.0 100.00 71.4 59.47 1

LEVEL NUMBER: 2 CONTIN NO 1 SITE INPUT COMMODITY LOC CONTINUATION ACT LEV CONTINUATION FACTOR

1 SAUGUS PROC 2 MSW IN VAN, KTON 5 71.4 .5946640 PROCESS PCT OF SITE CONT ACT LEV CONT FACTOR

2 WATER-WALL INCIN 100.00 71.4 .5946640 COEFFICIENTS: COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

6 STEAM, MLBS 5.480000 391.1 3.2587585 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD; SITE MKT NAME CONTIN NO 100.00 391.1 3.2587585 0 1 GE LYNN STEAM COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

7 PROC RESIDUE. KTON .150000. 10.7 .0891996 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN NO 100.00 10.7 .0891996 4 0 SAUGUS SECONDARY PR 1 COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

8 INCIN FERROUS. KTON .070000 5.0 .0416265 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN NO 100.00 5.0 .0416265 0 2 NEW YORK FERROUS

LEVEL NUMBER

CONTIN NO 1 3 SITE

4 SAUGUS SECONDARY PR PROCESS

5 SECONDARY PROC

INPUT COMMODITY

7 PROC RESIDUE

PCT OF SITE

1 00 . 00

LOC KTON 5 CONT ACT LEV 10.7

CONTINUATION ACT LEV 10.7 CONT FACTOR .0891996

CONTINUATION FACTOR .0891996

132
Exhibit 4.19 Analysis of Source Routings for Revere, Massachusett

SOURCE ROUTING REQUEST

SOURCE: A REVERE LOCATION 4 SOURCE TONS (KTPY): 40.000

OFFLOAD: SITE MKT NAME PRESEP TONS PRESEP PCT OFFLOAD TONS OFFLOAD POT CONTIN NO

1 0 SAUGUS PROC 40.0 100.00 20.5 51.30 1

LEVEL NUMBER: 2 CONTIN NO 1 SITE INPUT COMMODITY LOC CONTINUATION ACT LEV CONTINUATION FACTOR

1 SAUGUS PROC 2 MSW IN VAN, KTON 5 20.5 .5130000 PROCESS PCT OF SITE CONT ACT LEV CONT FACTOR

2 WATER-WALL INCIN 100.00 20.5 .5130000 COEFFICIENTS: COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

6 STEAM. MLBS 5.480000 112.4 2.8112398

LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN NO 100.00 112.4 2.8112398 0 1 GE LYNN STEAM

COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

7 PROC RESIDUE. KTON .150000 3.1 .0769500 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN NO 100.00 3.1 .0769500 • 4 0 SAUGUS SECONDARY PR 1 COMMODITY COEFFICIENT CONT ACT LEV CONT FACTOR

8 INCIN FERROUS, KTON .070000 1.4 .0359100 LOCATION-COMMODITY SPLIT

PCT OF LOC-COMM CONT ACT LEV CONT FACTOR OFFLOAD: SITE MKT NAME CONTIN NO 100.00 1.4 .0359100 0 2 NEW YORK FERROUS

CONTINTf's^E INPUT COMMODITY LOC CONTINUATION ACT LEV CONTINUATION FACTOR

4 SAUGUS SECONDARY PR 7 PROC RESIDUE KTON 5 3.1 .0769500 PROCESS PCT OF SITE CONT ACT LEV CONT FACTOR

5 SECONDARY PROC 100.00 3.1 .0769500

133
which influence the tipping fee are on-site cost and net off-site cost.

On-site costs are those operating and capital costs incurred because of

processing activity. They are measured through the two cost categories

declared on the process cards as well as any site prepration costs declared on the site card and matched to the process on the SIPROC card. Net off-site

costs, on the other hand, include implicit revenues and the implications of

all outputs of the process. The stun of these two costs is equal to the

tipping fee. The offload cost for the source may now be calculated by

multiplying the tipping fee by the equivalent percentage. The total source cost per ton is then defined as the sum of the link cost and the offload cost.

The total source cost per ton may also be used to show the relationship

between source costs and the undiscounted cost per ton given as part of the

analytical siimmary (see exhibit 4.13). If the proportion that each source contributes to the overall waste stream is designated as P-j^, where i is equal

to 1 for Lynn, 2 for Marblehead, 3 for Salem and 4 for Revere, and the total source cost as recorded on exhibit 4.20 is designated as C^j^, then the sum of

the PfCj^ products is equal to approximately $14.25 per ton.

134
Exhibit 4.20 Source Cost and Site Cost Analysis

SOURCE COST ANALYSIS

ALL COSTS ARE UNDISCOUNTED COST PER ORIGINAL PRESEPARATED SOURCE TON, AVERAGED OVER THE YEARS OF THE MODEL RUN. LINK COSTS INCLUDE REVENUES AND HAUL COSTS OF GLASS. CAN. AND NATIONAL PAPER MARKETS ALLOCATED TO OFFLOAD OF RESIDUE. ALLOC PCT IS TONNAGE AS PCT OF SOURCE TONNAGE. WITH SEPARABLES, AS ABOVE. ALLOCATED TO OFFLOAD OF RESIDUE. EOUIV PCT IS TONNAGE AS PCT OF SOURCE TONNAGE IN EQUIVALENT MSW . WITH EQUIVALENCE IN PROCESSING COST. SOURCE: 1 LYNN OFFLOAD

1 SAUGUS PROC

LOCATION: 1 TOTAL SOURCE TONS: 340.00

ALLOC PCT LINK COST EQUIV PCT OFFLOAD COST 100.00 16.51 59.47 -2.56

TOTAL SOURCE COST PER TON; SOURCE: 2 MARBLEHEAD

OFFLOAD

1 SAUGUS PROC

13.95

LOCATION: 2 ALLOC PCT LINK COST 100.00 19.17TOTAL SOURCE TONS: 7.00

EOUIV PCT OFFLOAD COST 51.30 -2.21

TOTAL SOURCE COST PER TON: SOURCE: 3 SALEM OFFLOAD

1 SAUGUS PROC

16.97

LOCATION: 3 ALLOC PCT LINK COST 100.00 17.23

TOTAL SOURCE TONS: 120.00

EOUIV PCT OFFLOAD COST 59.47 -2.56

TOTAL SOURCE COST PER TON: SOURCE: 4 REVERE OFFLOAD

1 SAUGUS PROC

14.67

LOCATION: 4 ALLOC PCT LINK COST 100.00 16.94

TOTAL SOURCE TONS: EOUIV PCT OFFLOAD COST 51.30 -2.21

40.00

TOTAL SOURCE COST PER TON: 14.73

SITE COST ANALYSIS

ACT IS ACTIVITY LEVEL IN THOUSAND TONS OR COMMC ALL COSTS IN UNDISCOUNTED COST PER SITE TON OR

SITE: 1 SAUGUS PROC LOCATION: 5 PROCESS ACT PCT OF SITE

2 WATER-WALL INCIN 297.65 100.00

SITE: 4 SAUGUS SECONDARY PR LOCATION: 5 PROCESS ACT PCT OF SITE

5 SECONDARY PROC 44.65 100.00

iD UNITS PER YEAR. COMMOD UNIT AVERAGED OVER YRS OF RUN.

ACT: 297.65 FULL SITE COST (TIPPING FEE): -4.30 ON-SITE COST NET OFF-SITE COST TOTAL COST 6.53 -10.83 -4.30

ACT: 44.65 FULL SITE COST (TIPPING FEE): .68 ON-SITE COST NET OFF-SITE COST TOTAL COST 2.96 -2.28 .68

135
5. GUIDANCE ON USING RRPLAN

The purpose of this chapter is to provide guidance for making intelligent use

of the model. The previous chapters dealt with the technical underpinnings of

the model and the mechanics of setting up and interpreting a case study. This

chapter goes beyond these basic issues by attempting to answer three major

questions associated with model use. First, what are the objectives of the

analysis? Second, what level of detail is appropriate for the case at hand?

Third, how should the study be st ructured? The first two questions and a number of related issues are the subject of section 5.1. The last question is

the subject of section 5.2.

5.1 SETTING UP A TEST RUN

Once all data have been collected and prior to an analysis with the model, a series of important questions must be answered. The answers to these

questions affect not only the mechanics of the optimization, through the

setting of run controls, but also the strategy for performing the technicaleconomic analysis of competing alternatives.

As an initial step, the user must carefully define the objectives of the study

and attempt to relate them to the data recorded on the worksheets. Some

typical objectives which senior management might wish to analyze involve:

(a) comparisons of recovery technologies with the status quo; (b) defining a strategy for shifting the costs of the program so that a more equitable

outcome is produced; (c) finding the most economical location for siting

competing alternatives; (d) development of a plan for upgrading existing

facilities or scheduling the construction of new facilities.

In performing any of the analyses mentioned above, it is essential that

sufficient detail be incorporated into the three data files so the model can make meaningful tradeoffs. For example, a baseline run using a minimum of

linkage can be used to establish the costs of the current program. Candidate

sites, processes and markets can then be defined for analysis of the recovery

technology run. A series of "what-if" questions can be addressed by changing

the values of certain inputs and rerunning the model. These results can then

be compared against the status quo to determine if the overall costs of

processing the region's solid waste has declined. Similarly, RRPLAN can provide a detailed cost accounting summary which could lead to changes in

tipping fees, a reallocation of vehicles among sources or sites, or point to areas where a contract for the sale of recoverables would be beneficial.

The focus will now shift to when and how to use a particular combination of

options so that the relationship to objectives, like those stated above,

becomes more clear. If the model is to be used effectively, it is crucial

that the values of 12 variables be set correctly. These 12 variables are:

(1) LPHASE; (2) NFORC; (3) KOVER; (4) KOPT; (5) LOPT; (6) KSUPP; (7) KMIX; (8)

KSEPO; (9) NTRIG; (10) RPRPAP; (11) ALLO; AND (12) RPRRES. Each variable will

be defined and systematically analyzed in the discussion which follows.

136
The first six appear on the run control card (always the first card in the case study file) . They govern the type and nature of all calculations

performed. The last six appear on the third card of the case study file.

Although they were discussed earlier, they are of sufficient importance to be

reexamined in this section.

The six run controls govern forcing (LPHASE, NFORC, KOVER) , the output from

the optimizer (KOPT, KSUPP) and the mode of operation (LOPT). The forcing

controls are independent of the two other sets of controls. The forcing mode

is entered through the settings of three inputs, all on the control card. The

purpose of each variable is as follows: (1) LPHASE tells the optimizer which

of the four phases discussed in section 2.2 is the last one; (2) NFORC

specifies the column forcing mode (i.e., single or double column forcing); and

(3) KOVER sets the control for site and site-process forcing and serves as an override for the limitation on problem size when double column forcing is

used.

For no force at all, set LPHASE to 3 and KOVER to 00 (NFORC is irrelevant, but

set to 1). For single column forcing, set LPHASE to 4, set KOVER to 00, and

set NFORC to 1. For double column forcing, set LPHASE to 4, set KOVER to GO,

and set NFORC to 2. In this case, a maximum matrix size of 50 rows and 100

columns is imposed to control the high computing costs of the double column

force. To override the matrix size and return to the general maximum matrix

size of 90 by 360, set KOVER to 99. Due to its effect on computational

expenses, the use of the override is not recommended.

For site forcing, set KOVER to 01; for site-process forcing, set KOVER to 02.

A KOVER of 01 or 02 will cause LPHASE to be reset to 3, and hence it will

override any other control on forcing. An LPHASE less than 3 or greater than

4 will be reset to 3. A KOVER greater than 02, including a 99 after the

matrix size is overridden, will be reset to 00. If all of the above controls

are left blank, the run will default to the no-force case.

The optimization reports are controlled by two inputs on the run control card:

KOPT and KSUPP. The alternative settings of KOPT will be considered below. A

KSUPP of 0 will suppress reports on the search for an initial feasible

solution. The optimizer is called once except in site and site-process

forcing, in which it is called iteratively. Reports are generated only for

the first call.

For either site forcing or site-process forcing, controls are provided on card number 5 for overriding the values of variable and fixed costs used for

"urging in". These controls are: (1) IFDRV, the control for the fixed cost

driver; (2) IVDRV, the control for the variable cost driver; (3) FDRIV, the

value of the fixed cost driver; and (4) VDRIV, the value of the variable cost

driver. The built-in values may be applied by leaving these controls blank.

137
If KSUPP is 0, all reports on the search for an initial feasible solution

will be suppressed, and if there is no negative activity level in the initial

basis, there will also be no report. If KSUPP is greater than 0, and if

there is at least one negative activity level in the initial basis, the

following will be reported:

The basis in column number and the activity levels.

The new column and the old column in index form.

The basis in column index and the activity levels.

The column index is the sequential number of the column; the column number is

the same before expansion, but artificials are numbered 2000 plus the row number, and slacks are numbered 3000 plus the row number.^ The old column is

the one with the largest negative activity level (it will always be a slack)

and the new column corresponds to activity NZ which is always the last column

before expansion, and which relieves all constraints.

If KOPT is 2 or 3, a summary of the optimization is then issued, showing:

Number of rows, number of columns (after expansion of the matrix for

slacks and artificials), number of non-zero coefficients in the matrix

(after expansion), and the actual size of the matrix in elements.

If KOPT is 1, the matrix is shown in detail (after expansion) as follows:

(1) The last phase and that the method of steepest descent will be

used.

(ii) Number of rows; for each:

Row number, type (0 is equation; 1 is less-than-or-equal

constraint), right-hand side.

(lii) Number of columns, for each:

Column index, column number, variable cost, fixed cost.

(iv) Number of non-zero coefficients; for each:

Coefficient index, row number, column number, coefficient.

There is one artificial variable for every equality constraint and one slack

variable for every less than or equal to constraint. These variables are introduced at the start of the search for an optimal solution in order to

provide an identity basis. An excellent introductory text for those readers

who wish more information on this subject is: S. I. Gass, Linear

Programming; Methods and Applications , Fourth Edition, (New York:

McGraw-Hill Book Company, 1975).

138
If KOPT is 1, the following will be reported after the solution:

The user-supplied initial basis in column number form (this is actually

the initial feasible basis);

Phase 3 and 4 solutions (as appropriate) in column number and activity

level.

If activity NZ is found to be in the solution at the end of optimization,

there is indeed no feasible solution — that fact is reported and the run is

stopped. If a scenario evaluation is being conducted, activity NZ will have

been replaced with activity NZX, which also relieves all constraints at lower

cost, so that the run will proceed to the back end of RRPLAN for

interpretation and analysis of the solution.

As mentioned earlier, the model has five basic modes of operation. Each mode

serves to define the type of objective which is to be minimized or maximized.

The first two modes of operation minimize the total cost of the regional plan over a specified planning period. If both the timing and magnitude of cash

flows are important, then the discounted costs of the regional plan should be

minimized. This may be accomplished by setting LOPT to a value of 0. If only

the magnitude of cash flows is important then undiscounted costs should be

minimized. This may be accomplished by setting LOPT to a value 1. It is

important to note that the choice of discounted rather than undiscounted costs

and vice versa may cause both the costs of the plan and the physical flows

within the system to differ. The third mode of operation seeks to maximize

the net energy (energy produced (saved) from (due to) resource recovery

activities minus all other energy inputs) of the regional plan. Such an objective function might be useful in comparing various waste-to-energy

programs. The model will maximize net energy if LOPT is set to a value of 2.

The fourth mode of operation seeks to minimize a linear form (weighted

sum) of program cost and net energy. This approach might prove useful in

comparing mixtures of traditional and waste-to-energy programs if some form of

matching formula for funds was in effect. This may be- accomplished by setting

LOPT to a value of 3. LOPT = 3 is also useful if the user wants to require

other than equal weighting for cost and energy categories. The final mode of

operation focuses on the topic of scenario evaluation . For example, a region

may have a proposed plan which needs to be evaluated from the viewpoint of

technical and economic feasibility. Typical questions addressed under this

mode of operation would include the following. Are all facilities able to

process the indicated waste stream without exceeding their rated capacity, or that of plants down the line which they feed into? Can resources be

reallocated so that overall costs are reduced? The model will check the

feasibility of a prespecified plan if LOPT is set to a value of 4.

The next three variables (KMIX, KSEPO, NTRIG) affect not only the objective

being analyzed but also the size of the matrix. Therefore, in first

attempting to solve an actual case study, it would be wise to minimize the

size of the problem. This can be done by setting KMIX equal to 1 and KSEPO

and NTRIG equal to 0. In this case, mixed MSW will always be sent to the

candidate sites for processing. Thus, the A matrix would not include any rows

or columns which would be associated with conditional or unconditional source separation. All of the source control values (IMIXSU, ISEPSU, ITRIGS(K)) may

be coded onto the cards read on KEY 11, but since they are dominated by the

139
run controls, they will be inoperative. Consequently, once this simplified

example has been run, the source separation options can be explored by

"turning on" the values of KSEPO and NTRIG.

The values of the last three variables (RPRPAP, ALLO, RPRRES) have a significant impact on processing costs due to the distinction between real

tonnage and equivalent tonnage. RPRPAP and RPRRES are only used if either

form of source separation (i.e., conditional or unconditional) is offered in

the run. ALLO is only used if the trigger price system (i.e. conditional

source separation) is offered in the run.

There are several practical considerations which govern the amount of detail

desirable for a given case study. These considerations affect primarily the

designation of cost, energy and commodity categories. First, all topics

should be ranked according to their importance to the decision maker. This

implies that greater effort should be made to obtain more precise information

on selected "high visibility" items, rather than spending an inordinate

amount of time on relatively unimportant data. Ideally, all data would be of

a comparable degree of accuracy. Second, geographical considerations should

be made explicit. If a region has several metropolitan areas and several

rural areas, the data base should be constructed in such a manner that undue

emphasis is not placed on one area at the expense of the other. For example,

metropolitan areas may have the greatest impact on processing costs whereas

rural areas may have the greatest impact on transportation costs. Thus both are capable of influencing siting decisions. Third, as stated in section 3.3, care must be exercised in linking up sources with sites and sites with other

sites and markets. If the model is not given an opportunity to choose among

competing linkages, the model will do little more than print back the case input. Finally, processing and marketing activities must be analyzed in an objective manner. For example, if problem size is of importance and a market

for an output is unlimited, then all market activities for that commodity can

be modeled through an implicit revenue declared on the SIPROC card. If the

market has a declining price, then such an approach would be advisible only if

processing activity was sufficiently small that the marketing was likely to be

carried out in the first segment.

The last topic serves to underscore the claim that the single greatest

constraint on detail is the size of the problem which the model can solve.

Since RRPLAN can not solve any problem with more than 90 rows (constraints) or 360 columns (activities) , it is essential that users be able to accurately

calculate the size of the problem before an attempt to solve it is made.

Tables 5.1 and 5.2 provide step-by-step instructions for calculating the size

of the problem. Table 5.1 contains the calculation for the row dimension;

table 5.2 contains the calculation for the column dimension.

140
Table 5.1 Guidelines for Calculating the Number of Rows in the A Matrix

Constraint Type Allocate one row each:

Source balance equations

Source paper balance equations

Site input residue balance equations

Site input paper balance equations

Site input balance equations for MSW

Site input balance equations for other

commodities

Site output balance equations

Market input balance equations

Site capacity constraints

Land capacity constraints

Market bounds

Constraint on artificial processing

activities

Source

Source coded for unconditional

source separation with a local paper

market

Site capable of receiving source separation residue

Site capable of receiving source separated paper

Site receiving MSW

Site receiving any non-MSW commodity

Location-commodity combination

Market

Capacitated site

Land using location

Market segment for which there is an upper bound

Problem formulation^

^The model builds in one column per site to take on a very small value

(epsilon) without incurring a fixed cost. Such columns are needed to balance

epsilon values on the right hand sides of site input balance equations, which

in turn retain these equations in use even if the sites are actually

inactive. Total inactivity of an equation could cause an inability to invert

the basis during optimization. These artificial processing activities are upper bounded with a single constraint which is always the last row in the

matrix.

141
Table 5.2 Guidelines for Calculating the Number of Columns in the A Matrix

Activity Type Allocate one column for each:

Category 2 transportation Site— to— site shipment declared on read KEY 22

Category 3 transportation Site-to-market shipment declared on read KEY 22

Category 4 transportation Source— to— site shipment of unseparated MSW^

Category transportation Source-to-site shipment of source separated MSW

(National paper market)^

Category 6 transportation Source-to-site shipment of source separated MSW

(Local paper market)^

Category oO transportation Source-to-site shipment of preseparated paper

(Local paper market)

Category 9 transportation Source-to-market shipment of preseparated paper

(Local paper market)

Category 11 transportation Source-to-site shipment of source separated MSW

(National paper market)^

Category 12 transportation Source-to-site shipment of source separated MSW

(National paper market)*^

C*\ja.L.KZ^\j af" ^cfmrvL y 13 transportation

v.iNaLionaj. paper marKeu^

Category 14 transportation Source— to— site shipment of source separated MSW

(National paper market)'^

Category 15 transportation Source-to-site shipment of source separated MSW

(National paper market)"

Residue Site at which source separation residue may

arrive

Paper Site at which source separated paper may arrive

Processing Linear segment of a process at a given site

Artificial processing Site®

activities

NZ Problem formulation^

NZX Scenario evaluation^

^Assumes KMIX = 1 on card 3 and IMIXSU = 1 for the source under consideration.

^Assumes KSEPO = 1 on card 3 and ISEPSU = 1 or 2 for the source under

consideration.

^Assumes KSEPO = 2 on card 3 and ISEPSU = 1 or 2 for the source under

consideration,

'^Assumes KSEPO = 1 on card 3 and ITRIGS(K) = 1 for the source under

consideration.

The value of K is equal to the transportation category minus ten.

®See note a on table 5.1.

^Relieves infeasibilities associated with an advanced starting point.

SRelieves infeasibilities when performing a scenario evaluation. Is not to be

counted unless LOPT = 4.

142
The case example presented in chapter 3 will be used in order to illustrate

how the sizing calculation would be performed in practice. Reference will be

made to exhibit 3.14, the case study file for the example, in performing the

calculation. Since there are four sources, there are four source balance

equations which insures that each source's wastes enter into transportation.

The value of KSEPO on card 3 was coded as 1, indicating a national paper

market, so there are no source paper balance equations. Two of the four sites were coded as MSW receiving, which implies they can also receive source separation residue from those sources which consider unconditional or conditional source separation. Thus, there are two site input residue balance

equations. The two MSW-receiving sites can also receive source separated

paper from those sources which consider conditional source separation, so there are two site input paper balance equations. There are two site input

balance equations for MSW for the two sites listed as MSW receiving. The two

sites which receive process residue each have a site input balance equation

for non-MSW commodities. Two of the four sites offer processes which produce

output commodities. For example, the water wall incinerator at Saugus

produces steam, incinerated ferrous and process residue. The two processes at

the Worcester site produce MSW (through the transfer station) and steam,

incinerated ferrous and process residue (through the water wall incinerator).

This implies a need for seven site output balance equations. There are three

market input balance equations, one for each market. The only site listed as capacitated (ICAP = 1 for the subject site read on KEY 12) was Saugus, so there is a single site capacity constraint. The two landfills each account

for a land capacity constraint. Altogether the two markets for steam had four

bounds (GE Lynn: 900 and 1800; Worcester: 2000 and 4000) which translates

into four constraints. The final row, which is always included, is the

constraint on artifical processing activities at each of the sites. The

results of the row tabulations are summarized in table 5.3, where it can be seen that the A matrix defined by the data in the ACASE file contains 30 rows

,

In performing the column calculation, it becomes necessary to recognize the

relationship between the run control variables (KMIX, KSEPO, NTRIG) , the source control variables (IMIXSU, ISEPSU, ITRIGS(K)) and the linkages defined

on the transportation category 1 cards read on KEY 22. Hiese controls govern

the expansion of the transportation category 1 data. Turning to exhibit 3.14,

it can be seen that there are five category 2 transportation activities (line

numbers 77 through 81) and four category 3 transportation activities (line

numbers 82 through 85), respectively. The data on each source was coded onto

lines 23 through 26. A review of this data shows that only one source (Marblehead) considered the shipment of mixed MSW. Turning now to the

transportation category 1 cards for Marblehead (line numbers 71 and 72), it can be seen that this source ships its waste to either Saugus or Worcester.

This implies a need for two category 4 transportation activities. An analysis

of the values coded into ISEPSU and ITRIGS(K) shows that Marblehead considered

both unconditional MSW and source separation. Revere considered unconditional

143
Table 5.3 Calculating the Number of Rows in the A Matrix for the Example

Presented in Chapter 3

Constraint Type Quantity

Source balance equations 4

Source paper balance equations 0

Site input residue balance equations 2

Site input paper balance equations 2

Site input balance equations for MSW 2

Site input balance equations for other cpmmodities 2

Site output balance equations 7 Market input balance equations 3

Site capacity constraints 1 Land capacity constraints 2

Market bounds 4

Constraint on artificial processing 1

TOTAL 30

Table 5.4 Calculating the Number of Columns in the A Matrix for the

Example Presented in Chapter 3

Activity type Quantity

Category 2 transportation 5

Category 3 transportation 4

Category 4 transportation 2

Category 5 transportation 8

Category 6 transportation 0

Category 8 transportation 0

Category 9 transportation 0

Category 11 transportation 4

Category 12 transportation 4

Category 13 transportation 4

Category 14 transportation 0

Category 15 transportation 0

Residue 2

Paper 2

Processing 9

Marketing 5

Artificial processing 4

NZ 1 NZX 0

TOTAL 54

144
source separation only, and Lynn and Salem considered both unconditional and

conditional source separation. All sources are therefore candidates for

category 5 transportation activities. Through reference to line numbers 69

through 76 in the ACASE file, it can be seen that there are two category 5 activities for each source, resulting in a total of eight. Since this is a national paper market (KSEPO = 1), there are no category 6, 8 or 9

transportation activities. The fact that there are three trigger prices

(NTRIG = 3), implies that Lynn and Salem can each have transportation

categories 11, 12 and 13. There are no transportation category 14 or 15

activities permitted in this run due to the setting of NTRIG. (To have a category 14 transportation activity NTRIG would have to be either 4 or 5; to

have a category 15 transportation activity NTRIG would have to be 5.) Lines

69 and 70 and 73 and 74 show that each source can ship to two sites. This

implies that each source has two activities for each transportation category

(i.e., four for category 11, four for category 12, four for category 13).

From the previous discussion we know that two sites are MSW receiving so they can also receive source separation residue and source separated paper. Thus

there are two residue activities and two paper activities. The SIPROC cards

(lines 54 through 58) provide all the information necessary to calculate the

number of processing activities. The number of processing activities is

simply the number of times a 1 appears in each of the ISPSEG(K) columns.

Counting up these cases for the example indicates that nine activities are needed. The marketing activities may be calculated by summing the number of

segments (i.e., the value of NSGMK declared on the first market card) across

all markets. In this case, there are five marketing activities. Since there are four sites, there are four artificial processing activities used as a control on artifically low processing activities for each site. The A tiacrix

also contains activity NZ as a means of relieving any infeasibilities

associated with an advanced starting basis. There is no NZX activity in this

example because LOPT is not equal to 4. The results of the column tabulation

are summarized in table 5.4, where it can be seen that the A matrix defined by

the data in the ACASE file contains 54 columns.

Since the model limits the size of the problem to 90 rows by 360 columns, it

is obvious that much larger case studies than defined in the ACASE file can be

easily solved. The formula outlined in tables 5.1 through 5.4 should permit users to determine the impact of problem size on their case study. More

precisely, if the number of rows or columns is large and computational cost is

of importance, trimming down the problem can now be done in an objective

manner. First, it should be recognized that reducing the number of rows (constraints) will result in the greatest reduction in computational costs.

Second, the three factors which have the greatest impact on the row dimension

are: (1) the number of sources; (2) the source separation options; and

(3) the output commodities. Thus, any decision to combine sources or model a market via the implicit revenue figure on the SIPROC card may translate into a substantial savings in computer costs. Third, the source separation options

145
and the processing activities have the greatest impact on the column

dimension. Thus, any decision to combine sources or preclude source separation should be column conserving as well as row conserving.

Whenever large amounts of data are collected, collated and input, it is almost

inevitable that some errors in recording or entering the data will occur. In

order to address these problems, RRPLAN has an elaborate system for editchecking the values of key input variables. If an error is encountered, then

a message is printed out to the user which should help to locate and correct

the error. There are three basic types of error messages: (1) general

errors; (2) linkage errors; and (3) process-related errors. General errors

are the easiest to find and correct. They are associated with input values

which are out of range, cards out of sequence, or sums of values that are out

of tolerance. They are likely to be due to typographical errors (e.g.,

incorrect column alignment) or leaving out (or duplicating) a card. The

diagnostics produced, the data output and remedies for such errors are summarized in table 5.5.

Linkage errors occur whenever the transportation network is incorrectly

specified. These errors are more subtle than general errors. Hence these errors require greater care in locating and correcting. There are nine types

of linkage errors , all but two of which produce output data as aids in

troubleshooting. The two linkage errors which do not produce output data are:

(1) more than 300 transportation activities; or (2) the lack of a market for source separated paper. The first error should not occur if attention is paid

to the sizing issue outlined in tables 5.1 through 5.4. The second error can only occur when KSEPO is coded equal to 2^ on the third card indicating a local

paper market. In the event that the region is served by a local paper market,

it will be necessary to define a market which receives source separated paper

(commodity category number 3) as its input. Distances from all sources from

which pre-separated paper could come should then be recorded in the two

distance files. The seven other linkage errors all provide output which

should enable the user to locate the file in which the problem is occurring

(e.g., ACASE, APKR, or AVAN) , The errors can then be located and corrected by

following the prescriptions given in table 5.6. Once the error is corrected,

the user should perform one additional check. If the error involved one of

the cards read on KEY 22, then each of the two distance files should be

examined to determine if the linkage defined in the case study file has a distance recorded in each of the distance files.

Process-related errors are due to inconsistencies between two groups of input

cards (e.g., sites and processes). For example, if a site listed its input

commodity as MSW and a process which received a non-MSW commodity was to be

located at the site, an error would result. This is because each site is

coded as receiving only one commodity. Methods for finding and correcting the errors are summarized in table 5.7

146
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149
As an aid in building skills at troubleshooting, readers are encouraged to

experiment with the example from chapter 3. A useful strategy might be as follows. First, run the model with all data as shown in chapter 3. Second,

make several copies of each of the files for use in subsequent runs. Third,

create an error in one of the files (e.g., delete a line, duplicate a line,

change the column alignment on a card, exceed the maximum or minimum value,

etc.). Fourth, run the model and examine the diagnostics. Fifth, determine

what steps you would take to fix the problem based only on the information

provided. Would these steps correct the problem? If the answer is no, then

it will be necessary to study the relationship between the error you created

and the diagnostics produced by the program to determine the source of the

difficulty.

5.2 CONTROLLING THE STRUCTURE OF THE RUN

Once a baseline case has been established, it becomes necessary for the user

to impose a structure on the overall study. The issues dealing with problem

structure addressed in this section relate to: (a) the way linkages are specified in a scenario evaluation or for locking in certain sources, sites, or markets; (b) cost accounting for multi commodity SIPROCs _or when more than

three cost categories are required for a process; (c) modeling a process where

decreasing returns to scale have set in; (d) the treatment of large or unusually complex problems; and (e) techniques for performing a sensitivity

analysis

.

In performing an analysis with lock ins, it is important to recognize that

optimizing runs are based on settings of LOPT between 0 and 3. In all cases,

the user should be generous with alternative linkages, sites, and processes to assure that the solution has not been predetermined.

The model will perform a scenario evaluation (check feasibility, generate

costs and net energies implied by the plan by category and total, and resolve

any alternatives given to it) by running the model with LOPT =4. A scenario

is a specified plan, input with alternatives eliminated, or at least seriously

reduced, and coded LOPT =4. If a processing or land capacity or market limit

is exceeded, that fact will be noted, together with the percentage of capacity

required for that element.

A scenario evaluation can be accomplished through a single run of the model.

However, it is useful to compare a feasible specified plan against a modelgenerated preferred plan. This can be accomplished by running the same scenario in an optimizing mode, but with more alternative sites, processes,

and linkages. For this purpose, LOPT = 0 (minimum discounted cost) is

recommended as the most appropriate base for comparison.

150
Difficulties in cost accounting may result due either to multicommodity

SIPROCs or when more than three cost categories are required for a process.

The same technique may be used to address both problems; it will be

illustrated through reference to the issue of cofiring sludge. It is possible

to study MSW and sludge in the same run by providing no overlap of linkage.

Thus, some source, process, and site numbers can represent MSW and others

sludge.

If the cost of a process receiving a mix of MSW and sludge can be represented

by a linear function of MSW and sludge inputs, it becomes possible to offer a site for cofiring MSW and sludge within a single optimizing run. The cofiring

site requires the use of three sites, processes, and site-processes, all at

the same location. One set is to receive MSW, one set is to receive sludge,

and the third is to receive an artificial commodity generated by each of the

other two. The user can represent the linear cost function, and the various

recoverables generated, and energy impacts, through the cost and energy inputs

of the three processes, and through the three sets of output coefficients.

In the more likely event that the cost and energy impacts are non-linear

functions of the inputs of MSW and sludge, as for example if a fixed mix of

the two is required, a different procedure is required. The user can lock in

any source to a site by providing only one link from the source — that to the

site of the lock-in. By locking in one or more MSW sources and one or more sludge sources to a single cofiring site, the user can control the mix, and can put in a cost function appropriate to that mix. The remainder of the run can be optimized. By varying the "lock-in" across a sequence of runs, the user can determine the preferred solution among the cofiring plans tested, and can compare that to a solution with no cofiring at all. In this procedure, it

is useful to the user that each case, representing a lock-in, is optimized

except for the lock-in itself.

The case of non-linear cost/energy functions also illustrates one way to

handle the problem of decreasing returns to scale. If decreasing returns to

scale have set in, then the user can lock-in certain sources and sites to

control the activity level at a SIPROC and input a cost function appropriate

to that level. Such a cost function will most likely consist of a single

segment with the fixed and variable cost components calibrated for the target

activity level. New sources and/or sites can then be locked in or uncoupled

in a sequence of runs to determine the preferred solution. As in the cofiring

example, the remainder of the run should be optimized.

Another approach to the decreasing returns to scale case which can be fully

otpimized is to define three levels of site-processes, initial, intermediate,

and final, at the same location. The full normal increasing-returns-to-scale

cost functions are entered for a single final-level site-process,

uncapacitated, representing the total cost function up to the point of

decreasing returns. Incoming transportation links arrive at a single initiallevel site-process which is costless and uncapacitated. The initial-level

site-process links to each intermediate-level site-process, which in turn

links to the final-level site-process. The first intermediate-level siteprocess is costless, but is capacitated to the point of decreasing returns.

151
The second intermediate-level site-process has a slope cost equal to the

difference between the slope cost after decreasing returns and the lastsegment slope cost of the final-level site-process. If the cost after

decreasing returns turns upward again, say at scale S, the cost curve can be

followed by capacitating the second intermediate-level site-process by the

difference between scale S and the point of decreasing returns, and by using a slope cost for the third intermediate site-process equal to the difference

between the slope cost after scale S and the last-segment slope cost of the

final site-process. As many intermediate-level site-processes may be added as are necessary to follow the cost curve.

The model is designed to study a large range of geography, on the one hand,

and detail, on the other. Balancing these is the art of designing a good

application. For large geography, gross detail runs, perhaps at state-level,

source separation alternatives should be avoided, since they are expensive in

matrix size. (A logical region size for a gross detail run would appear to be

that defined by a standard metropolitan statistical area (SMSA), since it

explicitly includes for economic transactions within the region.) Most of the

available matrix size should be used to define sources, and most of the

remainder to define sites. One linear segment, or two at most, should be

entered on the SIPROC cards (KEY 17) since small scales are unlikely. The

dedicated transfer station function will prove most useful for such large

geography runs since the selection of a dedicated transfer station has _no impact on the size of the matrix at all. Such runs are most useful for

defining sub-regions which become candidates in turn for optimizatin using the

model at a finer level of detail. In optimizing each sub-region, the

geography of the sub-region should be extended so that there is an overlap

zone at each regional interface. For these runs, source-separation options

are useful, but the dedicated transfer station function becomes less useful,

and may be locked out (set IXDED to 1 on the first card). Sources should be

defined at a finer level of detail, and a full offering of linear segments on the SIPROC cards should be considered.

Sensitivity analysis is a widely used technique for answering many what-if

type questions. If the analysis is done carefully, it may be possible to

decompose the plan in such a way that statistical relationships can be

estimated. Ideally, these relationships would show for a given change in one or more of the inputs how some variable of interest would be affected. Those

readers who wish to attempt such an analysis of the output are encouraged to

first carefully postulate from two to six input variables which are of

interest and their likely ranges. Values from the range can then be selected

in a coordinated manner to develop a basis for the subsequent statistical

analysis. It is recommended that users familarize themselves with some elementary topics in the literature on the planning and analysis of

comparative experiments prior to atempting such an endeavor.

An excellent text on the subject is: W.G. Cochran and G.M. Cox, Experimental

Designs

,

(New York: John Wiley and Sons, 1957).

152
In most cases, two techniques for testing the sensitivity of the solution to

changes in input values through comparison of two or three coordinated runs should be sufficient. First, by running a pair of cases, identical except for

the input of interest, the user can measure the full cost and energy impacts

of the change in the input of interest by calculating cost and energy

differences between the runs. This procedure referred to as the optimization

of an input assumes that the information is available in time to optimize to

the setting of the input.

Second, running a case for a setting of an input the user may generate a straw-man optimal plan for that setting. Then, by using the scenario

evaluation capability of the model (LOPT = 4) the user can measure the cost

and energy impact of using the straw-man optimal plan in the face of a different setting of the input of interset. A third run, optimizing to the

second setting of the input will provide a basis for the calculation of the

cost of optimizing to the wrong setting of the input (cost and energy

differences between the second and third runs). This procedure is referred to as the cross optimization of an input.

A what-if question is evaluated through a pair-wise comparison of runs,

identical in all respects except for the input representing the what-if

question. The cost and energy differences between the runs represent the full

impacts of the change in the input, with each case being optimized. These

differences are thus a full answer to the what-if question. The what-if

question technique can be used to evaluate site issues, transportation issues, source separation issues, market issues, MSW supply, public versus private

ownership, as well as planning over time. Each issue will be addressed

briefly.

What if a key controversial site were not available? The model is run with

and without the site. If the site is not used when offered in the first run,

the second run need not be executed. The cost differences between the runs will measure how much it is worth to fight through the political difficulties

of the site. By clarifying the economic value of using the site, the analysis

might help resolve the political issue.

What if a route is not available for packers? The change in input is a longer distance in the packer distance file for one or more location pairs.

What if a source separation alternative were made available at one or more

sources? A run can be performed with the alternative offered. If accepted at

any source, a comparison run can be executed with the alternative deleted.

A pair of runs can be executed to test either alternative market values or alternative price distributions. Since these values are subject to great

uncertainty, this is an issue which would lend itself well to the

cross-optimization technique.

153
Alternative assxamptions on future MSW supply can be tested by either the

optimization to input technique or the cross-optimization technique.

The public versus private ownership issue is represented by differentiated

cost functions and by differentiated financial assumptions (interest rate for

capitalization and years for capitalization) . This is an example of a what-if

question that requires the change of several inputs between the comparison

pair.

RRPLAN's predecessor, WRAP, was designed with a dynamic mode through which it

could generate optimal plans through time in up to four discrete time periods.

That capability proved very costly in matrix size, however, so that it could

be used only for extremely small sized problems (small in number of sources,

sites, processes, linkages, etc.).

A different approach has been used in the design of RRPLAN which permits

dynamic planning with no sacrifice of source, site, process, or linkage

availability. A specific capability for treating existing plants was built

into the costing model of RRPLAN through the use of a remaining life, which is

input for each captial cost category. Through this capability RRPLAN can generate djmamic plans through an overlapping comparative static approach.

Each succeeding run would include among its inputs, as existing facilities,

all site-processes generated in the previous run, with remaining lifetimes

being the remaining life of the previous run minus the time interval between

the first years of this and the previous run. Each run should use market

prices and costs that are average expected estimates over the time interval

from the first year of this run to the first year of the following run. Source tonnages should be the annual tonnage expected over that same interval,

but the maximum rather than the average over the period, since it will be

necessary to allow for growth of tonnage until the next planning period,

signaled by the starting period of the following run. The time interval need

not be constant among the various runs in the series. It would be useful to

set the first year of a run to coincide with the end of life of a major

processing facility.

In order to run the model for an overlapping set of time periods, it is

necessary to be able to:

1. Step the first year of the run (IFIRST on card 01) forward through

time.

2. Enter as existing sites:

-all sites that were existing at the start of the set of runs -all sites selected in prior runs of the set

providing such sites have not completed their useful lives.

154
To step the first year of the run forward to the first year of the current

run:

1. Adjust all costs by multiplying by:

(1 + .OlDIFLAT(J))T-t

where DIFLAT(J) is the differential inflation rate for cost category J,

T is the first year of the current run, and

t is the first year of the run set.

2. Enter year T as IFIRST.

3. Enter the number of years for this run as NYR on card 01.

To enter existing sites :

1. Code the process "existing" by setting lEXIS(I) to 1 for the Ith site

read on KEY 14.

2. Code the number of processes assigned to the site to 01 (NPROSI(K) on

the site card read on KEY 12). (An existing process must be the only

process offered at its site.)

3. For capital cost categories corresponding to the site, enter as NREM(J) , the years of life remaining for the corresponding capital

item. The first estimate of NREM(J) is:

NREM(J) = LIFU(J) - (T -tb)

where LIFU(J) is the useful life of the cost category and tb is the

year the plant was built.

If NREM(J) is negative we should assume that the capital will have been ' replaced at the end of its useful life, and add LIFU(J) to NREM(J) to simulate

the years remaining in the second life cycle. This process may be repeated if

NREM(J) is still negative.

The costs of overlapping stages cannot be combined in any meaningful way

because of the double-counting of overlapped years. If the user wishes to

obtain costs for the total period consistent with the decisions reached in the

overlapped stages, the stages may be run again, except without overlap, as follows

:

1. NYR should be the number of years to the next stage (i.e., no overlap)

.

2. The plan determined by the corresponding overlapped stage should be

entered as a specified plan with LOPT = 4 and with alternatives

removed

.

155
3. Cost and remaining life inputs should be entered as for the

overlapped stage.

4. After running the model for all stages, all discounted costs should

be rediscounted to year t, the first year of the first stage:

Ct = C(l +.01DISC0)t-T

where DISCO is the discount rate as a percent,

C is the discounted cost output by the model, discounted to

year T, and

C^ is the corresponding rediscounted cost, discounted to

year t where T and t are as previously defined.

In this form, all lifetime discounted costs may be aggregated, by total and by

cost category and cost summation category. Lifetime undiscounted costs may be

aggregated without any corresponding adjustment.

5. Costs per year and costs per ton should be interpreted as average

costs per year and per ton over the years of the stage. This applies

also for net energy.

6. Lifetime energy may be aggregated following the rules of undiscounted

cost

.

In using RRPLAN, it is crucial that the results of each run are carefully

critiqued. Each solution, or set of solutions, should be viewed critically; users should attempt to probe into their results, looking beyond the numbers

for implications. At a minimum, the following three questions should be

considered. Are the answers meaningful? Are all implicit objectives

satisfied? Have new problems (or opportunities) been identified? If one carefully plans the analysis, he should both be able to identify areas where

the biggest payoffs are likely to occur as well as gain intuition on what will

happen if one of the basic assumptions changes.

156
APPENDIX A GLOSSARY OF INPUT VARIABLES

This appendix is designed to give the model user an in-depth summary of all

input terms. The description goes beyond that given in chapter 3 because

array dimensions are defined and, where applicable, references are made to the

effects caused by variable settings in other parts of the input deck. The

glossary is arranged in alphabetical order by variable name. In all but a few

cases, a mnemonic, which should help the user associate the variable with its

purpose, is given. The key letters in the related name are capitalized and

underlined. The specification declared in the FORMAT statement through which

the variable is read in is then given. The read KEY, card type, and first

column on the card available for this variable follow immediately. If the

variable has a range, the range is given. Each variable is then cross referenced to a worksheet and a specific question. The variable is then

defined; and additional information is also noted at that time.

With regard to the worksheet-question responses in the glossary, there are three cases which serve as exceptions. The first relates to the location

number. The question on the worksheet refers to the location as a street

address or intersection. Once all worksheets have been filled out, each

address or intersection is assigned a number. It is this number which is used

for all distance calculations. If the term Note A is used, it is an indication that the user must assign the location number and construct the

distance table from the data on the worksheets. The second exception relates

to the end points of do loops. This case is designated by Note B. Variables

falling into this category correspond to counts on the number of times a particular worksheet was filled out. Each of the variables in this category

also serve to define an end point for a sequence of sources, sites, markets,

etc. Therefore, when the worksheets are counted, they should also be assigned

a sequence number. The third exception relates to run controls addressed in

chapter 5. This case is designated by Note C. Six other variables are also

grouped into this category. They are: the distance file format control, ISQR

(see section 3.3); the dedicated transfer station control, IXDED (see section

2.2); and the four fixed and variable cost drivers, IFDRV, IVDRV, FDRIV, VDRIV

(see section 5.1).

In the discussion which follows, it is sometimes necessary to refer to a specific element within an array. So that this may be done efficiently, a labeling convention has been adopted and is used throughout the appendix. If

the array is one-dimensional, the index is listed as K (i.e., ARRAY(K)). If

the array is two-dimensional, the indices are J and K, respectively (i.e.,

ARRAY(J,K)). If the array is three-dimensional, the indices are I, J and K,

respectively (i.e., ARRAY(I,J,K))

.

157
ALLO

AREAD(5)

Mnemonic! ALLOcation

Specification: F4.3 Key: 3 First Column: 73

Minimum: 0.0 Maximum: 1.0

Worksheet: I Question: 9.c

Definition: Allocation of paper processing cost to actual

paper processed as waste (ALLO) versus potential paper

processed as waste (1-ALLO).

Specification: F6.2

Worksheet : Note A

Definition: The Kth distance on the card read in either the

packer or van distance file.

ATRIG(5) Mnemonic: Average TRIGger price

Specification: F5.2 Key: 6 First Column: 52

Minimum: ATRIG(K-l) + 0.0001 (ATRIG(K) ) K>1

Worksheet: II Question: 4.

a

Definition: Average price of paper sold in the market for

the Kth trigger price.

AVLA(30)

First Column: 29

Mnemonic: Available LAnd

Specification: F10.3 Key: 13

Minimum: 0.001

Worksheet: VIII Question: 3

Definition: Land available at the Kth landfill.

BDMKA(5,30) Mnemonic: BounD MArKet

Specification: F7.2 Key: 19 First Column: 3

Minimum: BDMKA(J-1,K) + 0.0001 (BDMKA( J ,K)

)

Worksheet: XI Question: 2.

a

Definition: Bound for the Jth segment of the Kth market.

BOTTOM

BTMSW

Mnemonic: BOTTOM price

Specification: F5.2

Worksheet: II Question: 2.

a

Definition: Lower limit of first (lowest price) interval in

dollars per ton in the paper market price distribution.

Mnemonic: BTu content of MSW

Specification: F10.5 Key: 5

Minimum: 0.05

Worksheet: I Question: 5.

a

Definition: Millions of BTUs per ton of MSW.

First Column: 39

158
BTPAP

First Column: 29

Mnemonic: BTu content of PAPer

Specification: F6.3 Key: 3

Minimum : 0.0

Worksheet: I Question: 5.b

Definition: Millions of BTUs per ton of source separated

paper when processed as solid waste.

BTRES Mnemonic: BTu content of source separation RESidue

Specification: F6.3 Key: 3 First Column: 23

Minimum : 0.0

Worksheet: I Question: 5.c

Definition: Millions of BTUs per ton of source separation

residue.

BTRIG(5) Mnemonic: percent Burn by TRIGger price

Specification: F4.1 Key: 6 First Column: 32

Minimum: BTRIG(K-l) + 0.0001 (BTRIG(K) ) K>1

Worksheet: II Question: 4.b

Definition: Percent of source separated paper burned for

the Kth trigger price.

CAPSP(80) Mnemonic: CAPacity for a ^ite-Process

Specification: F9.3 Key: 17 First Column: 12

Minimum: 0.0001 if the site is coded as capacitated

Worksheet: X Question: 3

Definition: Capacity the process at the site under

consideration assuming the process is the only one at that

site.

CCOCC(2,20)

CCOEC(2,20)

Mnemonic: haul Cost-COmmodity-Cost Category

Specification: F8.3 Key: 10 First Column: 30

Minimum: 0.00001 if J=l Maximum: 1000.0

Worksheet: V Question: 2. Definition: The Jth haul cost factor for the Kth commodity

category (CCOCC(J,K))

.

Mnemonic: haul Cos t-COmmodit y-Energy ^ategory

Specification: F8.3 Key: 10 First Column: 50

Worksheet: V Question: 3

Definition: The Jth haul cost factor for the Kth energy

commodity (CC0EC(J,K))

.

CCPREP(50) Mnemonic: C^onstant ^ost for ^ite PREParation

Specification: F10.3 Key: 12 First Column: 44

Worksheet: VII Question: 5.b

Definition: Site preparation cost for the Kth site in

thousands of dollars.

159
CEPREP(50)

CFMKA(5,30)

Mnemonic: Constant Energy for site PREParatlon

Specification: F10.3 Key: 12 First Column: 56

Worksheet: VII Question: 5.d

Definition: Site preparation energy requirement for the Kth

site in full energy units.

Mnemonic: CoeFficient MarKet A

Specification: F7.3 Key: 20 First Column: 3

Minimum: 0.0 Maximum: CFMKA(J-1,K)

Worksheet: XI Question: 4.d

Definition: Jth revenue coefficient for the Kth market

(slope of the total revenue function for the Jth segment)

for the first cost category.

CFMKB(5,30) Mnemonic: CoeF^ficient MarKet _B Specification: F7.3 Key: 20 First Column: 38

Minimum: 0.0 Maximum: CFMKB(J-1,K)

Worksheet: XI Question: A.d

Definition: Jth revenue coefficient for the Kth market

(slope of the total revenue function for the Jth segment)

for the second cost category.

CHCA(60)

First Column: 64

Mnemonic: ^ost for Hauling CAns

Specification: F5.2 Key: 11

Worksheet: VI Question: 6.e

Definition: Cost per ton to haul cans from source K to the

market

.

CHGL(60)

First Column: 59

Mnemonic: ^ost for Hauling GLass

Specification: F5.2 Key: 11

Worksheet: VI Question: 6.d

Definition: Cost per ton to haul glass from source K to the

market

.

CHPA(60)

First Column: 54

Mnemonic: Cost for _Hauling PAper

Specification: F5.2 Key: 11 Worksheet: VI Question: 6.c

Definition: Cost per ton to haul paper from source K to the

market

.

CINT(30,3,2)

First Column: 8

Mnemonic: jCost INTercept

Specification: F9.2 Key: 16

Worksheet: IX Question: 6.c

Definition: Intercept (fixed charge) cost in thousands of

dollars for the Ith process, Jth segment, Kth cost type.

160
COF(7,30)

CSLO(30,3,2)

First Column: 10

Mnemonic: output COeFf Iclent

Specification: F8.5 Key: 15

Worksheet: IX Question: 5 Definition: The Jth output coefficient (output per unit of

incoming commodity) for the Kth process

.

First Column: 44

Mnemonic: C^ost SLOpe Specification: F9.6 Key: 16

Worksheet: IX Question: 6.c

Definition: Slope (variable cost in dollars per ton) for

the Ith process, Jth segment, Kth cost type.

CSPA Mnemonic: ^ost ^eP^aration A

Specification: F5.3 Key: 3 First Column: 49

Minimum: 0.0 Maximum: 1000.0

Worksheet: I Question: 8.

a

Definition: Total cost of collection in dollars per ton of

unseparated MSW.

CSPB Mnemonic: ^ost ^eJParation B

Specification: F5.3 Key: 3 First Column:

Minimum: 0.0 Maximum: 1000.0

Worksheet: I Question: S.b

Definition: Incremental cost of source separation in

dollars per preseparated ton.

56

DIFLAT(50)

First Column: 39

Mnemonic: Differential inFLATion

Specification: F5,3 Key: 8

Worksheet: III Question: 4

Definition: Differential annual inflation rate in percent

for the Kth cost category.

DISCO Mnemonic: DISCOunt rate

Specification: F8,4 Key: 1 First Column: 19

Minimum: 0.0 Maximum: 50.0

Worksheet: I Question: 2 Definition: Annual discount rate in percent.

ECANM Mnemonic: Energy value of CANs at the Market

Specification: F10.5 Key: 5 First Column: 27

Worksheet: I Question: 10.

c

Definition: Energy value of cans per ton in the market when

delivered there.

ECMK(2,30) Mnemonic: Energy Coefficient MarKet

Specification: F10.5 Key: 18 First Column: 55

Worksheet: XI Question: 5

Definition: Jth energy market value per ton when marketed

in the Kth market.

161
EGLAM Mnemonic: Energy value of GLAs s at the Market

Specification: F10.5 Key: 5 First Column: 15

Worksheet: I Question: 10.

b

Definition: Energy value of glass per ton in the market

when delivered there.

EINT(30,3,2) Mnemonic: Energy INTercept

Specification: F9.2 Key: 16 First Column: 26

Worksheet: IX Question: 7.b

Definition: Intercept energy in full energy units for the

Ith process, Jth segment, Kth energy type.

EPAPM Mnemonic: Energy value of PAPer at the Market

Specification: F10.5 Key: 5 First Column: 3

Worksheet: I Question: 10.

a

Definition: Energy value of paper per ton in the market

when delivered there.

EREVSP(80) Mnemonic: Energy REVenue _Site-Process

Specification: F10.5 Key: 17 First Column: 41

Worksheet: X Question: 5.d

Definition: Energy value of recoverables not entered into

the commodity file (implicit revenues) net of energy

requirement of haul to the market. In the back end,

EREVSP(K) is redefined as the total implicit revenue per

average year for the Kth SIPROC in undiscounted cost.

ESLO(30,3,2) Mnemonic: E^nergy SLQpe Specification: F9.6 Key: 16 First Column: 62

Worksheet: IX Question: 7.b

Definition: Energy slope for the Ith process, Jth segment,

Kth energy type.

ESPA Mnemonic: Energy _Se£aration A

Specification: F10.3 Key: 4 First Column: 6

Worksheet: I Question: 11.

a

Definition: Net energy requirement for collection of

unseparated waste per ton.

ESPB Mnemonic: Energy ^e£aration _B Specification: F10.3 Key: 4 First Column: 18

Worksheet: I Question: ll.b

Definition: Net incremental energy requirement for source separation per ton of preseparated waste.

FDRIV Mnemonic: Mxed and DRIVer Specification: F5.2 Key: 5 First Column: 53

Worksheet: Note C

Definition: Fixed cost value used for driving into the

solution an activity not in the solution when either site or site-process forcing is used.

162
FLATO

FREQ(200)

F8.4

Mnemonic: inFLATiOn rate

Key: 1 Maximum : 50 .

0

Question: 3

Specification: First Column: 31

Minimum: 0,0

Worksheet: I Definition: General annual inflation rate in percent.

First Column: 1 Mnemonic: FREQuency

Specification: F5.3

Worksheet: II Question: 3

Definition: The Kth frequency; frequency for the Kth

interval in the paper market price distribution function.

IBTU Mnemonic: ^Identity BTU

Specification: 12 Key: 5 First Column: 37

Worksheet: I Question: 13.d

Definition: Energy category for BTU differentiation among

MSW, paper and source separation residue.

ICANM

ICAP(50)

ICC(50)

Mnemonic: _Identity CANs Market

Specification: 12 Key: 5 First Column:

Worksheet: I Question: 13.

c

Definition: Energy category for cans in the market.

37

Ifoemonic: ^dentity CAPacity

Specification: II Key: 12 First Column: 37

Worksheet: VII Question: 6.

a

Definition: Capacity code for the Kth site: 0 if the site

is uncapacitated; 1 if the site is capacitated.

Mnemonic: ^dentity ^ost Category

Specification: 12 Key: 8

Worksheet: III Question:

First Column:

l.b

Definition:

category.

Identification number of the Kth cost

ICCCAN Mnemonic: ^dentity _Cost Category CANs Specification: 12 Key: 3 First Column: 47

Worksheet: I Question: 14.

c

Definition: Cost category number for cans in the market,

ICCGLA

ICCMK(2,30)

Mnemonic: Wentity ^ost Category GLAss Specification: 12 Key: 3 First Column: 45

Worksheet: I Question: 14.

b

Definition: Cost category number for glass in the market,

Mnemonic: JEdentity ^ost Category MarKet

Specification: 12 Key: 18 First Column: 46

Worksheet: XI Question: 4

Definition: Jth cost (i.e., revenue) for the Kth market.

163
ICCPAP Mnemonic: _Identity jCost C^ategory PAPer Specification: 12 Key: 3 First Column: 67

Worksheet: I Question: 14.

a

Definition: Cost category number for national paper market,

Not used for explicit paper markets under KSEPO = 2.

ICCS(10,10) Mnemonic: identity ^ost Category Summation

Specification: 12 Key: 21 First Column: 32

Worksheet: XII Question: 2

Definition: The identity of the Jth cost category in the

Kth cost summation.

ICO(20)

ICOCC(2,20)

ICOEC(2,20)

Mnemonic: Wentity Commodity

Specification: 12 Key: 10 First Column: 5

Worksheet: V Question: l.b

Definition: Identification number for the Kth commodity.

Mnemonic: jCdentity Commodity _Cost Category

Specification: 12 Key: 10 First Column: 28

Worksheet: V Question: 2 Definition: Cost category for the Jth haul cost factor for

the Kth commodity.

Mnemonic: ^Identity Commodity Energy _Category

Specification: 12 Key: 10 First Column: 48

Worksheet: V Question: 3

Definition: Energy category for the Jth energy level factor

for the Kth commodity.

ICOF(7,30)

First Column: 8

Mnemonic: identity COeFficient

Specification: 12 Key: 15

Worksheet: IX Question: 5 Definition: The commodity category number for the Jth

coefficient of the Kth process.

ICOMK(30) Mnemonic: _Identity Commodity MarKet

Specification: 12 Key: 18 First Column: 34

Worksheet: XI Question: 3

Definition: The commodity category number for the Kth

market

.

ICPR(30,2)

First Column: 44

Mnemonic: ^Identity jCost PRocess

Specification: 12 Key: 14

Worksheet: IX Question: 6

Definition: The Kth cost category for the Jth process.

ICPREP(50) Mnemonic: J[dentity ^ost site PREParation

Specification: 12 Key: 12 First Column: 42

Worksheet: VII Question: 5.

a

Definition: The cost category number for the Kth site's

preparation costs.

164
ICS(IO) Mnemonic: JLdentity ^ost Rumination

Specification: 12 Key: 21

Worksheet: XII Question:

First Column:

l.b

Definition:

category.

Identification number of the Kth cost summation

IDCC(50)

IEC(20)

IECMK(2,30)

IEPR(30,2)

IEPREP(50)

Mnemonic: IDentity code _Capital ^ost

Specification: 12 Key: 21 First Column: 5

Worksheet: III Question: 2 Definition: Cost type code: 0 is operating cost; 1 is

capital cost.

Mnemonic: ^Identity _Energy _Category

Specification: 12 Key: 9 First Column: 5 Worksheet: IV Question: l.b

Definition: The identification number of the Kth energy

category.

Mnemonic: identity Energy Category Market

Specification: 12 Key: 18 First Column: 38

Worksheet: XI Question: 5

Definition: The identification number of the Jth energy

category for the Kth market.

First Column: 48

Mnemonic: identity Rnergy PRocess

Specification: 12 Key: 14

Worksheet: IX Question: 7 Definition: The identification number of the Kth energy

category for the Jth process.

Mnemonic: _Identity _Energy PREParation

Specification: 12 Key: 12 First Column: 54

Worksheet: VII Question: 5.c

Definition: The energy category number for the Kth site's

preparation energy.

IEXIS(30)

First Column: 31

Mnemonic: Wentity Exist ence Specification: II Key: 14

Worksheet: IX Question: 3

Definition: Existing process code: 0 is new; 1 is

existing.

IFDRV Mnemonic: _Identity _Fixed cost DRiVer

Specification: II Key: 5 First Column: 52

Worksheet: Note C

Definition: Driver control for fixed costs. If 0, fixed

costs will be set to zero to "urge in" an activity in site

and site-process forcing; if 1, FDRlV will be used as the

fixed cost to "urge in" during site and site-process

forcing.

165
IFIRST Mnemonic: Wentity FIRST year

Specification: 14 Key: 1 First Column: 15

Worksheet: I Question: l.a

Definition: The identity of the first year in the run.

IGLAM Mnemonic: ^dentity GLAss Market

Specification: 12 Key: 5 First Column: 25

Worksheet: I Question: 13.

b

Definition: The energy category number for glass when

delivered to the market.

IHCSEP(60)

IHESEP(60)

ILACOF(30)

ILAND(50)

IMIXSU(60)

IMK(30)

Mnemonic: _Identity Eaul ^ost SEParation

Specification: 12 Key: 11 First Column: 50

Worksheet: VI Question: 6.

a

Definition: The cost category number for haul of paper,

glass and cans to the market for the Kth source.

Mnemonic: _Identity Haul Energy SEParation

Specification: 12 Key: 11 First Column: 52

Worksheet: VI Question: 6.b

Definition: The energy category number for haul of paper,

glass and cans to the market for the Kth source.

Mnemonic: _Identity code lAnd COeFf icient

Specification: II Key: 14 First Column: 34

Worksheet: IX Question: 9

Definition: Landfill coefficient code: 0 if process does

not use on-site landfill; 1 if it does.

Mnemonic: Identity code LAND

Specification: II Key: 12 First Column: 38

Worksheet: VII Question: 6.b

Definition: Landfill code: 0 if no landfill is available

at the Kth site; 1 if a landfill is available.

Mnemonic: Identity MIXed msw SolJrce

Specification: II Key: 11 First Column: 43

Worksheet: VI Question: 5.

a

Definition: Code for MSW option (no source separation) at source K: if 0, option is not offered; if 1 option is.

Itoemonic: Identity MarKet

Specification: 12 Key: 18 First Column: 5

Worksheet: XI Question: l.b

Definition: Identification number of Kth market.

INC0(50)

First Column: 34

Mnemonic: INput Commodity

Specification: 12 Key: 12

Worksheet: VII Question: 4

Definition: The commodity number of the input to the Kth

site.

166
INCOP(30)

First Column: 29

Mnemonic: INput Commodity process

Specification: 12 Key: 14

Worksheet: IX Question: 2

Definition: The commodity number of the Input to the Kth

process

.

INPAP

First Column: 66

Mnemonic: INput mode for PAPer Specification: II Key: 3

Worksheet: II Question: 4

Definition: Input mode for paper price distribution: if 0,

input trigger prices, percents of burn, and average prices;

if 1, input trigger prices and a paper market price

distribution function.

IPAPM

IPR(30)

IREAD(5)

Mnemonic: identity PAPer Market

Specification: 12 Key: 5 First Column: 13

Worksheet: I Question: 13.

a

Definition: The energy category for paper in the market.

Mnemonic: J_dentity PRocess

Specification: 12 Key: 14 First Column: 5

Worksheet: IX Question: l.b

Definition: The identification number of the Kth process

Mnemonic: identity READ

Specification: 13

Worksheet: Note A

Definition: The Kth minor location number on a card.

IREVSP(80) Mnemonic: identity REVenue ^ite-P^rocess

Specification: 12 Key: 17 First Column: 36

Worksheet: X Question: 5.

a

Definition: The cost category number for accumulating

implicit revenues associated with the Kth site-process

combination.

ISEPSU(60)

ISI(50)

Mnemonic: _Identlty SEParation ^olJrce

Specification: II Key: 11 First Column: 44

Worksheet: VI Question: 5.b

Definition: Code for unconditional source separation at source K: if 0, option is not offered; if 1 or 2, national

or local paper option as defined by KSEPO is offered.

Mnemonic: ^dentlty Site

Specification: 12 Key: 12 First Column: 5

Worksheet: VII Question: l.b

Definition: Identification number of the Kth site.

167
ISIREQ(50) Mnemonic: Mentity _SIte REQuest

Specification: II Key: 12 First Coliimn: 3

Worksheet: VII Question: 2 Definition: Site routing request code: if 0, routing not

requested; if greater than zero, ISIREQ(K) levels of routing

are requested for site K, if site K is in the solution.

ISPA Mnemonic: ^Identity _SeJParation A

Specification: 12 Key: 3 First Column: 54

Worksheet: I Question: 14.

d

Definition: Cost category number for total cost of

collection, CSPA,

ISPB Mnemonic: Identity _SeParation B

Specification: II Key: 3 First Column: 61

Worksheet: I Question: 14.

e

Definition: Cost category number for incremental cost of source separation, CSPB.

ISPPR(80) Mnemonic: Identity Site-Process, PRocess

Specification: 12 Key: 17 First Column: 9

Worksheet: X Question: 2.b

Definition: The process nximber of the Kth site-process

combination.

ISPSEG(80,3) Mnemonic: Identity Site-Process, SEGment

Specification: II Key: 17 First Column: 32

Worksheet: X Question: 4

Definition: Kth linear segment code for the Jth site- or process combination: if 0, the segment is not offered; if

1, the segment is offered.

ISPSI(80) Mnemonic: Identity Site-Process, Site

Specification: 12 Key: 17 First Column: 5

Worksheet: X Question: l.b

Definition: The site number of the Kth site-process

combination.

ISQR Mnemonic: Wentity SQuaRe

Specification: II Key: 1 First Column: 27

Worksheet: Note C

Definition: Distance file format control: if 0, model is

prepared for a triangular distance file; if 1, model is

prepared for a square distance file. The file is read major

record, minor record, distance. In a triangular file, the

major record is the higher numbered location and the minor

record is the lower numbered location. In a square file,

the major record is the destination location number and the

minor record is the origin location number.

168
ISU(60) Mnemonic: J[dentity _SoUrce

Specification: 12 Key: 11 First Column: 5

Worksheet: VI Question: l.b

Definition: The Identification number of the Kth source.

ISUREQ(60)

First Column:

Mnemonic: ^Identity _So^rce REQuest

Specification: II Key: 11

Worksheet: VI Question: 2

Definition: Source routing request code: if 0, routing is

not requested; if greater than zero, ISUREQ(K) levels of

routing are requested for source K, if source K is in the

solution.

ITRCO(600)

ITRIGS(5,60)

ITROG(600)

ITRSM(600)

ITTYP(600)

Jfaemonic: identity TRansportatlon Commodity

Specification: 12 Key: 22 First Column: 7 Worksheet: XIII

Definition: Transportation source/commodity code; Kth

transportation activity: if source-origin, categories

1,4,5,6,8,9,11-15, source number; if site-origin, categories

2 and 3, commodity number.

Mnemonic: identity TRIGger ^ource

Specification: II Key: 11 First Column: 45

Worksheet: VI Question: 5.c

Definition: Source separation option code for the Jth

trigger price at the Kth source: if 0, option is not

offered; if 1 option is offered.

Mnemonic: identity TRansportatlon ^rlG^in

Specification: 13 Key: 22 First Column: 10

Worksheet: XIII

Definition: The origin location number of the Kth

transportation activity.

Mnemonic: ^dentity TRansportatlon _Slte/Market

Specification: 12 Key: 22 First Column: 15

Worksheet: XIII

Definition: Offload identification of the Kth

transportation activity: if category 3 or 9, ITRSM(K) is

market number; otherwise ITRSM(K) is site number.

Mnemonic: JCdentity transportation TYPe

Specification: II Key: 22 First Column: 4

Worksheet: XIII

Definition: Category type code for the Kth transportation

activity: if 1, ITTYP(K) is source-to-site; if 2, ITTYP(K)

is site-to-site; if 3, ITTYP(K) is site-to-market.

169
IVDRV Mnemonic: _Identity _Variable cost DRlVer

Specification: II Key: 5 First Colvunn: 58

Worksheet: Note C

Definition: Driver control for variable cost: if 0,

internally set figure is used in "urging in"; if 1, VDRIV

is used.

IXDED bfciemonic: _Identity exclusion for DEDicated transfer

Specification: II Key: 1 First Column: 28

Worksheet: Note C

Definition: Dedicated transfer station control; if 0,

normal dedicated transfer station procedure operates; if 1,

dedicated transfer station procedure is suppressed.

KEMP Specification:

KEY(l): 15

KEY(2): 16

Def inition(l)

:

KEY(3): 19

KEY(4): 20

Definition(2)

:

12

First Column: 5 First Column: 5

Process number.

First Column: 75

First Column: 75

Market number.

KEY Specification: 12

Definition: Card type control: appears in columns 1 and 2 of all numbered cards.

KFOUT

First Column: 29

Mnemonic: Kontrol ^Forcing OUT

Specification: II Key: 1 Worksheet: Note C

Definition: Control for forcing out only in site and

site-process forcing: if 0 through 8, procedure includes

forcing in and forcing out; if 9, procedure uses forcing out

only.

KITLE Mnemonic: Kontrol tITLE

Specification: 19A4 Key: 2 First Column: 3

Definition: 76 alphanumeric digits for name of run.

KMIX Mnemonic: Kontrol MIX

Specification: II Key: 3 First Column: 63

Worksheet: I Question: 6.

a

Definition: Run control for availability of unconditional

MSW as an option: if 0, option is not available; if 1 it

is

.

KOPT

First Column:

Mnemonic: Kontrol OPTlon Specification: II Key: 1 Worksheet: Note C

Definition: Output control for the optimizer: 1 is full

output; 2 is matrix summary and final solution; 3 is matrix

summary and solution of phase 3 and 4, if phase 4 is

requested.

170
Mnemonic: Kontrol OVERride

Specification: 12 Key: 1 First Column: 5

Worksheet: Note C

Definition: Control for site and site-process forcing and

orverrlde of row/ column limit control for double-forcing, as in NFORC =2. If 0, a phase 3 or phase 4 solution will be

generated, according to the setting of LPHASE. If 1, site

forcing will be undertaken, LPHASE will be reset to 3. If

2, site-process forcing will be undertaken, LPHASE will be

reset to 3. If 2, site-process forcing will be undertaken,

LPHASE will be reset to 3. If 99, the NFORC = 2 matrix size

limit of 50 rows and 100 columns is overridden, to become 90 rows and 360 columns. Any other value of KOVER is reset to

0.

Itoemonic: Kategory REVenue _Site-P^rocess

Specification: 12 Key: 17 First Column: 39

Worksheet: X Question: 5.c

Definition: Energy category for EREVSP(K) , the net energy

value of any implicit revenues associated with the Kth

site-process combination.

Mnemonic: Kontrol SEParatiOn

Specification: II Key: 3 First Column: 64

Worksheet: I Question: 6.b

Definition: Run control for availability of unconditional

source separation: if 0, option is not available, if 1,

available with national paper market; if 2, available with

local paper market.

Mnemonic: Kode SEParation A

Specification: 12 Key: 4 First Column: 4

Worksheet: I Question: 13.

e

Definition: Energy category for collection of unseparated

waste

.

Mnemonic: Kode ^eP^aration B

Specification: 12 Key: 4 First Column: 16

Worksheet: I Question: 13.

f

Definition: Energy category for incremental energy

requirement due to source separation.

Mnemonic: Kontrol SUPPression

Specification: II Key: 1 First Column: 30

Worksheet: Note C

Definition: Control for suppression of intermediate

information on search for an initial feasible solution: if

0, information is suppressed; if 1, information is

provided.

171
LEMP

LIFC(50)

LIFU(50)

LITLE(20)

Specification: II Key: 16 First Column: 7

Worksheet: IX Question: 6.c

Definition: Segment number for the subject process.

Mnemonic: LIFe Capitalization

Specification: 13 Key: 8 First Column: 49

Worksheet: III Question: 5.b

Definition: Years for capitalization of the loan for the

Kth cost category.

Mnemonic: LIFe Useful

Specification: 13 Key: 8 First Column: 45

Worksheet: III Question: 5.

a

Definition: Years of useful life for the Kth cost

category.

Mnemonic: Ijlst tITLE for distances

Specification: 20A4

Worksheet: Note A

Definition: 80 alphanumeric digits available for naming the

distance files.

LLA(30)

First Column:

Mnemonic: I^ocatlon LAndf ill

Specification: 13 Key: 13

Worksheet: Note A

Definition: Location number of the Kth landfill.

LMK(30)

First Column:

Mnemonic: I^ocation MarKet

Specification: 13 Key: 18

Worksheet: Note A

Definition: Location number of the Kth market.

29

LOPT

First Column: 8

Mnemonic: JLoglc OPTlmization

Specification: II Key: 1 Worksheet: Note C

Definition: Optimization mode control: if 0, minimum

discounted cost; if 1, minimum undlscounted cost, If 2,

maximum net energy; if 3, minimum linear form; if 4,

feasibility test of a specified plan.

LPHASE Mnemonic: Last PHASE

Specification: II Key: 1 First Column:

Worksheet: Note C

Definition: Last phase control: if not 4, last phase Is 3

(optimization with fixed costs, but without forcing); if 4,

last phase is 4 (column forcing, in accordance with setting

of NFORC).

172
LSI(50) Mnemonic: ^Location Site

Specification: 13 Key: 12

Worksheet: Note A

Definition: Location number of the Kth site.

First Column: 29

LSTLO

First Column; 64

Mnemonic: LaST Location

Specification: 13 Key: 1 Worksheet: Note A

Definition: Last location in distance file. Edit checked

against location numbers in input deck, but not against

distance file.

LSU(60) Mnemonic: Location SoUrce

Specification: 13 Key: 11

Worksheet: Note A

Definition: Location number of Kth source.

First Colvimn: 29

NAMCC(50,5)

NAMCO(20,5)

NAMCS(10,5)

NAMEC(20,5)

NAMLA(30,5)

Mnemonic: " NAMe of ^ost Category

Specification: 5A4 Key: 8 First Column: 8

Worksheet: III Question: l.a

Definition: 20 alphanumeric digits for the name of the Jth

cost category.

Mnemonic: NAMe of Commodity

Specification: 5A4 Key: 10 First Column: 8

Worksheet: V Question: l.a

Definition: 20 alphanumeric digits for the name of the Jth

commodity.

Mnemonic: NAMe of ^ost _Summation

Specification: 5A4 Key: 21 First Column: 8

Worksheet: XII Question: l.a

Definition: 20 alphanumeric digits .for the name of the Jth

cost summation category.

Mnemonic: NAMe of Energy Category

Specification: 5A4 Key: 9 First Column: 8

Worksheet: IV Question: l.a

Definition: 20 alphanumeric digits for the name of the Jth

energy category.

Mnemonic: NAMe of LAndf ill

Specification: 5A4 Key: 13

Worksheet: VIII Question:

Definition:

landfill.

First Column: 8

l.a

20 alphanumeric digits for the name of the Jth

NAMMK(30,5) Mnemonic: NAMe of MarKet

Specification: 5A4 Key: 18

Worksheet: XI Question

Definition:

market

.

First Column: 8

l.a

20 alphanumeric digits for the name of the Jth

173
NAMPR(30,5) Mnemonic: NAMe of PRocess

Specification: 50A4 Key: 14 First Column: 8

Worksheet: IX Question: l.a

Definition: 20 alphanumeric digits for the name of the Jth

process

.

NAMSI(50,5) Mnemonic: NAMe of Site

Specification: 5A4 Key: 12 First Column: 8

Worksheet: VII Question: l.a

Definition: 20 alphanumeric digits for the name of the Jth

site.

NAMSU(60,5) Mnemonic: NAMe of _SoUrce

Specification: 5A4 Key: 11 First Column: 8

Worksheet: VI Question: l.a

Definition: 20 alphanumeric digits for the name of the Jth source.

NBDMK(30) Mnemonic: Number of _BounDs MarKet

Specification: II Key: 18 First Column: 54

Worksheet: XI Question: 4.

a

Definition: Number of bounds for the Kth market.

NCC Mnemonic: Number of ^ost C^ategories

Specification: 12 Key: 1 First Column: 43

Minimum: 1 Maximum: 50

Worksheet: Note B

Definition: Number of cost categories.

NC0F(30) Mnemonic: Number of Coefficients

Specification: II Key: 14 First Column: 32

Minimum: 0 Maximum: 7 Worksheet: IX Question: 5

Definition: Number of output ceofficients for the Kth

process

.

NCS Mnemonic: IJumber of Cost Summation categories

Specification: 12 Key: 1 First Column: 71

Minimum: 0 Maximum: 10

Worksheet: Note B

Definition: Number of cost summation categories.

NEC Mnemonic: Number of Energy Categories

Specification: 12 Key: 1 First Column: 46

Minimum: 0 Maximum: 20

Worksheet: Note B

Definition: Number of energy categories.

174
Mnemonic: j^umber of FORCe s Specification: II Key: 1 First Column: 4

Worksheet: Note C

Definition: Column force control. Operates only when

KOVER = 0 and LPHASE =4. If 1 , single column force; if 2,

double column force; if 0 or greater than 2, NFORC is reset

to 1.

Mnemonic: N^umber of LAndf ills

Specification: 12 Key: 1 Minimum: 0 Maximum: 30

Worksheet: Note B

Definition: Number of landfills.

Mnemonic: Number of MarKets

Specification: 12 Key: 1 Minimum: 0 Maximum: 30

Worksheet: Note B

Definition: Definition of markets.

Mnemonic: ^umber ^f INTervals

Specification: 13 Key: 7 Minimum: 1 Maximum: 200

Worksheet: II Question: 2.b

Definition: Number of intervals in the paper market price

distribution function.

Mnemonic: tJumber of PRocesses

Specification: 12 Key: 1 First Column: 55

Minimum: 2 Maximum: 30

Worksheet: Note B

Definition: Number of processes.

Mnemonic: Number of PROcesses at Site

Specification: 12 Key: 12 First Column: 39

Worksheet: X

Definition: The number of processes offered at the Kth

site.

Mnemonic: Number years REMaining

Specification: 12 Key: 8 First Column: 56

Worksheet: III Question: 5.d

Definition: Years of useful life remaining for existing

capital for the Kth cost category.

Mnemonic: Number of SEGments

Specification: II Key: 14 First Column: 33

Minimum: 1 Maximum: 3

Worksheet: IX

Definition: Number of linear segments in the Kth processes

cost/energy function.

First Column: 61

First Column: 68

First Column: 3

175
NSGMK(30) Mnemonic: Number _SeGments MarKet

Specification: II Key: 18 First Column: 53

Minimum: 1 Maximum: 5

Worksheet: XI Question: 4.d

Definition: Number of linear segments in the Kth market.

NSI Mnemonic: IJumber of Sites

Specification: 12 Key: 1 First Column: 52

Minimum: 1 Maximum: 50

Worksheet: Note B

Definition: Number of sites.

NSP Mnemonic: Number of _Site-Processes

Specification: 12 Key: 1 First Column: 58

Minimum: 1 Maximum: 80

Worksheet: Note B

Definition: Number of site-processes.

NSU Mnemonic: Number of ^ojJrces

Specification: 12 Key: 1 First Column: 49

Minimum: 1 Maximum: 60

Worksheet: Note B

Definition: Number of sources.

NTRIG

First Column:

Mnemonic: Number of TRIGgers Specification: II Key: 3

Minimum: 0 Maximum: 5 Worksheet: II Question: 4.

a

Definition: Number of trigger prices used in the run.

65

NYR

First Column;

Mnemonic: Nvimber of YeaRs

Specification: 12 Key: 1 Minimum: 1 Maximum: 99

Worksheet: I Question: l.b

Definition: Number of years in the study period.

12

PCCAN

First Column:

Mnemonic: JPer C^ent CANs

Specification: F5.2 Key: 3

Minimum: 0.0 Maximum: 90.0

Worksheet: I Question: 4.c

Definition: The percentage of cans in MSW by weight.

13

PCGLA

First Column:

Mnemonic: ^er C^ent GLAss

Specification: F5.2 Key: 3

Minimum: 0.0 Maximum: 90.0

Worksheet: I Question: 4.b

Definition: The percentage of glass in MSW by weight.

176
PCPAP

PCRES

PRCAN

Mnemonic: ^er ^ent PAPer Specification: F5.2 Key: 3

Minimum : 0.0

Worksheet: I Maximum: 90.0

Question: 4.

a

First Column: 3

Definition: The percentage of paper in MSW by weight

First Column: 18

Mnemonic: ^er ^ent RESidue

Specification: F5.2 Key: 3

Minimum: 0.0 Maximum: 100.0

Worksheet: I Question: 4.d

Definition: The percentage of residue in MSW by weight.

First Column: AO

Mnemonic: PRice CANs

Specification: F5.2 Key: 3

Minimum: 0.0 Maximum: 1000.0

Worksheet: I Question: 7.c

Definition: The price of cans in the market in dollars per

ton.

PRGLA Mnemonic: PRice of GLAs s Specification: F5.2 Key: 3 First Column: 35

Minimum: 0.0 Maximum: 1000.0

Worksheet: I Question: 7.b

Definition: The price of glass in the market dollars per

ton.

PRPAP

PTRIG(5)

RATE (50)

Mnemonic: PRice PAPer Specification: F5.2 Key: 1 First Column: 75

Worksheet: I Question: 7 .a Definition: Average price of paper as realized in the

market in dollars per ton. Used only if KSEP0=1; reset by

internally generated PRPAP if NTRIG greater than 0 and

INPAP =1.

Mnemonic: P^rice TRIGger Specification: F5.2 Key: 6 First Column: 7 Worksheet: 11 Question: A.

a

Definition: Kth trigger price in dollars per ton.

First Column: 52

Mnemonic: interest RATE

Specification: FA.l Key: 8

Minimum: 0.05 Maximum: 80.0

Worksheet: I Question: 2

Definition: If IDCC(K) = 1, RATE(K) is the interest rate in

percent for capitalization of the Kth cost category. Not

checked for IDCC(K) =» 0.

RCAH Mnemonic: Ratio CAn Haul

Specification: F10.5 Key: A First Column: A8

Worksheet: I Question: 12.

c

Definition: Conversion factor for haul of cans to the

market: ratio of energy haul requirement per dollar cost of

haul.

177
REVSP(80) Mnemonic: REVenue ^ite-Process

Specification: F9.3 Key: 17 First Column: 22

Worksheet: X Question: 5.b

Definition: Implicit revenue in dollars per input ton into

the Kth site-process, net of cost of haul to the market.

Represents the sum of net revenues arising from all

recoverables not entered into the model (i.e., the commodity

list) as explicit commodities.

R6LH Mnemonic: Ratio GLass Haul

Specification: F10.5 Key: 4 First Column: 38

Worksheet: I Question: 12.

b

Definition: Conversion factor for haul of glass to the

market: ratio of energy haul requirement per dollar cost of

haul.

RPAH Mnemonic: Ratio PAper Haul

Specification: F10.5 Key: 4 First Column: 28

Worksheet: I Question: 12.

a

Definition: Conversion factor for haul of paper to the

market: ratio of energy haul requirement per dollar cost of

haul.

RPRPAP

First Column:

Mnemonic: Ratio PRocessing PAPer Specification: F4.3 Key: 3

Minimum: 0.05 Maximum: 20.0

Worksheet: I Question: 9.

a

Definition: Ratio of paper to MSW as a cost driver in

processing paper as waste.

69

RPRRES

SIZINT

Mnemonic: Ratio PRocessing RESidue

Specification: F4.3 Key: 3 First Column: 77

Minimum: 0.05 Maximum: 20.0

Worksheet: I Question: 9.b

Definition: Ratio of source separation residue to MSW as cost driver in processing residue, i

Mnemonic: SIge of INTerval " Specification: F5.3 Key: 7 Worksheet: II Question:

Definition: Size of interval in the paper market price

distribution function in dollars of price per ton.

First Column:

2.C

6

TSU(60)

First Column! 32

Mnemonic: ^ons _SoUrce

Specification: F10.3 Key: 11

Worksheet: VI Question: 4

Definition: Tonnage at the Kth source in thousands of tons

per year.

178
USLA(30)

First Column: 35

Mnemonic: USe of LAnd

Specification: F8.5 Key: 14

Minimum: 0.0001 if ILACOF(K) = 1 Worksheet: IX Question: 4

Definition: Use of land on site by process K in acre feet

per thousand tons input.

VDRIV

First Column: 59

Mnemonic: \[ariable cost DRIVer Specification: F5.2 Key: 5

Worksheet: Note C

Definition: Variable cost used if IVDRV = 1 for urging into

the solution an acticity outside of the solution in site

and site-process forcing.

WTCC(50)

First Column:

Mnemonic: Weight ^ost Category

Specification: F8.3 Key: 8

Worksheet: III Question: 3

Definition: Weight of the Kth cost category for use in

L0PT=3 or 4. Should be positive or zero.

31

WTEC(20)

First Column: 29

Mnemonic: Weight Energy Category

Specification: F8.3 Key: 9

Worksheet: IV Question: 2

Definition: Weight for the Kth energy category for use in

LOPT = 3 or 4. Should be negative or zero.

179
APPENDIX B SAMPLE WORKSHEETS FOR THE NORTHEASTERN MASSACHUSETTS CASE EXAMPLE

The purpose of this appendix is to provide a detailed example of what a completed subset of the worksheets discussed in section 3.1 would look like.

The Northeastern Massachusetts case example, which was discussed in detail in

section 3.2, is used as a test vehicle. There is one example of each

worksheet. The data recorded on the worksheets were extracted from the file

(see exhibit 3.14). Although this is not the way it would be done in

practice, it helps to reinforce the crosswalk between the worksheets and the case study file. Readers are encouraged to consult exhibit 3.14 and identify

the card, columns and values entered on the worksheets. The cross reference

may be facilitated by consulting the Worksheet-Question portion of the

glossary (appendix A) for the term under consideration.

180
Exhibit B.l Completed General Information Worksheet

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

a. Base year for all cost comparisons: 1983 . b. Number of years in the planning period: 20 . Discount rate: 10% . Inflation rate: 6% . Waste stream composition (by weight)

:

a. Paper: 23.2% , b. Glass: 11.8% . c. Cans: 8.0% . d. Residue: 57.0% . Energy values of key commodities in millions of Btu per ton (when

processed as waste)

:

a. MSW: 5.2 . b. Paper: 6.0 . c. Residue: 5.0 . Source separation options:

a. No source seapration is to be offered for at least one source? yes

b. Source separation of paper, glass and cans is to be considered for at

least one source? yes . Expected revenue per ton for source separated commodities when delivered

at the market:

a. Paper: 20.00 . b. Glass: 25.49 . c. Cans: 15.00 . Collection costs:

a. Total cost per ton: 20.00 . b. Added cost per ton due to source separation: 2 .00 . Cost conversion:

a. Ratio of the cost of processing a ton of paper to the cost of

processing a ton of MSW: 0. 80 . b. Ratio of the cost of processing a ton of residue to the cost of

processing a tone of MSW: 0. 90 . c. Allocation of paper processing cost (due to higher capacity

requirements) to actual paper processed as waste: 0.60 . Energy values of source separated commodities (recycled at the market):

a. Paper: 20.0 . b. Glass: 2.0 . c. Cans: 2.0 . Energy requirements associated with collection:

a. Net energy required to collect a ton of MSW: 0.8 . b. Additional net energy required due to source separation: 0.1 . Energy conversions:

a. Energy required per dollar of haul cost to market for paper: 0.25 . b. Energy required per dollar of haul cost to market for glass: 0.20 . c. Energy required per dollar of haul cost to market for cans: 0.15 . Energy categories:

Paper at the market:

Glass at the market:

Cans at the market:

BTU differentiation:

Fuel Input

Fuel Input

Fuel Input

Steam Output . Collection (net energy): Fuel Input.

a.

b.

c.d.

e.

f. Source separation (net energy):

Cost categories:

a. Paper at the market:

b. Glass at the market:

c. Cans at the market:

d. Collection:

e. Source Separation:

Fuel Input

.

Municipal Cost . Municipal Cost . Municipal Cost . Municipal Cost . Municipal Cost.

181
Exhibit B,2 Completed Paper Market Data Worksheet

1. Is the market local or national? National . 2. Critical paper market prices:

a. Lower limit on price: 12 .00 . b. Number of intervals in paper price distribution: 5 c. Size of intervals in paper price distribution (dollars per ton):

5.00 . 3. Paper price distribution:

Interval number 1 2 3 4 5 6 7 8 9 10

Frequency 10.0 20.0 30.0 20.0 10.0

4. Issues related to the trigger price system:

a. Trigger prices to be considered (dollars per ton):

1. 15.00; 2. 20.00; 3. 25.00; 4. ; 5.

b. Percent of paper burned for each trigger price (optional):

1* ^3* ^A* • c. Average price of paper sold in the market for each trigger price

(optional)

:

1« i2« i3« iA« •3* •

Exhibit B.3 Completed Cost Category Data Worksheet

1. a. Name of this cost category: Contractor Cap Cost , b. Number of this cost category: 3 . 2. Is this an operating or capital cost category? Capital . 3. Weight given to this cost category: 1 .00 . 4. Differential inflation rate (in per cent per year) for this cost

category: 1.0% . 5. Answer sections a through d only if this is a capital cost category:

a. Useful life in years: 20

b. Years to capitalization for the loan: 15 c. Interest rate for the loan: 9.00

d. If this cost category corresponds to capital which was already in use

at the start of the study period (i.e., existing capital), then give

the number of years remaining prior to its need for replacement:

NA .

182
Exhibit B.4 Completed Energy Category Data Worksheet

1, a. Name of this energy category: Fuel Input, MBTU . b. Number of this energy category: 1 2. Weight given to this energy category: -1.00

Exhibit B.5 Completed Commodity Category Data Worksheet

1. a. Name of this commodity category: MSW in PKR,KTON . b. Number of this commodity category: 1 2. Haul costs (categories may be used for cost sharing):

a. First cost category for hauling this commodity: Municipal Cost . b. First cost factor (e.g., cost per ton mile) for hauling this

commodity: 1 .00

c. Second cost category for hauling this commodity: NA

d. Second cost factor for hauling this commodity: NA

3. Energy haul requirements (energy factors are energy requirements for haul

per ton-mile:

a. First energy category for hauling this commodity: Fuel Input, MBTU . b. First energy factor for hauling this commodity: 0.20

c. Second energy category for hauling this commodity: NA

d. Second energy factor for hauling this commodity: NA .

183
Exhibit B,6 Completed Source Data Worksheet

1. a. Name of this source: Lynn , b. Number of this source: 1 2. Depth of tracing requested: 9

3. Location of this source: 1 4. Kilotons per year of MSW generated by this source: 340.0 . 5. Source separation options:

a. No source separation: No . b. Unconditional source separation: yes . c. Conditional source separation; market paper according to the

following set of triggers:

1 . yes ; 2 . yes ; 3 . yes ; 4 . ; 5 . 6. Marketing considerations:

a. Cost category for haul of paper, glass and cans to the market:

Municipal Cost . b. Energy category for haul of paper, glass and cans to the market

Fuel Input, MBTU . c. Cost per ton to haul paper to the market: 2 .00 . d. Cost per ton to haul glass to the market: 1 .50 . e. Cost per ton to haul cans to the market: 1 .20 .

Exhibit B.7 Completed Site Data Worksheet

1. a. Name of this site: Saugus Proc

b. Number of this site: 1 2. Depth of tracing requested: 9

3. Location of this site: 5 4. Incoming commodity: MSW

5. Site preparation information:

a. Cost category for site preparation: Site Prep (4) . b. Total site preparation costs: 1000. . c. Energy category for site preparation: Fuel Input . d. Total energy required for site preparation: 250

6. Capacity considerations:

a. Is the site capacitated? Yes

b. Is landfill possible at the site? No

184
Exhibit B.8 Completed Landfill Data Worksheet

1. a. Name of this landfill: Amesbury Landfill , b. Number of this landfill: 1 2. Location of this landfill: 7

. 3. Land available at the landfill in acre-feet: 99800,

Exhibit B.9 Completed Process Data Worksheet

1.

2.

3.

4.

5.

a. Name of this process: Transfer Station . b. Number of this process: 1 Input commodity: MSW . Was this process in use at the start of the study period? No

If landfill is to perform on site, how many acre feet per kiloton of

input are required for on-site disposal? NA . Output commodities:

1 2 3 4 5 6 7 Name MSW

Coefficient 1.00

6. Cost allocations:

a. Number of first cost category:

b. Number of second cost category:

c. Estimated costs for this process:

First Segment Seconc Segment Third Segment

Fixed Variable Fixed Variable Fixed Variable

First Cost Category 20 1.80 40 0.50 60 0.26

Second Cost Category 20 1.80 35 0.61 55 0.24

7. Energy requirement:

a. Number of first energy category:

b. Number of second energy category; NA

First Segment Second Segment Third Segment

Fixed Variable Fixed Variable Fixed Variable

First Energy Category 0.80 0.50 0.70 0.40 0.60 0.30

Second Energy Category

185
Exhibit B.IO Completed Site-Process Combination Data Worksheet

1. a. Name of candidate site: Saugus Processor , b. Number of candidate site: 1 2. a. Name of candidate process: Water-Wall Incinerator , b. Number of candidate process: 2 . 3. Capacity limit: 360.0 . 4. Linear segments offered: 1. yes ; 2. NA ; 3. NA . 5. Implicit revenues for recoverables not defined through output

coefficients:

a. Number of cost category for process revenues: 2 . b. Process revenues (dollars per ton input): 1 .00 . c. Number of energy category: 1 d. Energy value of implicit revenues produced by this site-process

combination: 0.20

Exhibit B.ll Completed Market Data Worksheet

1. a. Name of this market: G.E. Lynn Steam , b. Number of this market: 1 2. Location of this market: 5

3. Commodity being sold: steam . 4. Market structure:

a. Market bounds (measured in cumulative commodity units):

1. 900 ; 2. 1800 ; 3. ; 4. ; 5.

b. Number of first cost category: 2 c. Number of second cost category: 1 . d. Revenue per unit by segment:

1 2 3 4 5 First Cost Category 3.00 2.00

Second Cost Category 0.00 0.00

5. Energy allocation:

a. First energy category for the energy value of the commodity in the

market : 2 b. First energy value of the commodity in the market: 5.40 . c. Second energy category for the energy value of the commodity in the

market: 1 d. Second energy value for the commodity in the market: -0.20 .

186
Exhibit B.12 Completed Cost Summation Data Worksheet

1. a. Name of this cost summation category: Contractor Cost , b. Number of this cost summation category: 1 . 2. Cost categories to be aggregated:

a. First cost category: 2

b. Second cost category: 3 . c. Third cost category: . d. Fourth cost category: . e. Fifth cost category: . f. Sixth cost category: , g. Seventh cost category: . h. Eight cost category: . i. Ninth cost category: . j. Tenth cost category: .

Exhibit B.13 Completed Transportation Data Worksheet

Type 1: Source-to-Site (card 69)

1. Origin (source number): 1 « 2. Destination (site nvimber) : 1

Type 2: Site-to-Site (card 77)

1. Origin (location number of site): 6

2. Destination (site number): 1 3. Commodity number of the output hauled along this linkage: 2(MSW) .

Type 3: Site-to-Market (card 82)

1. Origin (location number of site): 5

2. Destination (market number): 1 3. Commodity number of the recoverable hauled along this linkage: 6 (steam).

187
REFERENCES

American National Standards Institute, American National Standard Programming

Language FORTRAN , ANSI X3. 9-1978, New York, 1978.

Barton, D.R. and P. MacAuley, An Economic Analysis of Resource Recovery

Facilities and Waste Newspaper Recycling , Bureau of Industrial Economics, U.S.

Department of Commerce, August 1982.

Berman, E.B., WRAP - A Model for Regional Solid Waste Planning; User's

Guide , U.S. Environmental Protection Agency, SW-574, 1977.

Berman, E.B., R.E. Chapman and H.K. Hung, Program Documentation for the

Resource Recovery Planning Model , National Bureau of Standards, NBSIR (in

preparation)

.

Cochran, W.G., and G.M. Cox, Experimental Designs , (New York: John Wiley and

Sons, 1957).

Columbia University Urban Technology Center, Resource Recovery Technology for

Urban Decision Makers , January 1976.

Gass, S.I., Linear Programming; Methods and Applications , Fourth Edition,

(New York: McGraw-Hill Book Company, 1975).

Hensey, V., WRAP - A Model for Regional Solid Waste Planning: Programmer's

Manual, U.S. Environmental Protection Agency, SW-573, 1977.

Liebman, J.C., "Models in Solid Waste Management," in S. Gass and R. Slsson,

A Guide to Models in Governmental Planning and Operations , (Potomac, Maryland;

Sauger Books, 1975), pp. 139-164.

National Bureau of Standards, Technical Activities 1981: Office of Recycled

Materials , NBSIR 81-2417, November 1981.

Use of RAMP (Recovery And Market Planning) for the Evaluation of Policy Issues

in Resource Recovery , mimeo, undated.

Walker, W.E., "A Heuristic Adjacent Extreme Point Algorithm for the Fixed

Charge Problem," Management Science, Vol. 22 (1976), pp. 587-596.

188
NBS-114A (REV. 2-8C) U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

SHEET (See instructions)

1. PUBLICATION OR

REPORT NO.

NBS SP 657

2. Performing Organ. Report No, 3. Publication Date July 1983

4. TITLE AND SUBTITLE

The Resource Recovery Planning Model: A New Tool for Solid Waste Management

5. AUTHOR(S)

Robert E. Chapman and Edward B. Berman

6. PERFORMING ORGANIZATION (If joint or other than NBS. see instructions) 7. Contract/Grant No.

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE 8. Type of Report & Period Covered

WASHINGTON, D.C. 20234

Final

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City, State, ZIP)

Office of Recycled Materials

National Measurement Laboratory

National Bureau of Standards

Washington, DC 20234

10. SUPPLEMENTARY NOTES

Library of Congress Catalog Card Number: 83-600544

I I Document describes a computer program; SF-185, FlPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual sunr\mary of most si gnificant information. If document includes a si gnifleant

bi bliography or literature survey, mention it here) The Resource Recovery Planning (RRPLAN) model is designed with three purposes

in mind. First and foremost, is the ability to generate a preferred plan for

resource recovery. Second is the capability to evaluate a scenario specified by

the decision maker for technical and economic feasibility. Third is its use as a tool to facilitate the decision making process by providing answers to many what-if

questions through an in-depth sensitivity analysis. This report presents a nontechnical discussion of the RRPLAN model. The basic philosophy behind the model is

one of optimization. This approach was taken because it permits the economic and

engineering data associated with the problem (e.g., waste generation rates and the

location of processing facilities) to be organized in an objective manner. Althougl.

the model can focus on energy as its main objective, the minimization of the costs

of processing a region's solid waste stream is believed to be the objective most

often emphasized by decision makers. An example based on this objective is

critically analyzed and used to illustrate how the model would be applied in

practice.

12. KEY WORDS (S/x to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

Economic analysis; facility location; fixed-charge problem; mathematical

programming; optimization; resource recovery; solid waste management

13. AVAILABILITY

Unlimited

I I For Official Distribution. Do Not Release to NTIS

[X] Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402.

[^31 Order From National Technical Information Service (NTIS), Springfield, VA. 22161

14. NO. OF

PRINTED PAGES

198

15. Price

$6.00

USCOMM-DC a043-P80

* U.S. GOVERNMENT PRINTING OFFICE: 1983-380-997/5112

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