Simulation-Based Optimization for Planning of Effective...
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Simulation-Based Optimization for Planning of Effective Waste Reduction, Diversion, and Recycling Programs August 2012 PI: Nurcin Celik Department of Industrial Engineering University of Miami, Coral Gables, FL, USA Telephone: 1-305-284-2391, E-mail: [email protected] Research Team Eric D. Antmann, Xiaoran (Eileen) Shi, and Breanna Hayton Hinkley Center for Solid and Hazardous Waste Management University of Florida 4635 NW 53rd Avenue, Suite 205 Gainesville, FL 32653 www.hinkleycenter.org Report # XXXXX
Transcript of Simulation-Based Optimization for Planning of Effective...
Simulation-Based Optimization for Planning of Effective
Waste Reduction, Diversion, and Recycling Programs
August 2012
PI: Nurcin Celik
Department of Industrial Engineering
University of Miami, Coral Gables, FL, USA
Telephone: 1-305-284-2391, E-mail: [email protected]
Research Team
Eric D. Antmann, Xiaoran (Eileen) Shi, and Breanna Hayton
Hinkley Center for Solid and Hazardous Waste Management
University of Florida
4635 NW 53rd Avenue, Suite 205
Gainesville, FL 32653
www.hinkleycenter.org
Report # XXXXX
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TABLE OF CONTENTS
EXECUTIVE SUMMARY ......................................................................................................................... iv
RESEARCH TEAM ..................................................................................................................................... v
DISSEMINATION ACTIVITIES ............................................................................................................... vi
1. INTRODUCTION ................................................................................................................................ 1
2. BACKGROUND AND LITERATURE REVIEW ............................................................................... 3
3. SOLID WASTE MANAGEMENT INFRASTRUCTURE OF FLORIDA .......................................... 7
3.1 Definitions ..................................................................................................................................... 7
3.2 Facility Inventory .......................................................................................................................... 8
3.2.1. Landfills ................................................................................................................................ 9
3.2.2. Waste-to-Energy (WTE)Facilities ...................................................................................... 10
3.2.3. Material Recovery Facilities (MRF) ................................................................................... 12
3.2.4. Transfer Stations ................................................................................................................. 12
3.2.5. Organic Material Disposal Facilities ................................................................................... 13
3.2.6. Emerging Disposal Technologies ....................................................................................... 13
3.2.7. Transfer Mechanisms .......................................................................................................... 14
3.3 Generation Unit Inventory .......................................................................................................... 14
4. PROPOSED METHODOLOGY ........................................................................................................ 14
4.1 Overview of the Proposed Simulation-Based Decision-Making Framework ............................. 14
4.2 Database ...................................................................................................................................... 18
4.3 Assessment Module .................................................................................................................... 21
4.4 Resource Allocation Optimization Module ................................................................................ 23
4.5 Case Studies ................................................................................................................................ 26
4.5.1. Case Study 1: Miami-Dade County .................................................................................... 26
4.5.2. Case Study 2: Hillsborough County .................................................................................... 28
5. EXPERIMENTS AND RESULTS ..................................................................................................... 30
5.1 Southeast Region ........................................................................................................................ 30
5.2 South Region ............................................................................................................................... 32
5.3 Southwest Region ....................................................................................................................... 33
5.4 Central Region ............................................................................................................................ 34
5.5 Northeast Region ........................................................................................................................ 35
5.6 Northwest Region ....................................................................................................................... 36
6. DISCUSSION AND CONCLUSIONS .............................................................................................. 37
7. REFERENCES ................................................................................................................................... 39
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LIST OF FIGURES
Figure 1.1: Per-capita and total waste generation in Florida ....................................................................... 1
Figure 1.2: Current recycling performance in selected counties ................................................................. 1
Figure 3.1: Facility breakdown by type ....................................................................................................... 8
Figure 3.2: Disposal capacity by facility type ............................................................................................. 8
Figure 3.3: Capacity distribution by facility type ........................................................................................ 9
Figure 3.4: Ownership by facility type ........................................................................................................ 9
Figure 4.1: Mass flow diagram for Miami-Dade County SWM System ................................................... 17
Figure 4.2: Proposed simulation-based decision-making framework for integrated solid waste
management systems ............................................................................................................. 18
Figure 4.3: Database structure utilized in SQL-based database module .................................................... 20
Figure 4.4: Screenshot of the graphical user interface for the database module ........................................ 20
Figure 4.5: Screenshot of the assessment module’s graphical user interface ............................................ 21
Figure 4.6: Map of the FDEP regionsand example system of inter-county dependencies ........................ 22
Figure 4.7: Screenshot of the route-determination utilized by the integrated GIS ..................................... 23
Figure 4.8: Screenshot of the hybrid agent-discrete model in operation ................................................... 25
Figure 4.9: Screenshot of the real-time performance analysis mechanism utilized in the resource
allocation optimization module ............................................................................................. 26
Figure 4.10: Miami-Dade County SWM system ....................................................................................... 27
Figure 4.11: Hillsborough County SWM system....................................................................................... 29
Figure 4.12: Results from Case Study 2for maximization of recycling rate .............................................. 29
Figure 4.13: Results from Case Study 2 for minimization of total cost ..................................................... 29
Figure 5.1: Results from the Southeast Region .......................................................................................... 31
Figure 5.2: Results from the South Region ................................................................................................ 32
Figure 5.3: Results from the Southwest Region ......................................................................................... 34
Figure 5.4: Results from the Central Region ............................................................................................. 34
Figure 5.5: Results from the Northeast Region .......................................................................................... 36
Figure 5.6: Results from the Northwest Region ......................................................................................... 37
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EXECUTIVE SUMMARY
PROJECT TITLE: Simulation-Based Optimization for Planning of Effective Waste
Reduction, Diversion, and Recycling Programs
PRINCIPAL INVESTIGATOR: Nurcin Celik, Ph.D.
AFFILIATION: Department of Industrial Engineering, University of Miami
COMPLETION DATE: August 2012
OBJECTIVES:
The objective of this study is to develop a framework for the simulation and optimization of solid
waste management (SWM) infrastructure in the State of Florida. The proposed framework seeks to assist
all interested stakeholders, including but not limited to regulators, policymakers, public utilities, planners,
and private firms, on a statewide basis, in achieving the State of Florida’s 75% recycling goal for the year
2020. At present, the State of Florida’s recycling rate is approximately 30%, as per the objectives of the
landmark SWM Act of 1988, which set the State of Florida’s first recycling goal at 30%.
METHODOLOGY:
The proposed framework includes the following components:
1. Database: A structured database was developed including all SWM facilities and generation units
located in the State of Florida. Facilities included landfills, waste-to-energy (WTE) facilities, material
recover facilities (MRF), and transfer stations (TS). Municipalities were used as generation units. All
data in the database is in a human-readable format for ease of modification or addition after the
framework is delivered.
2. Assessment Module: The assessment module prompts users to select one or multiple counties to
define a study region. Once a study region is defined, the appropriate facilities and generation units
(model agents) are loaded from the database. The assessment module also contains a smart-linking
mechanism, which creates meaningful and quantified operational and economic links between cities
and the facilities which process their wastes (the facilities under contract with the municipal
governments), and between facilities which have a transfer relationship. In addition, an integrated
Geographic Information System (GIS) is included in the assessment module, which provides the
road-network distance between agents that have linkages.
3. Resource Allocation Optimization Module: The resource allocation optimization module features a
novel hybrid agent-discrete model. The model is initialized using automated constructors applied to
data imported by the assessment module, to ensure that the model reflects the latest database content,
and models the operational (tonnage), economic, and environmental (recycling rate and greenhouse
gases) attributes of each model agent. A performance analysis mechanism reviews the feasibility and
performance of all candidate solutions.
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RESEARCH TEAM
Nurcin Celik (P.I.) is an assistant professor at the Department of Industrial
Engineering at the University of Miami. She received her M.S. and Ph.D. degrees in
Systems and Industrial Engineering from the University of Arizona. Her research
interests lie in the areas of integrated modeling and decision making for large-scale,
complex and dynamic systems. Her dissertation work is on architectural design and
application of dynamic data-driven adaptive simulations for distributed systems. She
has received several awards, including University of Miami Provost Award (2011),
IAMOT Outstanding Research Project Award (2011), IERC Best Ph.D. Scientific Poster Award (2009),
and Diversity in Science and Engineering Award from Women in Science and Engineering Program
(2007). She can be reached at [email protected].
Eric D. Antmann is an undergraduate student in the Department of Civil,
Architectural, and Environmental Engineering at the University of Miami. His research
interests include solid waste management, water resources, and the simulation of large-
scale systems. He is presently President of the Engineering Advisory Board at the
University of Miami, and has received several awards, including the John Farina
Memorial Endowed Scholarship, Florida Air and Waste Management Association
Scholarship, and first place at the University of Miami Research, Creativity, and
Innovation Forum. He can be reached at [email protected].
Xiaoran (Eileen) Shi is a Ph.D. student in the Department of Industrial Engineering at
the University of Miami. Her research interests include sequential Monte Carlo
methods, and integrated system simulations with their application to distributed
electricity networks and solid waste management. She earned her bachelor's degree
from Department of Industrial Engineering at the Nankai University, China. She
earned the title of Excellent Student and Excellent Student Leader of Nankai
University. She can be reached at [email protected].
Breanna Hayton is an undergraduate student in the Department of Mechanical
Engineering at the University of Miami. Her research interests include solid waste
management and the simulation of large-scale systems. She can be reached at
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DISSEMINATION ACTIVITIES
CONFERENCES AND INVITED TALKS:
1. Industrial and Systems Engineering Research Conference (ISERC); May 19-23, 2012; Orlando, FL –
The conference paper was peer-reviewed and selected
for inclusion the Conference Proceedings.
2. University of Miami Research, Creativity, and
Innovation Forum (RCIF); April 11, 2012; Coral
Gables, FL – The project was awarded first place in
the Engineering Division.
3. Atlantic Coast Conference (ACC) Meeting of the
Minds Student Research Conference; March 30 –
April 1, 2012; Blacksburg, VA – The research team
was invited to present the current progress of our
study.
4. Society of Environmental Journalists (SEJ) Annual
Conference; October 19-23, 2012; Miami, FL – The research team was invited to present our findings
as a poster.
SITE VISITS AND PRESENTATIONS:
1. The team visited the Reuter Recycling Facility, owned by Waste Management, Inc., on July 9, 2012.
2. The team presented to Southern Waste Systems and
the Miami-Dade County Department of Public Works
and Waste Management on February 16, 2012.
3. The team visited the Miami-Dade County Department
of Public Works and Waste Management and had the
meeting with Dade County’s officers on August 16,
2011.
4. The team visited the Miami-Dade County Department
of Public Works and Waste Management’s Resource
Recovery Facility (RRF), a waste-to-energy facility,
on July 28, 2011.
TECHNICAL AWARENESS GROUP (TAG) MEETINGS:
The project team hosted three TAG meetings, on September 23, 2011, January 27, 2012, and July 13,
2012. Participants included:
TAG Members
− Dr. Helena Solo-Gabriele, Professor, University of Miami – expertise in environmental impacts of
solid waste
− Mrs. Kathleen Woods-Richardson, Director, Miami-Dade County Department of Solid Waste
Management – expertise in solid waste regulations and operations
− Dr. Jacqueline P. James, Assistant Professor, University of Miami – expertise in landfill design
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− Dr. Shihab Asfour, Professor, University of Miami– expertise in planning and optimization of large
scale systems
Participants from the Advisory Board and Industry
− John Schert from Hinkley Center
− Tim Vinson from Hinkley Center
− Ron Beladi from Neel-Schaffer, Inc.
− David Gregory from R. W. Beck, Inc.
− Jim Bradner from FDEP and Hinkley Center
− Ram Tewari from Broward County Solid Waste
and Recycling Services
− Kathleen Woods-Richardson from Miami-Dade
County Department of Public Works and Waste
Management
− Paul Mauriello from Miami-Dade County
Department of Public Works and Waste
Management
− Brenda Clark from HDR Engineering, Inc.
WEBSITE: The project team has prepared and posted a regularly-updated web page, accessible at
http://www.ie.miami.edu/celik/swmwebsite/default.htm.
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1. INTRODUCTION
Over the past several decades, both the volume and diversity of Municipal Solid Waste (MSW)
generation has increased markedly worldwide, with the United States exhibiting the greatest rate of
growth, both overall and per-capita, by a significant margin. This burgeoning growth, combined with the
concomitant increase in the regulation of disposal operations and dwindling availability of suitable
disposal sites, has made the planning and operation of Integrated Solid Waste Management (SWM)
systems progressively more challenging. As a result of these factors, and growing pressures for
environmental protection and sustainability, the State of Florida has established an ambitious 75%
recycling goal, to be achieved by the year 2020. At present, the recycling rate in the State of Florida is
approximately 30%, based on a goal set by the landmark Solid Waste Management Act of 1988. Clearly,
in light of the inherently challenging nature of the MSW stream and management, combined with the
fiscal difficulties befalling municipalities throughout the state, this goal presents numerous technical and
social challenges to all parties of solid waste management. Figures 1.1 – 1.2 show overall and per-capita
MSW generation, over the past two decades, and current recycling performance. Despite decreasing total
and per-capita waste generation, population growth is anticipated and the 2020 goal is rapidly
approaching, providing impetus for the continued advancement of such optimization schema.
Figure 1.1: Per-capita and total waste
generation in Florida
Figure 1.2: Current recycling performance in
selected counties
In light of this ambitious goal, and a variety of other pre-existing market conditions, many
stakeholders, including utilities, regulators, governmental agencies, municipalities, and private firms,
0
5
10
15
20
25
30
35
40MSW Generation in Florida
Total Waste Generation (Million Tons/Year)
Per-Capita Waste Generation (x 200 Pounds/Year)
0%
20%
40%
60%
80%
100%Selected Recycling Rates
Recycled Landfilled Combusted
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have recognized the necessity of modeling and optimization schema to provide decision-support for such
systems. However, the development of such solutions is challenged by the large-scale, dynamic, and
multi-faceted nature of integrated solid waste management, as well as by the multitude of stakeholders,
each with vested interests in performance, and unique objectives and constraints. Thus, a proposed
solution must begin by considering the many possible alternatives available for solid waste management
in a given region, including various recycling programs, collection schemes, transfer arrangements, and
disposal options available. The proposed solution must then assess and handle the numerous and
significant uncertainties facing the SWM system, both arising from internal and external sources, and
occurring at both the facility and system levels. These challenges are considerable and unique amongst
large-scale system optimization problems, thus requiring the development of novel simulation-based
decision support techniques for this field.
Table 1:MSW production at national and state levels (State of Florida accounts for more than 10% of total
waste production in the U.S.)
2000 2010
United States 243 million tons/yr 250 million tons/yr
Florida 25.1 million tons/yr 26.9 million tons/yr
Further complicating matters, SWM infrastructure is owned by a myriad of private firms in a
majority of cities. These numerous entities further complicate planning, reporting, and enforcement tasks,
and introduce many additional, and often conflicting, operational and financial objectives and constraints.
Each of these contractors becomes a stakeholder to the SWM system, with a vital interest in its
performance, but with unique objectives and constraints that must be met for its continued business
viability. In a monolithic SWM system, profitability must only be maintained on a system-wide scale,
while in these modularized systems, each private contractor, which may only cover one segment of the
SWM lifecycle, must make a suitable profit to remain in business. In Case Study 1 of this project, a
simulation-based decision-making and optimization framework for monolithic SWM systems was
developed and successfully applied to the Miami-Dade County Solid Waste Management System.
However, the SWM infrastructure of Miami-Dade County is relatively unique amongst other systems in
the State of Florida, in that it is largely publicly owned. Therefore, the aforementioned case study focused
on a single set of operational and financial objectives. Considering the significant disparities between this
system and most others, which are modular in nature and have significant private ownership, Case Study
2 of this project extended the previous theory and framework into a multiple-objective, hybrid agent-
discrete optimization framework, which was applied successfully in Hillsborough County, Florida. This
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framework is finally applied to the remainder of the State of Florida to prepare conclusions and
recommendations.
The framework developed for Case Study 2 differs fundamentally from the former by replacing a
monolithic simulation and optimization paradigm with a modular approach. Such a change is obligatory
in order to accurately capture the diverse and sophisticated relationships developed between and amongst
generation units (municipalities) and the various contractors, as well as the unique controls, constraints,
and objectives of each party. Further complicating these relationships is the fact that a majority of these
contractors do not handle the entire life cycle of MSW, and thus relationships exist both amongst
processing firms and between those firms and the generation units. Thus, the framework developed for
Case Study 2 provides an advanced linking mechanism to permit varied stakeholders to interact and
ensure that the complete MSW lifecycle is provided. For example, in the City of Pembroke Pines, in
Broward County, MSW is collected by one contractor, hauled to a transfer station owned by a different
contractor, and then sent to either a material recovery facility or landfill operated by this firm. In addition
to the relationships formed by individual cities, Broward County has formed an Interlocal Agreement
(ILA) of 26 municipalities, for solid waste disposal and recycling services. This agreement adds further
complications to the set of system relationships, although its constituents have decided to dissolve the
ILA in the end of 2012.Thus, the creation and dissolution of multi-generation-unit blocks must also be
considered in the proposed framework.
Planning an effective solid waste management system in such a complex environment therefore
requires the simultaneous consideration of multiple conflicting operational and financial objectives.
Hence, the aforementioned modular simulation and optimization strategy must simultaneously realize the
optimal overall interest while maximizing the interests of all involved parties. The interests of the various
private contractors will be weighed based on the process-tons handled out of the overall system demand.
A process-ton will be defined as one ton of MSW through one major unit process (collection, transfer,
materials recovery, and disposal). Hence, the objective of this study is to develop a framework to address
the simulation-based optimization of SWM systems with modularized or monolithic organization, with
which we seek to develop a framework and capture a method to realize the optimal overall interest, with
balanced interests of all parties.
2. BACKGROUND AND LITERATURE REVIEW
The complexity of SWM infrastructure has grown markedly in the past several decades, concomitant with
increasing waste generation and growing environmental regulations (Everett and Modak, 1996; Hokkanen
and Salminen, 1997; Thompson and Tanapat 2005; Li and Huang et al., 2009). Accordingly, significant
modifications to existing solid waste management systems have become necessary to achieve the most
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optimal allocation of limited resources, by taking both economic and environmental responsibilities into
consideration. The first economic cost-optimization model was proposed by Anderson (1968). Since
then, various deterministic mathematical programming models and fuzzy integer programming
approaches have been developed for the planning of solid waste management systems. These modeling
techniques include linear programming (Lund and Tchobanoglous, 1994; Lyunggren, 2000), mixed
integer programming (Jenkins, 1982; Zhu and ReVelle, 1990; Gupta, Sharma, 2011), and dynamic system
programming (Huang, 1994;Baetz, et al., 1996; Liand Huang, 2009), amongst many others. Chang and
Chang (1998) present a constrained non-linear optimization model, aiming at maximizing the overall
operation profits of the SWM systems, in which energy and material recovery requirements are
considered and the number and typologies of plants are defined as a priori. The profit function includes
all of the possible benefits (i.e., electricity sales, revenue from re-used products, etc.), transportation,
treatment, maintenance, and recycling costs. In a similar vein, an integrated interval-parameter fuzzy
mixed integer programming model was developed by Huang et al. (2001) as a new analytical tool for the
planning of solid waste management systems. The modeling approach proposed in this study improves
the system planning by providing support for determining desired waste flow allocation schemes under
various conditions. They also demonstrate that the decision alternatives can be obtained by adjusting
different combinations of decision variables within their solution intervals. Thus, the proposed modeling
method can be easily used for decision-makers based on different projected applicable conditions. In
their model, the objective function includes waste collection and transportation costs, capital costs for
facilities, and residual market values, through which the maximization of the profitability are maintained
on a system-side scale.
Limitations that are encountered on both of these models are similar. In particular, the decision
variables in these two models denoting the amount of wastes transferred among the different types of
plants are continuous, leading to the impossibility of capturing the discrete nature of the transfer
operations. In addition, while the problems are subjected to mass balance, facility capacities, and
technical constraints, the models do not take into account aspects related to environmental issues. In
order to incorporate environmental impacts, including noise generation by waste processing facilities,
leachate, and gaseous discharges, as factors for decision-making, Tsiliyannis (1999) discussed the major
environmental problems related to MSW management based on the life cycle assessment (LCA)
methodology, in particular the release of the pollutants generated in landfills, waste-to-energy facilities
and compacting facilities. The conclusion obtained through his analysis has been used as the benchmark
for evaluation of environmental impacts on integrated MSW management schema in many studies
(Fiorucci, et al., 2003; Costi, et al., 2004;Minciardi, et al., 2008). Additional studies regarding planning
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of the solid waste management systems that consider financial issues as well as the environment related
issues are compared in Table 2.
Table 2: Selected works on environmental-economic planning of solid waste management
Authors (Year) Problem Assessed Case Study Benefits Disadvantages/Limitations
Barton et al.
(2000)
A method for identifying the
environmental burdens that
occur during waste collection,
treatment, and disposal.
Hypothetical
regional waste
management
system
Takes into account the
environmental impacts throughout
the entire life cycle of the waste
and tests the potential benefits
that LCA may bring.
A real-world application is
necessary to demonstrate the
feasibility and validity of the
proposed method.
Shmelev and
Powell (2005)
Analysis of the ecological-
economic trade-offs in the
development of the SWM
systems
Gloucestershire,
England
Public health and biodiversity,
which are novel aspects of the
environmental impacts, are
particularly emphasized.
The approach is weakened by
software limitations.
Pollution management costs are
not taken into account.
Rathi (2007)
Planning of SWM systems
that integrate different options
and stakeholders involved in
the solid waste management.
Mumbai, India
Sensitivity analysis is
conducted for different types of
costs related to the objective
function.
Only the compost plants and
landfills are considered in the
optimization model.
Parameters related to the
environmental costs used in the
case study are not the real data,
imposing a limitation on the
validation of the proposed model.
Noche et al.
(2010)
Strategic planning for the
optimal locations of transfer
stations and the optimal
allocation of the wastes.
Duisburg City,
Germany
The proposed integer
programming model can be used
to optimize the haul transfer and
disposal systems, the trade-off
transportation costs against the
capital and the operation costs of
introducing the transfer station.
A waste prediction model is
needed for the long term planning.
Bio-wastes and recycling
material should be considered in
the future work.
Markovic et al.
(2010)
System efficiency and system
user satisfaction optimization
based on analytic hierarchy
process.
Niš, Serbia
Easily incorporating or
removing aspects and issues that
play an important role in the
SWM systems.
Setting of degree of importance
is unreliable.
Most of the aforementioned deterministic programming models neglect system uncertainties to a
great degree and rely heavily on hypothetical cases, ceasing to be practically useful in the real-world
applications. From this perspective, a modeling method with realistic settings is a necessity, in which
uncertainties within the SWM systems must be characterized via both quantitative and qualitative factors.
According to Huang, et al.(1996) and Yeomans, et al. (2003), grey programming (GP) and genetic
algorithm with simulation (GAS) may be a superior method for solving problems of solid waste
management involving uncertainties and stochastic components. In order to provide a computationally
efficient optimization technique, Huang (1996) uses grey binary variables to represent the grey decisions
that reflect potential system condition variations caused by the input uncertainties. In addition,
continuous decision variables are incorporated to represent the waste flow allocation. Results achieved
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from a synthetic case study indicate that feasible decision alternatives can be generated through
interpretation of the optimal or near-optimal grey solutions under the projected applicable system
conditions via their proposed method. However, in Yeomans’s work, a population of possible candidate
solutions is maintained via the genetic algorithm. Simulation is then conducted for all possible scenarios
(i.e., for any given settings for decision variables). Finally, the optimal solution is determined from one
of the alternatives generated during the simulation phase. Both of these methods aim to determine a
single optimal policy that can be applied into MSW system. However, policy makers often require a
selection of alternatives instead of a single solution. In response to this situation, the resource allocation
optimization module included in the proposed framework features a novel agent-discrete model of the
system under consideration, in which a performance analysis, both at the global and agent level, is carried
out for each candidate generated via meta-heuristic optimization. The inventory of feasible candidates
becomes a selection of feasible solutions, each with performance statistics provided by the model.
Generally, the goals to optimize the overall interest and balance the interest of all parties are not
easily reconciled in decision making, which makes it difficult to integrate strategies aiming at producing
an optimal profitable and environmentally sustainable option for all the attended entities. Thus, the
objective of this study is to develop a multi-objective hybrid simulation and optimization framework
while considering the demands of all agents attended in the system. The proposed decision making
framework aims at progressing on the earlier works presented in the literature in terms of its
implementation viewpoint. By embedding the modularized organization into the proposed model, the
work can realize the optimal overall interest, while balancing interests of all parties, and can be
realistically applied to existing and future SWM systems.
Therefore, the proposed solution offers several advancements over those found in the literature.
The consideration of environmental, economic, and operational factors in a single model and optimization
scheme facilitates multi-objective optimization and objective weighting, and increases the potential
applications of the proposed tool. Uncertainties are considered both at the facility and the system level,
providing a more realistic and controllable simulation of uncertainties and randomness. Most
significantly, the use of a hybrid agent-discrete modeling topology allows for the accurate capture of
discrete events while providing the detailed agent-specific analysis and controls provided by agent-based
modeling. This novel modeling technique, combined with the use of automated agent constructors and a
smart-linking mechanism, allows the adaptation of the proposed tool to numerous and varied systems,
with significantly lower computational burden than other allocation variable definition schema exert.
Thus, the proposed tool provides greater levels of detail and inclusion while being applicable to
dramatically more systems than those found in the literature.
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3. SOLID WASTE MANAGEMENT INFRASTRUCTURE OF FLORIDA
Solid Waste Management activities in the State of Florida are regulated by the Florida Department of
Environmental Protection (FDEP), which is the licensing authority for all SWM facilities in the State, and
maintains records of generation and disposal rates for each of the State’s 67 counties. At the county level,
most county governments coordinate collection and disposal activities to at least some extent. The level
of authority held by the county government varies concomitantly with the proportion of the disposal
infrastructure that is county-owned, which also varies widely between counties.
Several highly-populated counties, such as Miami-Dade and Palm Beach, have strong county
SWM authorities, which own the majority of the disposal facilities in the county. In these cases, most of
the county’s waste generation is disposed of in-county, and collection may be coordinated by either the
county authority or private contractors working for the county authority. However, in most cases, the
majority of disposal facilities are owned by private contractors, which either contract directly with the
cities for disposal services, or with hauling contractors which in turn contract with the cities. Such
modular systems exist in both low and high-population counties, and constitute the majority of the SWM
systems in the State.
3.1 Definitions
− Municipal Solid Waste (MSW): The stream of non-hazardous solid waste materials generated by all
residential and commercial entities in a given region.
− Class I Waste: Solid waste consisting of nonhazardous materials which do not qualify as Class III
waste (defined below), typically household and some commercial waste which includes food waste or
other putrescible materials (defined pursuant to Florida Administrative Code Chapter 62-701.200).
− Class I Landfill: A landfill permitted to receive Class I waste, subject to the most stringent lining,
leachate treatment, groundwater monitoring, and other requirements set by the FDEP. These landfills
are significantly more costly to construct and operate than a Class III landfill (defined below) (defined
pursuant to Florida Administrative Code Chapter 62-701.200).
− Class III Waste: Solid waste consisting of yard trash, construction and demolition debris, processed
tires, asbestos, carpet, cardboard, paper, glass, plastic, furniture other than appliances, or other
materials approved by the FDEP, that are not expected to produce leachate that poses a threat to
public health or the environment(defined pursuant to Florida Administrative Code Chapter 62-
701.200).
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− Class III Landfill: A landfill which receives only Class III waste, and is subject to relaxed lining,
leachate management, and other requirements by the FDEP (defined pursuant to Florida
Administrative Code Chapter 62-701.200).
− Recycling Rate: The total tonnage of MSW delivered to Material Recovery Facilities (MRF’s)
divided by the total tonnage of MSW generated.
− Recycling Rate (FDEP): The total tonnage of MSW delivered to Material Recovery Facilities
(MRF’s) divided by the total tonnage of MSW generated, subject to the following adjustment,
pursuant to Chapter 403.786 of the Florida State Statutes:
o In counties in which less than 50% of the MSW stream is delivered to Waste-to-Energy (WTE)
facilities, one ton is added to the total tonnage recycled per megawatt hour (MWh) of total
electrical generation at WTE facilities.
o In counties in which more than 50% of the MSW stream is delivered to WTE facilities, two tons
are added to the total tonnage recycled per MWh of total electrical generation at WTE facilities.
3.2 Facility Inventory
A total of 302 operating SWM facilities were identified in the State of Florida. Information regarding
their name, location, owner, daily capacity, and type was gathered from their FDEP licenses, as reported
by the Florida Georelational Database. The distribution of these facilities by type, capacity, and
ownership is detailed in Figures 3.1 – 3.3.
Figure 3.1: Facility breakdown by type
Figure 3.2: Disposal capacity by facility type
20%
6%
39%
35%
Facility Count by Type
Landfill
Waste-to-Energy
Material Recovery Facility
55%
13%
32%
Disposal Capacity by Facility Type
LandfillWaste-to-EnergyMaterial Recovery Facility
9
Figure 3.3: Capacity distribution by facility type Figure 3.4: Ownership by facility type
3.2.1. Landfills
Landfills are the final destination for materials not extracted from the waste stream for recycling or
combustion for energy. Modern landfills differ significantly from earlier open dumping facilities, by
utilizing a variety of daily cover and gas collection systems to be more environmentally friendly and
socially acceptable. Landfilling is currently used for some portion of the waste stream from every county
in the State. However, landfills have numerous long-term economic and environmental costs, which have
brought alternatives into favor. Despite this, for the foreseeable future, they must be considered as part of
the waste disposal solution.
One of the most significant byproducts of landfilling is leachates. These liquids contain a number
of organic, inorganic, and toxic chemicals, and must be collected and treated before discharge into the
environment. Depending on the maturity, age, and biochemical reactions occurring in the landfill, the
characteristics of its leachate vary significantly, in terms of organics, ammonia, nitrogen, heavy metals,
dissolved solids, chemical oxygen demand (COD), and color. As a result, leachate treatment is a complex
and costly process, and often must continue for many years after the closure of a landfill. A variety of
processes are used for leachate treatment, including immediate disposal to the sewage utility, pretreatment
on site followed by disposal to the sewage utility, and complete treatment on site. Furthermore, various
recirculation schemes have been developed to reduce the overall volume of leachates. All of these factors
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Landfill WTE MRF TS
Facility Capacity Distribution
(Tons/Day)
0-100 100-1000 1000-5000 5000 +
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Landfill WTE MRF TS
Faciity Ownership by Type
Public Private
10
are operational characteristics of landfills, and may have significant impacts on their operations, costs,
and capacities. Another significant byproduct of landfilling is greenhouse gas emissions. The chemical
and biochemical reactions which take place within landfills create large volumes of methane and other
greenhouse gases, giving landfills their characteristic odor. These gases must be collected and either
flared off or combusted for electrical generation. Both of these methods are costly and cause significant
greenhouse gas releases, and the collection and handling of these gaseous emissions must often continue
for many years after the closure of a landfill, adding to the lifecycle cost of landfilling.
A total of 62landfill facilities are presently in operation in the State of Florida, with a combined
daily capacity of 109,947 tons. Both Class I and Class III landfills are in operation in the State, although
no distinction was made in this project, due to the unavailability of information from many privately
owned facilities. No bioreactor landfills or other emerging landfill technologies are currently used at a
utility scale in the State, and landfill gas combustion for energy generation was not considered, due to its
relatively small scale and wide disparities in implementation. The lifetime greenhouse gas emission rate
for landfill disposal was set at 0.02575 metric tons carbon equivalent (MTCE) per ton of waste, based on
the United States Environmental Protection Agency’s (EPA) 2008 report, entitled Solid Waste
Management and Greenhouse Gases: A Lifecycle Assessment of Emissions and Sinks. Average
operational costs were set at $30 per ton, based on a survey of several representative facilities statewide.
Little if any variance these figures is anticipated amongst the State’s landfills, as they depend only upon
waste composition and the regulatory environment, both of which are relatively consistent statewide.
3.2.2. Waste-to-Energy (WTE)Facilities
WTE facilities use combustion to extract a portion of the chemical energy stored in waste materials.
These facilities serve as a final destination, much like landfills, except that during the process of
combustion, energy is extracted, which is used to generate electricity via a boiler and steam turbine, and a
significant volume reduction is achieved. The ashes leftover from combustion, typically constituting 5 –
10% of the original volume of waste, are either sent to an ash landfill (FDEP class II landfill, or monofill),
or are reused as an aggregate in civil engineering projects.
Two established technologies are used at WTE facilities in the State, Refuse-Derived Fuel (RDF)
and Mass Burn. At an RDF facility, materials in the waste stream of a moderate to high heating value,
such as paper and plastic, are selected, and undergo a shredding process prior to combustion. The
remaining materials, typically constituting less than 15% of arrivals, are either sold as a low-heating-value
biomass fuel, or sent to a landfill. At Mass Burn facilities, the arriving wastes are sent directly to
combustion in a boiler, with minimal processing beside the manual or automated removal of unacceptable
11
materials. Both types of WTE facilities typically recover both ferrous and non-ferrous metals, either by
pre-sorting arrivals or sorting ash, typically with electromagnets and eddy current separators. Emissions
control devices, such as baghouses and scrubbers, are also used at these facilities, and hazardous
atmospheric emissions, such as mercury and dioxins, are dramatically lower than at first-generation
incineration facilities of the 1970’s or earlier.
A total of 17 waste-to-energy facilities are currently in operation in the State of Florida, with a
combined daily capacity of 25,528 tons. Emerging WTE technologies, such as gasification and plasma-
arc treatment, are not yet used on a utility scale in the State, and thus were not considered. Due to the
time required to site, design, permit, and construct these highly complex facilities, combined with their
high cost ($1 Billion +), it is unlikely that any new WTE facilities will be in operation in the State by the
year 2020.Energy generation, emissions, and costs are similar between current implementations of both
RDF and Mass Burn technologies, and the sale of biomass fuel was not considered, due to its small scale
at present. The lifetime greenhouse gas emission rate for disposal at these facilities was set at 0.05889
MTCE per ton of waste, based on the 2008 EPA report entitled Solid Waste Management and Greenhouse
Gases: A Lifecycle Assessment of Emissions and Sinks. Average operational costs were set at $60 per ton,
based on a survey of several representative facilities statewide, and electrical generation was set at 0.75
megawatt-hours (MWh) per ton, based on a report by the American Institute of Chemical Engineers.
Potential revenues from the sale of electricity were not considered, due to significant variability in the
market for electricity, which is beyond the scope of this project.
Significantly greater variance between these facilities is expected than amongst landfills. This is
due to the inherently greater complexity of these facilities, in which size, age, technologies used, state of
maintenance, and waste composition all impact operational cost, emissions, and electrical generation.
Thermal efficiency in these facilities may range from as low as 13% to as high as 60%, and net electrical
generation for WTE facilities may range from as low as 0.6 MWh per ton to as high as 1.2 MWh per ton.
However, because detailed performance data was not made available on a per-facility basis for several
commercially-owned facilities, these average figures were used, which are considered representative of
the typical WTE facility in the United States by their respective authors.
In its calculation of recycling rates, the FDEP provides recycling credits for energy generated at
WTE facilities. However, due to the structure used to assign credits, detailed in section 3.1, and the
technological feasibility of generating greater than 1 MWh of electricity per ton of waste, it is conceivable
that a recycling rate greater than 100% could be achieved should a modern, highly-efficient WTE facility
be present. In the exceptional hypothetical case in which a county sent 100% of waste to WTE facilities
that generated 1 MWh per ton, well within technological feasibility, an FDEP recycling rate of 200%
12
would be achieved. However, due to the use of averaged electrical generation rates, and existing WTE
capacities, this does not occur in any county during this study.
3.2.3. Material Recovery Facilities (MRF)
MRF facilities sort recyclable materials, such as paper and plastic, from arrivals, and divert these
materials to markets for reuse. These facilities serve as the final destination in the SWM lifecycle for
recyclable materials, as they are returned to manufacturing for reuse in new products after leaving an
MRF. The design and performance of MRF facilities varies widely amongst implementations, and is a
significant area of research and development in and of itself. MRF facilities are designed to accept either
single-stream recyclables (mixed paper, plastic, and metal) or multi-stream recyclables (source-separated
by type). A special class of MRF’s, Dirty MRF’s, receives unsorted MSW, separates recyclable
materials, and sends the remainder of the wastes to either landfill or WTE facilities. These facilities are
uncommon, due to their significantly greater operational challenges and occupational hazards compared
to other types of MRF’s, and none are in operation in the State.
A total of 118 MRF facilities are presently operational in the State of Florida, providing a total
daily capacity of 64,072 tons. These facilities have by far the greatest variability, in terms of their size,
operational cost, recyclable recovery rates, and greenhouse gas impacts. The strength of commercial
markets for recycled materials, and purity of those materials, has a significant impact on the viability of
MRF facilities. Some MRF’s are able to pay for wastes, as operational efficiency and local markets are of
suitable strength to allow profitable operation, while others must charge generation units or haulers for
delivering recyclables. Therefore, operational costs were set at $59.77 per ton, based on a survey of
representative facilities statewide, as facility-specific data was not made available for numerous MRF
facilities. This cost figure assumes no revenue is made from recycled material sales, due to the lack of
market data. Greenhouse gas emissions were also set at zero, as MRF operations result in little if any
direct emissions, and emissions related to their energy consumption are minimal and beyond the scope of
this project. Revenues from the sale of sorted recyclable materials were also not considered, due to wide
variance in markets and the quality of recovered materials, which were not included in the scope of this
project.
3.2.4. Transfer Stations
Transfer stations, unlike the former three classes of facilities, are not final destinations. Wastes and
recyclable materials are delivered to these facilities by collection vehicles, and picked up by larger
transfer trucks for delivery to a final destination facility. Transfer stations are constructed for a variety of
13
social and economic reasons, as they may reduce traffic congestion and allow collection vehicles to run
more loads per day, depending on locations and operations.
There are currently 105 operational transfer stations in the State, which have a combined daily
capacity of 54,515tons. Much like MRF facilities, there is much variance between transfer stations, in
terms of their size, operations, and operational cost, and direct greenhouse gas emissions are negligible
and not considered in this project. However, unlike MRF’s, all transfer stations have a direct operational
cost, although in some cases it is made up for by reduced transfer costs depending on routing and
operations. The lack of viable direct revenue from transfer stations, as opposed to MRF facilities, is
reinforced by their significantly greater proportion of public ownership, as illustrated in Figure
3.4.Average operational cost was set at $38 per ton, based on a survey of representative facilities in the
State, and greenhouse gas emissions were set at zero, as transfer station operations cause minimal if any
direct emissions.
3.2.5. Organic Material Disposal Facilities
At present, there are two experimental organics disposal and recycling facilities operating in the State.
Both are owned and operated by Waste Management, Inc., and are at the small utility scale. These
facilities use a composting process to convert organic materials, mainly food waste, into a soil
amendment suitable for agricultural use. Unlike earlier first-generation composting facilities, these
facilities produce a soil amendment which has proven to be commercially marketable, and do not produce
the objectionable odors characteristic of earlier composting sites.
Currently, these facilities are not considered SWM facilities by the FDEP, and thus their operations
are not credited towards recycling, because their material stream is collected separately from other wastes
and recyclables. However, prior to the operation of these facilities, these materials were part of the MSW
stream, so it would be conceivable that in the future, these facilities are classified as specialized MRF’s,
and thus their throughput would qualify as recycling. For the purposes of this project, these facilities
were not considered, due to their relatively small scale and current classification. However, in the future,
it may be necessary to consider such facilities as a part of the SWM solution. Due to the simple design of
these facilities, it is feasible that these operations occur on a utility scale by the 2020 deadline.
3.2.6. Emerging Disposal Technologies
There are a variety of emerging SWM technologies under development by several private firms and
researchers. Amongst these are various gasification and liquefaction schemes, plasma-arc treatment, solid
fuel production, bioreactor landfills, and various high-technology MRF proposals. Although none of
14
these emerging alternatives are presently in operation at a utility scale in the State, several are already in
use at experimental facilities, and have achieved a high level of safety, reliability, and economic
performance. Therefore, in the future, these emerging technologies may need to be considered part of the
SWM solution as well. However, due to the time required to site, design, permit, and construct such
facilities, it is unlikely that any of these alternatives will be in use by the 2020 deadline.
3.2.7. Transfer Mechanisms
The transfer of waste from the collection to the disposal system, and amongst facilities within the
processing and disposal system, is a significant source of both cost and complexity in the design and
operation of an effective integrated SWM solution. Many factors, such as the means of collection,
number of storage areas, and other mechanical aspects of trucks impact the operation and performance of
transfer systems. This area is a significant field of research and development in and of itself, but falls
beyond the scope of this project. Therefore, based on a survey of the Miami-Dade County SWM fleet,
considered typical of those found throughout the State, transportation costs were set at $1.55 per ton per
mile, including both variable (fuel, wear and tear, etc.) and fixed (labor) costs. Greenhouse gas emissions
from transfer operations, although significant, were disregarded, due to the wide variance in emissions
between older Diesel, modern Diesel, and natural gas vehicles, all of which are in widespread service
throughout the state, and the lack of fleet-specific data availability statewide.
3.3 Generation Unit Inventory
A total of 476 cities were identified as the generation units. Generation rates were calculated by
multiplying city population by the average county per-capita generation rate, as reported by the FDEP’s
2010 Annual Report. Generation units were assigned a point location in the center of each city, and
unincorporated regions of each county were assigned a point location in the geographic center of the
county. The current recycling rate of each generation unit is estimated based on the FDEP’s 2010 Annual
Report, and the waste allocation destinations were obtained from municipal government’s web sites or via
email and telephone contact.
4. PROPOSED METHODOLOGY
4.1 Overview of the Proposed Simulation-Based Decision-Making Framework
Integrated solid waste management systems are systems that provide for the collection, transfer, and
disposal or recycling of waste materials from a given region. These systems handle a wide variety of
15
materials and require numerous specialized facilities and resources, including collection and transfer
fleets, transfer stations, recycling facilities (MRF), and disposal facilities (landfill and/or WTE).Amongst
the Integrated SWM systems in operation in the State of Florida, the ownership and operational structure
of these resources varies widely, from systems in which the vast majority of resources are owned by a
strong public utility, to those in which all resources are owned by numerous private firms, and rich and
dynamic linkages exist both amongst these firms and between them and the generation units. In addition,
many counties do not contain facilities for the entire SWM lifecycle. In this case, dependencies exist
between counties, which must be identified and included in the framework to ensure accuracy and
feasibility. Furthermore, Integrated SWM systems are subject to a level of uncertainty that is exceptional
amongst large-scale systems. Uncertainties exist both at the system level, in terms of waste generation,
regulatory environment, and public/private relations, as well as at the facility level, in terms of capacity
availability, economic performance, and relationships with both the generation units and regulatory
authorities.
Therefore, in order to accurately simulate and optimize such a diverse variety of highly complex
and large-scale systems, the proposed framework must be capable of capturing a variety of ownership
models, economic and operational relationships, transfer operations, and facility characteristics.
Furthermore, the proposed solution must be capable of handling widespread and significant uncertainties,
accepting dynamic inputs, and scaling from the small county level to the regional or multiregional level.
As a framework, the proposed solution must also provide all tools necessary for the simulation and
optimization of such diverse and widespread systems, in a user-friendly and portable package, with
reasonable computational burden.
The proposed simulation-based decision-making framework consists of three main modules: a
database, an assessment module, and a resource allocation optimization module. In the database,
historical data from a wide variety of credible sources, including the FDEP, EPA, county governments,
private firms, and past Hinkley Center projects is stored in a structured manner, and noise is filtered out of
the data. In the assessment module, users are prompted via a graphical user interface (GUI) to define a
study region of one or more counties. Based on this input, the assessment module polls the database for
the generation units and facilities relevant to the study region, as well as existing operational and
economic linkages between them. The assessment module then carries out a parameterization, in which
the human-readable data is converted to a machine-readable format, for use by the model in the resource
allocation optimization module. An integrated Geographic Information System (GIS) in the assessment
module also provides actual road link distances of all relevant linkages identified in the database. The
resource allocation optimization module contains a hybrid agent-discrete model, which builds the
appropriate agents and linkages based on parameterized data provided by the assessment module, via a
16
collection of automated java-based constructors. An embedded optimization mechanism then
simultaneously optimizes all decision variables for the study region, and a performance analysis
mechanism reviews the operational feasibility, cost, greenhouse gas emissions, and recycling rate
achieved by each candidate solution. The most optimum combination of decision variable values is then
sought out, for any arbitrary objective function. For the purposes of this study, that objective is set to
achieving the highest recycling rate possible with existing infrastructure, in order to achieve the 2020
75% recycling goal with minimum cost. Figure 4.1 provides an example mass flow diagram of the SWM
system in Miami-Dade County, providing a representative depiction of the complexity and inter-
connectedness present in such systems. Figure 4.2 provides an overview of the proposed framework.
17
Figure 4.1: Mass flow diagram for Miami-Dade County SWM System
C & D Debris Yard Debris Garbage Trash Process Fuel
Waste Collection Facility 1 3,982,082 tons
Debris Yard
Trash
Food
Waste
Commercial Metals Glass Aluminum
and Plastics
Paper Process
Fuel
Transfer Station
2 541,776 tons (97.9%)
Trash & Recycling
3 4
Materials Recovery
5 Home Chemical Collection Center
553,280 tons
(13.9%)
160,887 tons
(4.1%)
1,024,101 tons
(25.7%)
690,557 tons
(17.3%)
183.6 tons
(0.0004%)
Resources Recovery Waste-to-Energy 8
399,212 tons
(73.8%)
53,692 tons
(33.4%)
Mixed Waste Composting
7
Mulching
6
Negligible
(Private Company at
Virginia Key)
Soil Conditioner
[material added to a
soil to improve its physical properties]
Negligible
Other Landfill Facility
9
Medley Landfill
10
South Dade Landfill Transfer Station
11
North Dade Landfill Transfer Station 12
Combustion with Power Generation
13 14
Combustor Material
(Mainly Metal)
Recycled
21,781 tons
(1.47%)
Recycling Collection Vendors
690,557 tons (100%)
183.6 tons
(100%)
Manufacturing
Hazardous
Waste Disposal
1,477,005 tons (100%)
Negligible
South Dade Landfill Gas Recovery Facility
15
North Dade Landfill Gas Recovery Facility
16
Sequencing Batch Reactor
17
South Dade Landfill Restored Wetland 18
North Dade Landfill Restored Wetland
19
362,253 tons
(24.5%)
Gas
Gas
Pretreated
Water
(Leachate)
Waste Water Treatment
21
Gasification 22
Refuse Recovery Facility
20
Energy Ash
Landfill
940,979 tons
(63.7%)
151,992 tons
(10.3%)
Biomass Fuel Off Site Combustion
Derived Fuel
Unders
Rejects
Fines
Tires
Non-processables
66,253 tons
(18.3%)
82,044 tons
(22.6%)
147,816 tons
(40.8%)
5,022 tons
(1.4%)
239 tons
(0%)
33,148 tons
(9.2%)
27,731 tons
(7.7%)
Water
Gas
644,721 tons
192,626 tons
712,338
tons
Reusable
Product
Negligible
623,680 tons
(15.7%)
248,820 tons
(6.2%)
93,268 tons
(17.3%)
584,654 tons
(14.7%)
6,375 tons (1.1%)
42,921 tons (7.9%)
95,877 tons (2.4%)
Water and Energy
53,692 tons
(33.4%)
Collection Waste Transfer Waste Disposal Legend: Notes:
1. Total Waste: 3,982,082 tons; Recyclable: 690,557(17.73%); Waste: 3,291,525(82.27%)
18
Figure 4.2: Proposed simulation-based decision-making framework for integrated solid waste
management systems
4.2 Database
The database module contains a structured database of all SWM facilities and generation units in the State
of Florida, as well as all economic and operational linkages between generation units and facilities and
amongst facilities. Obtaining a complete and accurate dataset presented significant challenges in and of
Database Parameters Control Parameters
Environments
Functions
Agents
SMART LINKING
AGENT-BASED
MODEL
Ind
ivid
ua
l P
rop
ert
ies
Co
llec
tive
Be
ha
vio
rs
Request for Information
Maps
TRANSFER
STATION
MRF
WASTE TOENERGY
LANDFILL
Ma
teri
als
to
MR
F
Ma
teri
als
to
WT
E
Ma
teri
als
to
La
nd
fill
Ma
teria
ls to
MR
F
Ma
teria
lsto
WT
E
Ma
teria
ls to
La
nd
fill
Ma
teria
ls to
TS
M
ate
rials
to L
an
dfill
HISTORICAL PARAMETERS
WASTE TRANSFER &TREATMENT
REAL SYSTEM
MODULAR HYBRID AGENT-DISCRETE FRAMEWORK
STRATEGIC SOLUTION
Recycling Rates Environmental Impact Financial Responsibilities
Determination of the Optimal Strategic Solution
Location Type
Capacity
Env. Impact
Costs
Owner
Contracts
Contracts
Location
Type
Capacity
WASTE
COLLECTION
Transfer
MRF
WTE
Landfill
City A
City B
City C
ADAPTIVE GIS
Location
STRUCTURED DATABASE
Control Variables
Events
Facilities
Owner
Env. Impact Costs
Facilities
Contracts
Waste Produced
19
itself, and the data was obtained via an extensive and in-depth data collection process. The project team
reviewed over 500 documents from the FDEP, EPA, and various county and municipal governments, and
made over 400 telephone calls to municipal and county governments, public utilities, private SWM
contractors, and facilities themselves. A total of 302 facilities and 476generation units were included in
the database, constituting all such units in the State of Florida. Each generation unit and facility was
provided with up to four linkages: two for recyclables and two for wastes. This was found to be sufficient
to capture all existing arrangements in the State.
Data is stored in a human-readable format in a SQL database. A graphical user interface (GUI) is
provided to facilitate updates and additions once the framework is published, and a programmatic
interface is provided for interfacing with the assessment module. Figures 4.3 and 4.4 illustrate the GUI
provided for the database module and its structure, respectively. Generation rates, facility parameters,
and all other data was converted from myriad reporting conventions to a standardized format, in which
waste generation and facility capacity is reported on a daily basis, assuming 7-day-per-week collection
and operations. Although this schedule is not utilized in many of the systems considered, the average
used to prepare the database ensures that results will be comparable to those prepared under a five or six
day-per-week scheduling regime. The actual schedules used by facilities vary widely, with some opening
as early as 4:00 AM, and other remaining open as late as 10:00 PM. Table 3 provides an example from
Miami-Dade County of the varied facility scheduling schema used statewide.
Table 3:Operation hours of DSWM facilities in Miami-Dade County
Facilities Hours of Operation
Transfer Station
Central Transfer Station 7:00am-5:00pm,Mon.-Sat.
West Transfer Station 7:00am-5:00pm,Mon.-Sat.
Northeast Transfer Station 7:00am-5:00pm,Mon.-Sat.
Landfill
South Dade Landfill 7:00am-5:00pm, 7 Days
North Dade Landfill 7:00am-5:00pm, 7 Days
Ashfill 4:00am-6:00pm, Mon.-Fri.,
7:00am-5:00pm,Sat.
Resource Recovery
Facility
Miami-Dade County Resource Recovery Facility
(RDF-Type Waste-to-Energy)
4:00am-6:00pm, Mon.-Fri.,
7:00am-5:00pm,Sat.
Trash and Recycling
Centers
North Miami Dade TRC, Norwood TRC, Palm
Springs North TRC, West Little River TRC, Golden
Glades TRC, Sunset Kendall TRC, Snapper Creek
TRC, Richmond Heights TRC, Chapman Field TRC,
Eureka Drive TRC, West Perrine TRC, Moody Drive
TRC, South Miami Heights TRC
7:00am-5:30pm, 7 Days
Home Chemical
Collection Centers
West Dade HC2 Center
South Dade HC2 Center 9:00am-5:00pm,Wed.-Sun.
Material Recovery
Facility Waste Services of Florida-Miami 20 hours per day
20
Figure 4.3: Database structure utilized in SQL-based database module
Figure 4.4: Screenshot of the graphical user interface for the database module
21
4.3 Assessment Module
Upon initialization, the assessment module presents users with a graphical form. This form prompts users
to select one or more counties to define a study region, and is depicted in Figure 4.5. Each county is
assigned a region, as defined by the FDEP, and illustrated in Figure 4.6. Users are prohibited from
selecting counties in different regions, and inter-county dependencies are detected from the database
module and enforced. When a user selects a county, the assessment module automatically polls the
database for all relevant inter-county dependencies. Inter-county dependencies are defined as
relationships in which one county either sends waste to another for disposal, or receives waste from
another for processing and disposal. In either case, both counties must be included in simulation runs, in
order to provide disposal facilities for all waste and include all input streams to the study region. Figure
4.6 also provides an example of inter-county dependencies, in which Holmes, Calhoun, and Leon
Counties deliver waste to Jackson County, Washington County sends waste to Walton County, and
Wakulla County sends waste to Leon County. Counties required to satisfy inter-county dependencies are
added to the study region automatically by default, supersede the single-region requirement, and are
enforced under all circumstances. The enforcement of inter-county dependencies ensures that under all
circumstances, an operationally-feasible and realistic study region is defined.
Figure 4.5: Screenshot of the assessment module’s graphical user interface
22
Figure 4.6: Map of the FDEP regions and example system of inter-county dependencies
Following completion of the user form, the assessment module polls the database for all facilities
and generation units in the study region. These entities, to become agents in the hybrid agent-discrete
model in the resource allocation mechanism, are loaded, and a parameterization is carried out, in which
the human-readable data in the database is converted to a machine-readable format for use by the model.
During this process, a smart-linking mechanism creates operational and economic linkages between
generation units and facilities and amongst facilities. These linkages accurately capture the diverse and
dynamic relationships between these entities found in the real system, in terms of both cost and tonnage.
By utilizing an automated, data-based smart-linking mechanism, as opposed to providing binary or
continuous variables for all possible linkages, as is done in earlier models, the number of decision
variables is reduced by one or more orders of magnitude, reducing computational burden compared to
other solutions found in the literature. This reduced computational burden allows the analysis of larger
study regions with the proposed tool.
An integrated geographic information system (GIS) is also included in the assessment module.
This tool determines the road-network distance of linkages created by the smart-linking mechanism, using
road network data collected by the United States Census Bureau in the TIGER (Topologically Integrated
Geographic Encoding and Referencing)/Line system. The road network dataset consists of almost one
million roads, and a total of 469,912 possible routings were identified statewide for the existing database
23
linkages. Route length data is incorporated into the linkages during their creation, and provides the model
in the resource allocation optimization mechanism with accurate transportation cost, transportation-
caused greenhouse gas emission, and transportation time information for each linkage. Figure 4.7
provides a screenshot of the route-determination algorithm used by the integrated GIS.
Figure 4.7: Screenshot of the route-determination utilized by the integrated GIS
4.4 Resource Allocation Optimization Module
The resource allocation optimization mechanism contains a novel hybrid agent-discrete model of the
SWM system for the selected study region. The hybrid agent-discrete modeling topology was developed
for this project due to the unique nature of modularized SWM systems, which require the agent-specific
detail of agent based models, but also feature intrinsically discrete operations. The model initializes by
running a series of automated constructors, which obtain parameterized data from the assessment module,
and compile the agent-discrete model of the study region during the initialization sequence. The use of
automated constructors and real-time compilation both allows the model to acquire a general form,
suitable to many different systems via the constructors, and ensures that the model is linked to the most
24
recent version of the database content, thus allowing changes made following the publication of the
framework to be included in simulation runs without hard coding. The generality allowed by this
structure is a first amongst solutions found in the literature, and dramatically expands the useful scope of
the proposed solution. The ease of update provided by real-time-compilation allows proposed framework
to be adapted to suit the needs of any stakeholder to the SWM system, and to analyze various alternatives,
which could be programmed into the database module via the included GUI.
Typically, discrete-event simulation would be utilized for large-scale system modeling. Discrete
event models are commonly used for industrial purposes, and the model used in Case Study 1 was a
discrete event model. These models accurately and consistently capture inherently discrete processes,
such as transfer truck movements, facility processing, and similar operations. However, discrete event
models only provide accurate performance analysis at the system level, which would be unacceptable for
modularized systems. In such systems, facilities are owned by numerous private firms, each of which
has unique operational and economic constraints and objectives. Hence, operational, economic, and
environmental performance must be quantified, measured, and analyzed on a per-facility basis in
modularized systems. These models are also unable to capture the dynamics and decisions made during
the interaction of various entities (contractors or counties) simultaneously, and do not allow multiple
events to occur simultaneously, in any portion of the model. Furthermore, discrete-event simulation
would have required hard coding for each study region, and the linkages within, providing control with
binary or continuous variables, or a similar control structure. These limitations would have made
arbitrary study region creation impossible, limited the size of practical study regions, and required a
library of pre-compiled models for each region or county.
Agent-based simulation is growing commonplace for the modeling of social systems and other
interaction-intensive systems. The primary strength of agent-based modeling is that performance
(operational, cost and environmental) can be tracked both on an agent-specific (facility or generation unit)
and global basis, and agents can be constructed programmatically. This allows optimization for either
global or agent-specific objectives and accurate reporting for all agents. Furthermore, agents can interact
amongst each other with controllable autonomy. This allows the use of the smart-linking mechanism, and
allows agents to be generated programmatically. Furthermore, the capability of programmatic agent
generation facilitates an arbitrary study region, as the appropriate agents can be loaded by the assessment
module in real-time.
However, agent-based models lack the ability to accurately capture intrinsically discrete events,
such as transfer truck arrivals and departures, and facility processing, which are integral to SWM
infrastructure. Therefore, in this project a hybrid agent-discrete modeling technique has been developed.
This hybrid module is initialized much like a traditional agent-based model, in which both the generation
25
units and facilities are loaded as agents, each with the relevant generation rate or capacity and cost data
from the database. However, unlike typical agent-based models, agents for collection and transfer trucks
are also created.These transfer agents carry out the operational and economic links between the facilities
and generation units, carrying waste and transferring costs between these points. The hybrid agent-
discrete framework allows these transfer agents to create a discrete event at the moment they load and
unload wastes. The execution of this discrete event allows the accurate capture of the discrete processing
events, effectively providing discrete event simulation, and moving material from generation agents
through transfer agents and finally to a final disposal (landfill, WTE, or MRF) agent. Another discrete
event, set to occur daily, provides the generation units with their daily generation and updates feasibility
status and queues at facilities. Essentially, all of the features of discrete-event simulation are provided in
the environment of an agent-based model. Figure 4.8 provides a screenshot of the hybrid agent-discrete
model in operation. The blue and green squares represent generation units and facility agents,
respectively, while the trucks carry waste and transfer costs between these agents, on the linkages defined
by the smart-linking mechanism. Figure 4.9 details the performance analysis mechanism included in the
model, which monitors operational, economic, greenhouse gas emission, and recycling performance in
real-time with the simulation.
Figure 4.8: Screenshot of the hybrid agent-discrete model in operation
26
Figure 4.9: Screenshot of the real-time performance analysis mechanism utilized in the resource
allocation optimization module
4.5 Case Studies
4.5.1. Case Study 1: Miami-Dade County
For case study 1, a discrete-event model was constructed of the SWM system utilized in Miami-Dade
County. The Miami-Dade County SWM system is unique amongst most found in the State in that it is
monolithic in nature, with the Miami-Dade County Department of Public Works and Waste Management
owning the majority of the disposal capacity and coordinating operations at the county level. The system
includes three landfill facilities, one large WTE, and one MRF, as well as several neighborhood recycling
centers and related infrastructure, and serves the most populous county in the State. Figure 4.10details
the infrastructure present in Miami-Dade County’s SWM system.
27
Figure 4.10: Miami-Dade County SWM system
A discrete-continuous modeling topology was developed and implemented, in which both
inherently discrete events, such as truck movements, and continuous variables, such as waste allocation
rates, were both captured accurately. The model considered recycling rate, operational feasibility,
operational cost, and transportation cost, and all possible linkage road distances were hard-coded, feasible
due to the low number of linkages in the single county. The inability of discrete-event models to simulate
simultaneous events was addressed allowing up to 1000 events per second, achieving an approximation
which approaches simultaneous for the study region. Experiments were carried out over 3, 6, 9, and 12-
year intervals, under both constant waste generation and 3% annual growth in generation. Results
indicated that it would not be possible to achieve the 75% recycling goal with existing infrastructure
present in Miami-Dade County, even if a cost-no-object objective structure were implemented.
Furthermore, the results found that if waste generation were to increase at 3% annually, the maximum
28
possible recycling rate would decrease significantly by the year 2020, to under the 30% rate achieved at
present. Table 4provides the most optimum performance possible with the existing infrastructure with
3% growth in annual waste generation, and an objective to maximize recycling, cost-no-object.
Table 4: Results from Case Study 1
Planning
Horizon
(Years)
Average waste
processed
(Million Tons)
Average
waste
recycled
Average
cost
Average annual
waste processed
(Million Tons)
Average
annual waste
recycled
Average
annual
cost
Average
annual
recycling rate
3 6.17 1.62 $366.4M 2.06 0.54 $122.1M 26.17%
6 12.81 3.35 $761.1M 2.14 0.56 $126.8M 26.13%
9 20.04 5.24 $1191.9M 2.23 0.58 $132.4M 26.14%
12 27.67 7.23 $1642.6M 2.31 0.60 $136.9M 26.13%
4.5.2. Case Study 2: Hillsborough County
Although the Miami-Dade County is the most populous county in the State, it is relatively unique in its
monolithic SWM organization. Therefore, a second case study was carried out for modularized systems,
which constitute the majority of SWM systems in the State. Hillsborough County was selected for this
case study, due to its population of over 1 million, variety of landfill and WTE facilities, and heavy use of
transfer stations. Figure 4.11 details the SWM infrastructure present in Hillsborough County.
A total of four generation units and 13 facilities, of which 5 were transfer stations, were
incorporated into the case study. Figure 4.11 depicts the infrastructure in use in Hillsborough County.
The hybrid agent-discrete model, as detailed in sections 4.1 – 4.4, was utilized for this case study, and a
simulation period of 2012 – 2020 was selected, with no waste generation growth. Experiments were run
with both with the optimization priority set to maximize recycling and to minimize total cost. Figure
4.12provides the results obtained with the former objective function, and Figure 4.13provides the results
obtained with the latter. The difference in format between these results and those from case study 1 is due
to the progression in model development, as the hybrid agent-discrete model provided greater levels of
detail than the earlier discrete-event model.
In Figures 4.12and 4.13, each dot represents an operationally feasible solution generated by the
resource allocation optimization module. The inclusion of multiple solutions at different levels of cost
and recycling performance provides decision-makers a spectrum of options, in lieu of the single most-
optimum point produced in case study 1. The line represents a maximum performance curve (Pareto
optimality): any combination of cost and recycling rate along the line is feasible and represents the most
optimum performance of the system. Combinations above the line are feasible, but sub-optimal, while
those underneath the line are infeasible. Thus, the results indicate that the maximum feasible recycling
29
rate for Hillsborough County is 34%, and that annual cost does not vary significantly amongst all feasible
solutions.
Figure 4.11: Hillsborough County SWM system
Figure 4.12: Results from Case Study 2for
maximization of recycling rate
Figure 4.13: Results from Case Study 2 for
minimization of total cost
$155
$160
$165
$170
30% 31% 32% 33% 34% 35%
Annual
Co
st (
Mil
lio
n D
oll
ars)
Recycling Rate
Hillsborough County:
Maximize Recycling Rate
$150
$160
$170
$180
$190
$200
$210
$220
$230
$240
25% 26% 27% 28% 29% 30%
Annual
Co
st (
Mil
lio
n D
oll
ars)
Recycling Rate
Hillsborough County:
Minimize Total Cost
30
5. EXPERIMENTS AND RESULTS
A series of experiments was carried out in each of the six FDEP regions, utilizing the existing
infrastructure present, as detailed in Tables5 and 6. The hybrid agent-discrete model, as developed in
sections 4.1 – 4.4 and utilized for case study 2, was used for all experiments, and a total of five feasible
scenarios were prepared for each region. Each scenario provides a viable and optimum cost-recycling
rate combination for the existing infrastructure, assuming total waste generation remains constant during
the period from 2012 – 2020. Inter-county dependencies, including those which cross region boundaries,
were enforced as elaborated in section 4.3.
Table 5: Agent count per region
Region Facilities
Generation Units Landfill WTE MRF Transfer Station
Southeast 12 7 29 21 125
South 5 1 8 6 18
Southwest 12 5 21 13 78
Central 10 1 18 15 90
Northeast 11 0 21 37 88
Northwest 11 2 21 13 77
Table 6: Agent capacity and generation per region (tons per day)
Region Facilities
Generation Units Landfill WTE MRF Transfer Station
Southeast 57,475 11,520 26,858 21,645 23,412
South 4,080 636 3,465 1,540 3,900
Southwest 13,220 9,322 8,985 13,100 18,535
Central 14,470 350 7,210 7,230 16,593
Northeast 14,720 200 11,840 7,860 9,127
Northwest 5,982 3,500 5,714 3,140 5,387
5.1 Southeast Region
The southeast region is, by a significant margin, the most populous in the State, including the major
metropolitan areas of Miami, Fort Lauderdale, and West Palm Beach. The region includes Broward,
31
Martin, Miami-Dade, Okeechobee, Palm Beach, and St. Lucie Counties. Glades and Monroe counties
were also included in these experiments, due to their inter-county dependencies with this region. The
region under study produces approximately 23,412 tons of MSW daily. Figure 5.1 shows the feasible
scenarios developed for the southeast region.
Figure 5.1: Results from the Southeast Region
None of the scenarios carried out during the experimentation yielded a 75% recycling rate or
FDEP recycling rate for this region. This indicates that the existing infrastructure in this region lacks the
capacity to achieve this goal. It should also be noted that, as indicated by Table 6, total MRF capacity in
this region exceeds 75% of total waste generation. Therefore, all MRF capacity is clearly not being
utilized, and the first recommended actions would be to better utilize this capacity. Low utilization of
these resources may either be due to either ineffective diversion and collection programs, in which case
an insufficient volume of dual or single-stream recyclables is available, or due to the contractual
environment, in which case the linkages between MRF capacity and generation units may be sub-optimal.
Although the proposed framework is unable to determine which is the case, in either scenario, the most
cost-effective means of increasing recycling performance would be to better utilize these facilities.
The experiments yielded an FDEP recycling rate significantly greater than current performance,
which is approximately 30%. The significant disparity between the FDEP and other recycling rate
indicates that this gain was actualized from with WTE facilities. Therefore, for this region, another cost-
effective means of achieving the 75% FDEP recycling rate would be to increase WTE capacity, as once
WTE usage reaches 50% of total disposal, the recycling credits from electrical generation are doubled.
580
585
590
595
600
15 20 25 30 35 40 45 50 55
Aver
age
Annual
Co
st (
Mil
lio
n $
)
Recycling Performance in Southeast Region
Recycling Rate Recycling Rate(FDEP)
32
Furthermore, adding WTE capacity eliminates the impact of the strength of the market for recycled
materials, which is widely variable and strongly elastic, and potential public resistance to modified
collection and billing structures necessary to achieve greater diversion rates.
As the southeast region includes the most urbanized areas of the state, it is the most compatible
with WTE facilities, as evidenced by the presence of almost half the State’s WTE facilities in this region.
Furthermore, due to its urbanization, this region likely has the most developed power grid facilities
capable of receiving electricity from such a facility. Therefore, additional WTE capacity is recommended
for this region in order to achieve the 75% recycling goal.
5.2 South Region
The south region contrast sharply with the former, as it is the least populated region in the State, and has
only one small WTE facility. This region under study includes Charlotte, Collier, Hendry, Highlands,
and Lee Counties, and produces less than 4,000 tons of MSW daily. Figure 5.2 shows the feasible
scenarios developed for this region.
Figure 5.2: Results from the South Region
Much like the former region, the experiments indicated that the existing infrastructure present is
incapable of achieving the 75% recycling goal. Likewise, Table 6 indicates that sufficient MRF capacity
is present to achieve the 75% recycling goal without WTE recycling credits, indicating that the same lack
of MRF capacity utilization present in the southeast region was present. The dispersed nature of the
generation units may make the optimal usage of numerous MRF facilities logistically difficult or
infeasible, or frustrate the implementation of diversion programs and public education, resulting in the
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75
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Aver
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Annual
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st (
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lio
n $
)
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Recycling Rate Recycling Rate(FDEP)
33
low utilization of MRF capacity. Continuing the similarities with the former region, FDEP recycling
rates were significantly higher than the other recycling rate figure, indicating that, although small, the
WTE capacity was once again making a rather large impact on recycling rates.
Therefore, despite clear and fundamental disparities in constitution compared with the southeast
region, the operational and recycling performance of the south region follows very similar trends. Hence,
the recommendation for this region is also to add WTE capacity. Due to the low waste generation of this
region, it is feasible that a single facility is all that is needed to achieve the 75% recycling goal, owned
either by a public regional consortium or a private firm. As SWM budgets are typically low in these low-
population areas, a private firm may be preferable, as through a long-term put-or-pay agreement, the bulk
of construction costs may be borne up-front by the private firm. However, the transfer costs that would
arise from such an arrangement may be substantial, due to the long route lengths that would result. This
is the trade-off to avoiding the logistical difficulties of utilizing numerous dispersed facilities in this low-
population-density region, and the use of transfer stations and rail infrastructure should be investigated in
order to reduce these transfer costs.
5.3 Southwest Region
The southwest region is also a relatively high-population region, although its population is significantly
more dispersed than that of the southeast region. This region includes Citrus, Desoto, Hardee, Hernando,
Hillsborough, Manatee, Pasco, Pinellas, Polk, and Sarasota Counties, including the major metropolitan
areas of Tampa and Sarasota. Waste generation is marginally less than that of the southeast region, at
18,535 tons per day. Figure 5.3 shows results from this region.
430
435
440
445
30 35 40 45 50 55 60
Aver
age
Annual
Co
st (
Mil
lio
n $
)
Recycling Performance in Southwest Region
Recycling Rate Recycling Rate(FDEP)
34
Figure 5.3: Results from the Southwest Region
The southwest region has a substantially lower proportion of MRF capacity than the former two
regions, although the recycling rate and FDEP recycling rate figures produced by the experiments were
marginally higher. This indicates that this region is achieving a greater utilization of existing MRF
capacity, and thus has implemented effective collection and diversion programs to facilitate this.
However, as the experiments failed to achieve an FDEP recycling rate of 75%, the existing infrastructure
is still not capable of reaching the 75% goal for 2020. In light of the apparent presence of effective
diversion and collection programs for recyclables, and viable market conditions, it would be advisable,
for this region to either invest in additional MRF capacity, as it is significantly less expensive than WTE
capacity, or develop transfer arrangements to utilize excess MRF capacity in neighboring regions. Before
utilizing MRF capacity in neighboring region, transfer costs must be carefully considered, due to the
potentially long arc lengths. The use of transfer stations or railroad infrastructure should be evaluated in
order to reduce transfer cost, if the use of out-of-region MRF’s is selected..
5.4 Central Region
The central region produces marginally less waste than the southwest region, and likewise has a more
dispersed population than the southeast region. This region includes Brevard, Indian River, Marion,
Lake, Orange, Osceola, Sumter, Seminole, and Volusia Counties. Figure 5.4 shows the experimental
results from this region.
Figure 5.4: Results from the Central Region
295
300
305
310
315
320
325
25 30 35
Aver
age
Annual
Co
st (
Mil
lio
n $
)
Recycling Performance in Central Region
Recycling Rate Recycling Rate(FDEP)
35
The central region had the lowest FDEP recycling rates statewide. This performance, coupled
with the much higher proportion of MRF capacity (greater than 40% of generation), clearly indicates that
either the diversion and collection programs or the contractual structures, or both, utilized in this region
have significant deficiencies. These deficiencies are possibly due to the relatively dispersed nature of the
population in this region, although the southwest region has achieving substantially greater capacity
utilization with a similarly dispersed populace. The closeness of FDEP recycling rates to recycling rates
is consistent with the minimal WTE capacity in this region.
In order to increase recycling performance, the first priority in this region should be to increase
utilization of existing MRF capacity. Achieving this would require modifications to either the diversion
program, is insufficient recyclables are available, or to the contractual linkage structure, if the local and
regional diversion systems prove to be sufficient. Due to the low utilization of existing MRF capacity,
utilizing out-of-region MRF capacity is not recommended, as it would carry higher transfer costs than
already available capacity in-region. If increasing the utilization of these resources is infeasible, due to
physical distance, inadequate transfer stations and fleets, or lack of viable markets for recycled materials,
then the most cost-effective means to add recycling performance would be investment in WTE capacity.
This region has sufficient generation rates to justify multiple large-scale WTE facilities, which obviates
the potentially high transfer costs that were of concern in the south region.
5.5 Northeast Region
The northeast region falls midway between the sparsely populated south region and most urbanized
southeast region. This region includes Alachua, Baker, Bradford, Clay, Columbia, Dixie, Duval, Flagler,
Gilchrist, Hamilton, Jefferson, Lafayette, Levy, Madison, Nassau, Putnam, St. Johns, Suwannee, Taylor,
and Union Counties, and includes the metropolitan area of Jacksonville. Figure 5.5 provides the results of
experiments in this region.
The data indicates that the northeast region achieved greater utilization of MRF capacity than
many other regions achieving unadjusted recycling rates near 50%, the highest in the state. This indicates
that both the contractual linkages and end market for recycled materials are function. However, the
region has sufficient MRF capacity to cover all waste generation. Therefore, in order to increase the
already commendable recycling rates, the least expensive option would be to revise diversion and
education programs, so that more materials are sent to the clearly functional MRF capacity and markets.
It may be cost-effective to build new MRF capacity close to generation units, if the combination of lower
transfer costs and more efficient new facilities cover construction costs. Waste-to-energy facilities are not
36
recommended for this region, due to the dispersed population and utilization of existing MRF facilities,
and at present, very minimal WTE capacity exists in this region, as evidenced by Table 6 and the
closeness of FDEP-adjusted recycling rates to their unadjusted counterparts.
Figure 5.5: Results from the Northeast Region
5.6 Northwest Region
The northwest region is the second-least-populated, behind the south region, and its population is equally
dispersed. It includes Bay, Calhoun, Escambia, Franklin, Gadsden, Gulf, Holmes, Jackson, Liberty,
Leon, Okaloosa, Santa Rosa, Wakulla, Walton, and Washington Counties, including the Tallahassee
metropolitan area. Figure 5.6 details the results of experiments in the northwest region.
Results from this region varied far more than those from other regions, both in terms of
unadjusted and FDEP-adjusted recycling rates. However, in all cases, much of the MRF capacity
available, which in total exceeds the region’s daily waste generation, was unutilized. The lack of
utilization of MRF capacity is consistent with the other low-population region (south), and suggests that
the logistical and transfer difficulties created by low population density is not conducive to high MRF
utilization. However, in all five scenarios, the difference between unadjusted and FDEP-adjusted
recycling rates was similar. This indicates that WTE facilities are being utilized, although once again, not
all WTE capacity, which exceeds 50% of generation, is used, as such a condition would be reflected in
greater differences between unadjusted and FDEP recycling rates. The combination of low utilization of
both MRF and WTE facilities clearly indicates that there are significant challenges to SWM operations in
low population density regions. Constructing smaller, but more numerous, facilities, closer to generation
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st (
Mil
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)
Recycling Performance in Northeast Region
Recycling Rate Recycling Rate(FDEP)
37
areas, may alleviate the logistical and transportation challenges created by low population density, but
comes at the cost of economic performance, as both the construction and operation of MRF and WTE
facilities enjoy economies of scale, and does not guarantee that markets for recycled materials would exist
in these regions. Utilities considering such an approach should evaluate the costs of extended transfer
networks, including the use of railroads, as opposed to distributed facility capacity, and whether their
recycled material will be viably marketable. Thus, as in the south region, the immediate recommendation
to increase recycling performance for the 2020 75% goal is investment in WTE capacity, in order to avoid
the challenges of developing markets and diversion and collection programs.
Figure 5.6: Results from the Northwest Region
6. DISCUSSION AND CONCLUSIONS
Despite dramatic differences between regions, in terms of both total population and its dispersion, several
trends were clearly manifest in the results of experimentation. Most significantly, the utilization of MRF
capacity was low statewide, in many cases less than 50%. Low utilization of these facilities indicates that
some combination of inadequate diversion and collection programs, sub-optimal contractual linkages, and
weak markets for recycled materials are at play on a statewide basis. These issues are most severe in the
two lowest-population regions: the central and the northwest. This indicates either some or all of these
factors most severely impact small utilities and/or low population density areas.
Diversion and collection systems provide for the separation of recyclables from other waste
materials, and delivery of these separated materials to MRF or other recycling facilities. These systems
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st (
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Recycling Performance in Northwest Region
Recycling Rate Recycling Rate(FDEP)
38
require both utility and public participation, in terms of the sorting of wastes into appropriate containers,
and may utilize either dual-stream operations, in which paper and other recyclables are separated prior to
collection, or single-stream operations, in which recyclables are collected comingled. Single-stream
recycling is the newer and higher-technology approach, as many utilities have found that the higher cost
of single-stream MRF facilities is more than made up for by the increased public participation, due to
greater convenience. However, in either case, diversion systems require public education and outreach
events and suitable collection vehicles. The challenges associated with these resources are greatest in
low-population areas, which typically have low budgets and small fleets. Furthermore, drop-off recycling
programs are also least viable in these low population density situations, as the distances to drop-off
points would need to be prohibitively long, or the collection of numerous, low-usage drop-off points
would be costly. Therefore, the recommendation for all regions is the development of single-stream
diversion programs. Despite the greater cost of single-stream MRF facilities, the experiments have
clearly shown widespread deficiencies in the diversion of recyclables. Single-stream diversion requires
the least public education and fleet resources.
Contractual linkages provide the economic and operational ties between generation units and
processing facilities. These linkages are necessary for wastes and recyclables to be transferred to the
appropriate disposal facilities. Both the southwest and northeast regions, despite lower levels of
urbanization than the southeast region, have achieved the most effective diversion and linkage
arrangements in the state, as evidenced by their leadership in MRF and WTE capacity utilization.
However, on the statewide scale, contractual linkages are also sub-optimal on the large part. These
linkages are intimately tied the viability of markets for recyclable materials, as MRF facilities would be
forced to send recyclables to WTE facilities or landfills should they accept more materials than they are
able to sell. Therefore, the recommendation in this area is the establishment of region-level markets for
diverted materials and recycled materials. An example of this was carried out in Broward County, in
which an Interlocal Agreement (ILA) was established by 26 municipalities, by which the member
generation units agreed to a put-or-pay relationship with Waste Management, Inc., in return for the
construction of two large WTE facilities. By leveraging both the collection of diverted materials and sale
of recycled materials with the large scale, these agreements have brought economies of scale to
generation units which would not have achieved them on their own. Similar interlocal arrangements are
recommended for recycling, in which the scale achieved via collectivity will bring the economic
advantages of large-scale facilities and the marketability advantages of large and consistent volumes of
recycled materials. Essentially, the recommended interlocal arrangements will serve to foster the
operation of large-scale facilities and long-term relationships, with commercially viable market positions,
39
in which both the generation units and private contractors have vested interest in the long-term viability of
the arrangement.
Transfer mechanisms, such as trucks, railways, and barges, as well transfer stations and related
resources, are integral to the operation of contractual linkages. However, given the dispersed nature of
much of Florida’s population, transfer activities are also a significant source of cost for the SWM system,
especially given continuously rising energy costs. These challenges are likely amongst the contributors to
the low MRF utilization and overall recycling performance in low-density regions. This is supported by
the lack of large WTE facilities in these regions, which are typically centralized and capital-intensive
facilities, which require strong transfer linkages to ensure a reliable supply of material. The
recommendation in this area is for the exploration of alternative transfer mechanisms, such as railways
and barges, and the use of county or regional interlocal organizations to fund the construction of the
necessary transfer stations or loading facilities required to utilize these transfer alternatives.
Clearly, the results of the experiments carried out indicate significant deficiencies in the operation
of existing recycling facilities, in terms of diversion, linkages, transportation, and marketability. These
deficiencies are all exacerbated by the low-density and dispersed nature of much of the state, in which
generation units are small and have little market leverage, and transportation costs are high. These factors
frustrate not only the operation of recycling facilities, but also the diversion of recyclables at first.
Therefore, this study recommends the implementation of county or regional-level cooperative
agreements, in which generation units pool their waste streams in order to achieve the economies of scale
associated with large, centralized facilities, and the greater marketability of recyclable good facilitated by
such facilities. However, this proposed solution raises significant issues of transfer costs, and thus it is
recommended that alternative transfer mechanisms, such as railways and waterways, be evaluated during
the formulation of interlocal plans.
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