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An Optimization Model for the Design of a Sustainable
Municipal Solid Waste Management System
by
Ahmed Shaban (https://sciprofiles.com/profile/1163752?utm_source=mdpi.com&utm_me
1  (mailto:please_login) (https://orcid.org/0000-0002-1241-5647),
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2  (mailto:please_login) (https://orcid.org/0000-0001-9241-9121),
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Mathematical Model Formulation
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1 Applying the Model to Egypt
Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum
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63514, Egypt
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2 Conclusions
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Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via
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Italy and ads, to provide social media features and to analyse
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Solid
waste
management is critical to sustainable, healthy, and eco-friendly cities and societies.
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dispose
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recycling
plants, in addition to the transportation system that integrates the different subNecessary
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of Interest
systems.
problem facing decision-makers while designing or reconfiguring a SWMS is to
lens
References
determine
the optimal supply chain network design for such systems in a way that ensures the
Preferences
treatment
and disposal of all daily generated waste at the lowest cost. In this context, this paper
aims to develop a generic optimization model suitable for application in SWMS optimization in
developing
Statisticscountries. A new mixed-integer linear programming (MILP) model is formulated for a
Comment
SWMS configuration that integrates waste generation sources, collection/transfer stations,
recycling plants, incinerators, and landfills. The proposed MILP model is formulated to determine
Marketing
the optimal number and locations of the different facilities, and the optimal flow of waste in the
system that minimizes the net daily cost incurred in the system. The model has been applied in a
case study on the SWMS in Fayoum Governorate, Egypt. The main contribution of this research
Show details
refers both to the theoretical development of a generic MILP model that can be applied to
optimally design the SWMS in developing countries, and to its operational counterpart, as per
the design solutions provided in the SWMS of Fayoum Governorate (Egypt).
Keywords: solid waste
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Introduction
Solid waste represents any substance that may result
from
human activities and
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has
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beMathematical
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municipal solid waste (MSW), which consists of everyday items that
lenspeople use and then throw away such as food residues, cardboard, grass clippings, furniture,
Results and Discussions
Cite
lensclothing,
bottles, appliances, paint, batteries, and many others [3]. The sources of MSW include
Conclusions
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lenshomes,
hospitals, and businesses. The amounts of MSW that municipalities have to
Authorschools,
Contributions
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lensdeal
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Funding are growing considerably due to global economic development, the increase in per
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[4].
Thethey’ve
per capita
share
of your
waste
ranges from 0.11 kg per day to 4.5 kg per day and
that
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from
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of their services.
groups/public/10.3390/su14106345?
lensit
is
expected
to increase
at a rate of 19% by 2050 in high-income countries, and 40% in lowInformed
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to build effective solid waste management systems that are capable to treat and
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of of
theInterest
total amount of daily generated municipal solid waste [6].
Conflicts
lens
There are a multitude of methods and technologies for the treatment and final disposal of
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Comment
Preferences
MSW,
such as open dump, landfills, recycling, incineration, composting, energy generation, and
others [7]. Inadequate disposal of solid waste has several effects on health and the environment
[8,9,10].
The problem of improper and inefficient disposal of solid waste materializes in
Statistics
developing countries due to the lack of effective solid waste management systems in those
countries [4,11,12]. In developing countries, only 10% of municipal solid waste is disposed of in
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a safe manner, while the remaining 90% is disposed of through open landfills and uncontrolled
burning of waste [13]. Therefore, developing countries are striving to address this problem
through establishing effective, economical, and sustainable solid waste management systems. A
details
solid waste management system (SWMS) refers to a combination of various Show
functional
elements
associated with solid waste management (see Figure 1) [14]. A SWMS should facilitate the
collection, treatment, and disposal of solid waste in the community at minimal cost while
safeguarding public health and ensuring little or no impact on the environment and ecosystem.
The elements that make up the system start with waste generation sources, then the generated
waste is sometimes stored in plastic bins, conventional landfills, waste oil drums, large storage
bins, etc. The waste is then collected and transported through collection vehicles to
collection/transfer stations. Waste sorting, which is the process of separating municipal solid
waste into different types, may be performed at collection/transfer stations [4]. In general, it can
Powered by Cookiebot by Usercentrics (https://www.cookiebot.com/en/what-is-behind-powered-byoccur before or after the waste is collected,
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operations of processing/treatment plants (recycling plants, composting, etc.). Processing is
required to change the physical and chemical properties of the waste for energy and resource
recovery and recycling. Recovery involves separating valuable resources from
mixed
solid
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Figure 1. Components of a typical SWMS [14].
Several SWMS configurations have been proposed, modeled, and investigated in the
Marketing
literature
[15,16]. Those configurations vary in the type of facilities and technologies integrated
within the SWMS, depending on the economic situation and strategies of the countries [6,7]. It
has been observed that the proposed SWMS configurations for the developing countries that
details
usually rely upon simple SWMS are different from the ones proposed Show
for the
developed
countries that adopt more effective and sustainable SWMS [3]. In addition, it has been noticed
that the related research to the investigation of suitable SWMS configurations for developing
countries is limited. SWMS configurations in lower-middle income countries (LMIC) often consist
of waste sources, collection stations, composting or recycling plants, and landfills [1,2,17]. In
upper-middle-income countries (UMIC), the configurations are similar to LMIC, in addition to
some different components such as incineration facilities and anaerobic digestion facilities to
convert heat and methane to energy [10,18]. Conventional gasification, pyrolysis gasification,
pyrolysis arc gasification, chemical recycling, and heat recycling are different station
Powered by Cookiebot by Usercentrics (https://www.cookiebot.com/en/what-is-behind-powered-bycomponents of SWMS configuration in UMIC
[19,20]. There is no standard configuration for
cookiebot/)
SWM that suits all cities and countries, and therefore the selected configuration for a certain
country should be related to the economic situation of the country and the strategies adopted in
the country. Moreover, the existing solid waste management systems can be adapted
cope
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lens(maximum profit), considering the different system constraints. The main costs incurred in solid
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related
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of
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SWMS. The decisions also include the selection of the suitable technologies for the different
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determined. The aforementioned design decisions must be determined in light of specific
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optimization
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environmental impact minimization [25]. Most of the existing research considers the cost
minimization models for determining the optimal designs of SWMS configurations, especially in
Statistics
developing
countries that have limited budgets to allocate for solid waste management [2,14].
Significant attention has been devoted to the optimization of municipal solid waste
management
Marketing systems [16,18,26,27]. In this regard, several mathematical optimization models
have been presented in the literature to support the supply chain network design of SWMS
[21,28] Most of the existing models are mathematically formulated as MILP models and have
single objective functions and possess specific assumptions that limit their
to
Showapplications
details
specific cities and countries [15,22,29]. Others have formulated mathematical models that can
consider multiple objectives [19,30,31]. Nevertheless, limited research has been directed to
develop optimization models that can be easily employed or adopted to optimally design solid
waste management systems in developing countries [2]. With respect to Egypt, limited
optimization models have been developed for the potential SWMS configurations that can be
deployed in Egypt. Badran and El-Haggar [2] developed an MILP model for a proposed SWMS
configuration for Port-Said Governorate (Egypt) that consists of waste generation sources,
collection stations, composting plants, and landfills. The model is formulated to determine the
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by Badran and El-Haggar [2] and applied it to determine the optimal SWMS configuration
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lensfor Desoq District, Kafr El Sheikh Governorate, Egypt.
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This research aims to propose a suitable SWMS configuration for developing countries,
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[2,24]. The proposed SWMS
lensconsidering the proposed configurations in the related literature
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Mathematical Model Formulation
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sources, collection/transfer stations,
lensincinerators, recycling plants, and landfills. A generic MILP model has been formulated for this
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SWMS configuration. The model is formulated to determine the optimal number and
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lenslocation of each type of facility, and the optimal number of trucks, and the optimal flow of waste
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indicate the governorate can achieve a considerable daily profit from its SWMS if it
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the capacities of the collection stations and recycling plants. Moreover, the reliance on
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will be limited in the optimally designed SWMS.
lens
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The main contribution of this research includes the development of a generic MILP model
that
can be applied to optimally design solid waste management systems in developing
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References
countries. In particular, the model assumptions most suit the SWMS that can be deployed in
Egyptian cities. However, the model can be readily adapted to apply to other countries. In
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addition,
the optimal designs related to some proposed scenarios for the design of the SWMS of
Fayoum Governorate (Egypt) have been provided.
This paper is organized as follows. Section 2 presents the proposed SWMS configuration
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for developing countries and outlines the problem statement. In Section 3, a new MILP model
for the optimal design of the proposed SWMS configuration is described. Section 4 outlines the
application of the proposed MILP model to optimally design the SWMS
of details
an Egyptian
Show
governorate. The results and discussions are reported in Section 5. The conclusions and future
research are summarized in Section 6.
2. System Description
Waste management systems in the developing countries are limited to collection,
transportation, and disposal, with no capabilities of recycling and reuse [11]. Therefore, most of
the developing countries are striving to adapt their solid waste management systems to cope
with the
growing challenges of the solid waste management problem and to build sustainable
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and effective systems. There is no standard
SWMS configuration that suits all cities and
countries [29]. Ayvaz-Cavdaroglu et al. [8] investigated the optimal mix of municipal solid waste
management technologies (landfilling, composting, recycling, incineration, and anaerobic
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digestion) that should be adopted for the SWMS of Istanbul (Turkey). They formulated
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lensfor a given waste composition and applied the model for the case of Istanbul. Badran and ElIntroduction
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lensHaggar [2] proposed the integration of collection stations with the existing SWMS configuration
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lensSWMS in that governorate. The literature includes several other attempts that have been made
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functional
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recycling plants, and landfills (see Figure 2). A schematic diagram of the proposed
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lensincinerators,
lensSWMS
configuration, indicating the waste flow through the different components and facilities of
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lens
Acknowledgments
Conflicts of Interest
their generated waste to the nearest bins. The collection vehicles collect the waste (e.g., QSCij,
lens
References
QSI
im, QSLil) from those bins and transfer to the assigned collection stations for this district. The
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total daily waste quantity received at the collection stations is distributed to the incinerators
(QCIjm), recycling plants (QCRjk), and landfills (QCLjl), according to a predetermined distribution
Statistics
plan.
The collection stations have to be equipped with the sufficient number of trucks and other
relevant resources needed for achieving the distribution plan. The incinerators are used to burn
theMarketing
received waste in a controlled manner and the remaining ash and residues (QILml) are
transferred to the landfills. The recycling plants attempt to recover most of the valuable materials
in the received waste, while the remaining residues (QRLkl) that can no longer be recovered are
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Figure 2. Proposed SWMS Configuration.
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The solid waste management system should be designed with the sufficient capacities that
can handle the total quantity of daily generated waste. This requires making strategic decision
related
to the design of the supply chain network of the solid waste management system. The
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strategic decisions related to the supply chain network design of SWMS include the
determination of the number, location, and capacities for the different facilities (collection
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stations, incinerators, recycling plants, and landfills). Other important strategic decisions include
the determination of suitable technology for the different facilities, and the required resources to
operate the system, such as the number and type of trucks to allocate for different facilities
details
within the system. The planning decisions related to the optimal flow andShow
distribution
of the
waste throughout the supply network can also be made during the strategic design phase (see
Figure 2). All those decisions have to be made such that the system can handle the total
quantity of the daily generated waste at minimum cost. The total costs incurred in a SWMS can
be divided into capital/fixed costs and variable costs. The fixed costs are the costs needed to
establish the facilities that constitute the SWMS’s supply chain network. The variable costs
include the operating and maintenance costs required to operate the different system’s facilities
and the transportations costs. The recycling plants and incinerators can provide a source of
revenue,
which can then be deducted from the total system cost.
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Introduction
A new mixed integer linear programming (MILP)Order
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for the
proposed SWMS
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configuration
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lenssystem. Some assumptions have been made to facilitate the model formulation, considering the
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lensadopted
assumptions in the relevant models presented in the literature [2]. It is assumed that the
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lenscollection
efficiency at each source i is 100% and the total amount of daily generated waste at
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lenseach
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Indices:
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i
Waste source, where i = 1, …, I.
Necessary
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Conflicts
of location
Interest
j
Potential
of collection station, where j = 1, …, J.
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k
Potential location of recycling plant, where k = 1, …, K.
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m
Potential location of incinerator, where m = 1, …, M.
l
Potential location of landfill, l = 1, …, L.
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Model parameters:
Si
Amount of daily waste generated at source i (ton/day).
Cj
Daily capacity of collection station j (ton/day).
Rk
Daily capacity of recycling plant k (ton/day).
LCl
Daily capacity of landfill l (ton/day).
ICm
Daily capacity of incinerator m (ton/day).
TCSCij
Transportation cost from waste source i to collection station j ($/ton).
TCSIim
Transportation cost from waste source i to incinerator m ($/ton).
TCSLil
Transportation cost from waste source i to landfill l ($/ton).
TCCIjm
Transportation cost from collection station j to incinerator m ($/ton).
TCCRjk
Transportation cost from collection station j to recycling plant k ($/ton).
TCCLjl
Transportation cost from collection station j to landfill l ($/ton).
TCRLkl
Transportation cost from recycling plant k to landfill l ($/ton).
VCCj
Variable cost (O&M) incurred in collection station j ($/ton).
VCRk
Variable cost (O&M) incurred in recycling plant k ($/ton).
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Variable cost (O&M) incurred in landfill l ($/ton).
l
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VCMm
Variable cost (O&M) incurred in incinerator m ($/ton).
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CTCj
System Description
lens
CTIm
Daily fixed
cost of
γ-ton truck allocated to incinerator m ($/day).
Mathematical
Model
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CTRk
Daily
costto
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Daily Discussions
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Daily fixed cost of recycling plant k ($/day).
Conclusions
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FCLl
Daily fixed cost of landfill l ($/day).
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Truck collected
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Revenue
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unit for incinerator m ($/unit).
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Conflicts
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p
Conversion ratio of recycling plant k (%).
k
References
nm
Conversion
Comment
Preferences
ratio of incinerator m (%).
Decision variables:
QSCij
The amount of daily municipal solid waste transported from waste source i to collection station j (ton/day).
QSIim
The amount of daily municipal solid waste transported from waste source i to incinerator m (ton/day).
QSLil
The amount of daily municipal solid waste transported from waste source i to landfill l (ton/day).
QCIjm
The amount of daily municipal solid waste transported from collection station j to incinerator m (ton/day).
QCRjk
The amount of daily municipal solid waste transported from collection station j to recycling plant k (ton/day).
QCLjl
The amount of daily municipal solid waste transported from collection station j to landfill l (ton/day).
QRLkl
The amount of daily unrecovered municipal solid waste transported from recycling plant k to landfill l (ton/day).
QILml
The amount of daily incineration byproducts transported from incinerator m to landfill l (ton/day).
XCj
Total number of γ-ton trucks allocated to collection station j.
XRk
Total number of γ-ton trucks allocated to recycling plant k.
XIm
Total number of γ-ton trucks allocated to incinerator m.
PCj
A binary decision variable where PCj = 1, if a collection station is installed at potential location j, and PCj = 0, otherwise, where
j = 1, …, J.
PRk
A binary decision variable where PRk = 1, if a recycling plant is installed at potential location k, and PRk = 0, otherwise, where
k = 1, …, K.
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PIm
PLl
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is installed at potential location m, and PIm = 0, otherwise, where m
cookiebot/)
= 1, …, M.
A binary decision variable where PLl = 1, if a landfill is established at potential location l, and PLl = 0, otherwise, where l = 1,
…, L.
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A binary decision variable where Y = 1, if the total amount of daily generated waste at all sources exceeds the total capacity of
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stations and potential incinerators, and Y = 0, otherwise.
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The complete MILP model is formulated as follows:
announcement
K
M
L
⎡
Minimize NDC = ⎢⎢∑ FCCj . PCj + ∑ FCRk . PRk + ∑ FCIm . PIm + ∑ FCL
k=1
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Applying the Model to Egypt
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+ ⎡⎢⎢∑ ∑ TCSC . QSC + ∑ ∑ TCSI . QSI + ∑ ∑ T
i=1 m=1
i=1 l=1
⎣ i=1 J j=1
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K
M
⎡
⎛
⎞
⎛
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Data Availability Statement
⎜
⎟
⎜
⎜
⎟
⎢⎢⎜⎜∑ CTCj . XCj ⎟⎟ + ⎜⎜∑ CTRk . XRk ⎟⎟ + ⎜⎜∑ CTIm . XIm ⎞⎟⎟⎟⎤⎥⎥
+
Acknowledgments
j=1
k=1
m=1
Necessary
⎝
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Conflicts of Interest K
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⎢⎢∑ RRk . pk . ⎜⎜∑ QCRjk ⎟⎟⎥⎥ − ⎢⎢∑ RIm . nm . ⎜⎜∑ QSIim + ∑
−
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⎣ k=1
⎝ j=1
⎠⎦ ⎣ m=1
⎝ i=1
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QSCij + ∑ QSIim + ∑ QSLil = S𝑖,∀i
j=1
m=1
l=1
(2)
Show details
I
∑
QSCij ≤ Cj . PCj ,∀j
i=1
I
J
k
(3)
M
∑
QSLil + ∑ QCLjl + ∑ QRLkl + ∑ QILml ≤ LCl . PLl,∀l
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m=1
(4)
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J
∑
QCRjk ≤ Rk . PRk ,∀k
j=1
(5)
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Data Availability Statement L
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management
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component
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Constraints (3) ensure that the daily amount of waste transferred to each collection station j
cannot exceed the daily capacity of that station. Constraints (4) indicate that the daily amount of
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waste
transferred to each landfill l cannot exceed the daily capacity of that landfill. Constraints
(5) ensure that the daily amount of waste transferred to each recycling plant k cannot exceed the
daily
capacity of that recycling plant. Constraints (6) indicate that the daily amount of waste
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transferred to each incinerator m cannot exceed the capacity of that incinerator. Constraints (7)
relate the number of trucks needed at each collection station j to the total daily amount of waste
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landfills). In other words, this constraint ensures that each collection station j is equipped with a
sufficient number of trucks. The constraints (8) to (9) also ensure that each of the recycling
plants and the incinerators have sufficient number of trucks as well. Constraints (10) ensure that
the daily amount of waste received at each collection station j is equal to the daily amount of
waste distributed from that collection station to the other facilities. Constraints (11) balance the
total daily amount of waste received at each recycling plant k and the daily quantity of rejects
and unrecycled waste that are transferred to landfills for final disposal. Likewise, constraints (12)
balance the amount of waste in and out the incinerators. Constraints (13) to (15) prioritize the
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increases
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rate of about 1.9% [32]. Egypt generates about 10 million tons/year of
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agricultural
residues [33], and about 26 million tons of municipal solid waste annually [4].
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perin capita share of municipal solid waste for the Egyptian governorates varies between 0.3
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lenswaste management systems.
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4.1. Background on Fayoum
Fayoum is one of the Egyptian Governorates that is located about 100 km to the southwest
of Cairo. It has a population of about 3,848,708 in 2020 and a total area of 6068.70 Km2,
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representing 0.6% of the total area of Egypt. Fayoum governorate consists of six administrative
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districts:
Fayoum, Sinnuris, Ibsheway, Itsa, Tamiyyah, and Youssef Al Seddik. The annual
quantity of waste generated in Fayoum from the various waste sources (municipal, agricultural,
industrial and others) is around 1.5 million tons as per the statistics of the year 2018. The
amount of municipal solid waste generated daily amounts to 1950 tons, which
Showconsists
details of 552
tons from urban areas and 1398 tons from rural areas. The waste generation per capita ranges
between 0.33 kg/day in rural areas and 0.63 kg/day in urban areas. The governorate collects
about 1209 tons per day (62%), and about 3% of the daily generated waste is collected by
scavengers, while the remaining amount is left in the streets or thrown to uncontrolled areas.
4.2. Data Collection
The application of the proposed MILP model to optimally design an integrated SWMS for
Fayoum Governorate requires the collection of several data which are required by the model for
the different
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the SWMS. (https://www.cookiebot.com/en/what-is-behind-powered-byThe required data to collect includes the location of
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at each source, the location of current facilities
(collection stations, recycling plants, and landfills) and their capacities, and the potential
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lensdifferent components of the system are also needed to apply the proposed MILP model.
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The current SWMS of Fayoum Governorate consists of six waste sources, five
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six
districts
as follows: Fayoum (S#1), Youssef Al-Seddik (S#2), Sinnuris (S#3), Tamiyyah (S#4),
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lensItsa (S#5), and Ibsheway (S#6). Therefore, Fayoum is considered to include six sources of
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Figure 3. Current SWMS in Fayoum Governorate.
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Table 1. Waste generation sources at Fayoum Governorate.
There are five collection/transfer stations that are distributed throughout the governorate at
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locations: Itsa/Difinnu (CS#1), Tamiyyah (CS#2), Tamiyyah/Kafr-Mahfouz (CS#3),
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Sinnuris (CS#4), and Kom-Oshim (CS#4) [34]. The approximate locations of these collection
stations are marked on the map in Figure 3. The related data to collection stations, including the
fixed and variable costs and capacities, are reported in Table 2. The current capacities of the
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the respective districts. The related fixed and variable costs
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Table
Coststoand
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Fayoum
Governorate has four landfills that are located at Itsa/Qasr Al-Basil (LF#1), Youssef
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Hanna Habib (LF#2), Ibsheway/Ezbet Al-Sanjaq (LF#3), and Kom Osheam
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[34]. The first three landfills can be considered open dump landfills while the last one is a
well-established landfill. The approximate locations of these landfills are marked on the map in
Comment
Figure
3. The required data for the landfills include the capacities and the fixed and variable
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costs. It has been assumed that the current landfills can be adapted into modern landfills with a
capacity of 500,000 ton/year each. This capacity level is suggested in the “Master Plan for
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Integrated Municipal Waste Management” report prepared for Fayoum Governorate [34]. The
fixed and variable costs of the landfills have been estimated based on this official report and
other published cost data in the literature [24,34].
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The data required for recycling plants include potential locations for installing recycling
plants, capacities, fixed and variable costs. Fayoum Governorate currently operates a recycling
plant with a capacity of 130 ton/day, where this amount is sorted and treated into compost. The
governorate plans to extend the capacity of the current plant and its facilities to be able to
conduct sorting operations, produce compost and refuse-derived fuel (RDF) [34]. Therefore, it is
assumed the capacity of the existing recycling plant can be extended to 300 ton/day. The
respective fixed and variable costs, revenue from recycled materials, compost and RDF, and
conversion ratio have been adapted from the literature and official reports related to the SWMS
in Fayoum
Governorate
[24,34].
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The data related to the distances between
the different facilities in the SWMS of Fayoum
Governorate have been collected to estimate the unit transportation cost between the different
facilities (L.E./ton). The unit transportation cost is a function of fuel consumption per Kilometer,
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cost per liter, and travelled distance [35]. In addition, the fixed costs of the trucks that need
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lensto be allocated to the different facilities have been estimated based on the pricing of 10-ton
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theConclusions
SWMS
of Fayoum
Governorate. The proposed scenarios have been investigated using the
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proposed
MILP model, considering the above-described data. The first scenario adopts the
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the model determines the optimal flow of the waste throughout the system.
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Fayoum
and the optimal flow of the waste in the system. The second scenario considers the
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complete
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of Interest
of the SWMS presented in Figure 2. Accordingly, it is assumed that there
lensare potential locations not only for installing collection stations and landfills (as in Scenario #1)
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butComment
also for installing recycling plants and incinerators. Thus, the model decides about the
number and locations of collection stations, recycling plants, and landfills. The incinerators are
excluded
from the analysis due to the lack of adequate data about those facilities.
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The MILP model is implemented and solved using LINGO 18 on a laptop with Intel(R) Core
(TM) i7-6500U CPU @ 2.50 GHz–2.60 GHz processor, and 8.00 GB RAM. The optimal solution
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of the model for both scenarios has been obtained in a few seconds. The optimization results for
the first scenario indicates that the optimal design of the SWMS in Fayoum Governorate would
incur a net daily cost of 143,394.9 L.E./day. The optimal solution suggests four collection
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stations, and a recycling plant and a landfill where their approximate locations are indicated on
the map shown in Figure 4. The detailed optimization results are provided in Table 3 and Table
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Table 4. The daily amounts of waste transported from the collection stations to the
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The optimization results of the first scenario indicate that four collection stations (CS#1,
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total capacity of 2400 ton/day. The optimal allocation of the generated waste at the different
sources to these collection stations and the landfill is detailed in Table 3. The results indicate
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source area except CS#4, which serves four sources and receives a total amount of daily
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lensamount of 300 ton/day, which is equal to the maximum capacity
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The optimal solution of the second scenario proposes to install a recycling plant with a
capacity of 300 ton/day in each of the six potential locations for installing recycling plants. This
indicates that a recycling plant is to be installed close to each waste generation source (district).
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The optimal distribution and allocation plan of the received waste at the collection stations to the
recycling plants and landfills is detailed in Table 6. All the recycling plants are fully utilized, and
each recycling plant is allocated the waste from the nearest collection stations to it. The
remaining amounts of the waste at the different collection stations and the residues/rejects at the
recycling plants are transferred to the landfill. The required number of trucks that are needed for
the collection stations and the recycling plants is estimated as 23 trucks of 10-ton capacity.
A comparison between the two scenarios, based on total daily cost, total daily revenue, and
net daily cost, is provided in Figure 6. The results indicate that although the total daily costs
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increase will be coupled with a considerable
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the recycling plants within their solid waste management systems. However, the decisions
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The MILP model has been used to investigate the impact of recycling plant capacity on the
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recycling plant capacity on the net daily cost in both scenarios is presented in Table 7 and Table
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the total capacity of the recycling plants related to the investigated capacity levels. The optimal
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plants in the
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solution. The model can only provide the optimal flow of the waste from the different collection
station to the recycling plant, for a given capacity level. It is expected that the system will
achieve a daily profit (negative net daily cost) if the recycling plant capacity exceeds 800 ton/day.
Table 7. The impact of recycling plant capacity on net daily cost in Scenario #1.
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Table 8. The impact of recycling plant capacity on net daily cost in Scenario #2.
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This research
chain
References
networks for solid waste management systems. First, a proposed solid waste
Comment
Preferences system (SWMS) configuration that suits the conditions of developing countries
management
(particularly Egypt) has been proposed. The proposed SWMS configuration integrates waste
generation
Statistics sources, collection/transfer stations, recycling plants, incinerators, and landfills. A
new mixed integer linear programming (MILP) model that can be applied to determine the
optimal design of the proposed SWMS configuration, and that minimizes the net daily cost
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incurred in the system, has been formulated. The proposed MILP model is formulated to
determine the optimal number and locations of the different facilities integrated in the system
(collection stations, recycling plants, landfills, and incinerators), and to determine the optimal
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flow of the waste through the system.
The applicability of the proposed MILP model has been demonstrated through a case study
based on Fayoum Governorate, Egypt. The optimal designs for two proposed scenarios for the
design of a suitable SWMS for Fayoum Governorate have been obtained. The first scenario
considers the current SWMS configuration in Fayoum while the second scenario considers the
possibility to extend the existing SWMS, considering the proposed SWMS configuration for
developing countries. The optimal network design of the SWMS configuration in Scenario #1
includes four collection stations, one recycling plant, and one landfill, and this provides a
minimum
net by
daily
cost ofby143,394.9
L.E./day.
The optimization results for Scenario #2 favors
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increasing the number of recycling plants into
six recycling plants while keeping four collection
stations and one landfill. The optimal design obtained for Scenario #2 is expected to achieve a
daily profit of 232,554.9 L.E./day, which provides a sustainable and effective SWMS for the
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governorate. Accordingly, the MILP model has helped the investigation of the
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Conceptualization, A.S., R.P. and G.D.G.; methodology, A.S.; software, F.-E.Z.; validation,
A.S., R.P. and G.D.G.; formal analysis, A.S. and I.H.A.; investigation, A.S. and F.-E.Z.;
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draft preparation, A.S., F.-E.Z. and A.F.; writing—review and editing, A.S., R.P., G.D.G. and A.F.;
visualization, A.S., R.P. and F.-E.Z.; supervision, A.S. and I.H.A.; project administration, A.S. and
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1.
Kalu, E.O.; Inyama, S.C.; Nwobi, F.N. Mathematical Model of Municipal Solid Waste
Necessary
Endorse
Management System in Aba Metropolis of Abia State Nigeria. J. Res. Appl. Math. Quest
Conflicts of Interest
J. 2017, 3, 38–51. [Google Scholar (https://scholar.google.com/scholar_lookup?
References
title=Mathematical+Model+of+Municipal+Solid+Waste+Management+System+in+Ab
a+Metropolis+of+Abia+State+Nigeria&author=Kalu,+E.O.&author=Inyama,+S.C.&au
thor=Nwobi,+F.N.&publication_year=2017&journal=J.+Res.+Appl.+Math.+Quest+J.&
Comment
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Statistics
volume=3&pages=38%E2%80%9351)]
2.
Badran, M.; El-Haggar, S. Optimization of municipal solid waste management in Port Said
—Egypt.
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(https://scholar.google.com/scholar_lookup?
title=Optimization+of+municipal+solid+waste+management+in+Port+Said%E2%80
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%94Egypt&author=Badran,+M.&author=ElHaggar,+S.&publication_year=2006&journal=Waste+Manag.&volume=26&pages=53
4%E2%80%93545&doi=10.1016/j.wasman.2005.05.005&pmid=16055323)] [CrossRef
(https://doi.org/10.1016/j.wasman.2005.05.005)]
(http://www.ncbi.nlm.nih.gov/pubmed/16055323)]
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3. Ayodele,
T.; Alao, M.; Ogunjuyigbe, A. Effect of collection efficiency and oxidation factor
Abstract
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on greenhouse gas emission and life cycle cost of landfill distributed energy generation.
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Cities
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title=Effect+of+collection+efficiency+and+oxidation+factor+on+greenhouse+gas+e
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mission+and+life+cycle+cost+of+landfill+distributed+energy+generation&author=A
Results
and Discussions
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Cite yodele,+T.&author=Alao,+M.&author=Ogunjuyigbe,+A.&publication_year=2019&jou
Conclusions
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hor=Hodgkinson,+I.&author=Guo,+J.&publication_year=2022&journal=Sustainabilit
y&volume=14&pages=480&doi=10.3390/su14010480)]
(https://doi.org/10.3390/su14010480)]
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Sustainability | Free Full-Text | An Optimization Model for the Design of a Sustainable Municipal Solid Waste Management System
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