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Waste Manage Res 2002: 20: 46–54
Printed in UK – all rights reserved
Waste Management & Research
Copyright © ISWA 2002
Waste Management & Research
ISSN 0734–242X
46
Introduction
A regional LP optimisation model for Integrated Solid
Waste Management (ISWM) was developed and
described by Abou Najm et al. 2002. This paper presents
the results of model simulations for the development of
a regional ISWM plan in Northern Lebanon. The model
was also used to assess the sensitivity associated with the
cost of waste management alternatives and recycling
policies. Operational costs were varied for the processing
facilities, composting plants, incinerators, and landfills.
The problem was addressed on the regional level
considering counties as generation nodes with present
and proposed facilities as management alternatives in
the model. The primary reason to move MSW
An optimisation model for regional integrated solid
waste management II. Model application and
sensitivity analyses
M. Abou Najm
M. El-Fadel
G. Ayoub
Department of Civil Engineering, American University of
Beirut, Lebanon
M. El-Taha
Department of Mathematics and Statistics, University of
Southern Main, ME
F. Al-Awar
Soil Irrigation and Mechanisation, American University of
Beirut, Lebanon
Keywords – Integrated solid waste management, linear
programming, optimisation, wmr 456–2
Corresponding author: M. El Fadel, American University of
Beirut, Faculty of Engineering and Architecture, Bliss Street,
PO box 11–0236 Beirut, Lebanon. Fax: 961–1–744 462.
Email: mfadel@aub.edu.lb
Accepted in revised form 14th September 2002
Increased environmental concerns and the emphasis on
material and energy recovery are gradually changing the
orientation of MSW management and planning. In this
context, the application of optimisation techniques have
been introduced to design the least cost solid waste
management systems, considering the variety of manage-
ment processes (recycling, composting, anaerobic digestion,
incineration, and landfilling), and the existence of uncer-
tainties associated with the number of system components
and their interrelations. This study presents a model that
was developed and applied to serve as a solid waste decision
support system for MSW management taking into account
both socio-economic and environmental considerations.
The model accounts for solid waste generation rates,
composition, collection, treatment, disposal as well as
potential environmental impacts of various MSW
management techniques. The model follows a linear
programming formulation with the framework of dynamic
optimisation. The model can serve as a tool to evaluate
various MSW management alternatives and obtain the
optimal combination of technologies for the handling,
treatment and disposal of MSW in an economic and
environmentally sustainable way. The sensitivity of various
waste management policies is also addressed. The work is
presented in a series of two papers: (I) model formulation,
and (II) model application and sensitivity analysis.
management to a regional level was to optimise on
economies of scale. This regional model helped
obtaining an optimized solution for an ISWM system
based on the most economically feasible and environ-
mentally sound alternative.
Model application
To demonstrate the applicability of the model, the
region of Northern Lebanon was considered as a case
study with data collected for the year 2000. Every coun-
ty was considered a generation node (6 generation
nodes), with 6 possible landfill sites, 6 processing facili-
ties, 2 composting plants, and one incinerator (Fig. 1).
This resulted in a problem of 150 decision variables and
64 constraints. Population distribution and generation
rates were estimated and waste quantities were obtained
(Table 1).
Selection of model parameters
The selected model parameters and their level of detail
and accuracy directly affect the reliability of the model’s
output. Although the model can optimise any waste
management scenario, the question remains: how close is
this scenario to the actual case study? The model
parameters that need determination are: (1) number of
generation nodes, treatment and disposal facilities, and
time intervals (main parameters), (2) waste composition
and characteristics, (3) transportation distances between
links, (4) minimum and maximum capacities of
treatment and disposal facilities, (5) economic and
environmental costs for every ISWM alternative, and (6)
adopted policies for the household separation and waste
recycling.
Main model’s parameters
The selection of the main parameters is the first stage in
the model buildup. Every county was considered as a
generation node in which a processing facility and a land-
fill were assumed. In addition, two potential composting
plants in Akkar and Koura as well as an incinerator in
Tripoli were considered. The order and labeling of the
model’s parameters is shown in Table 2.
Waste composition and characteristics
Although waste composition and quantities change
between communities with seasonal, economic, and
cultural variations, the waste is assumed to have
constant constituents and to be generated at predeter-
mined rates for simplicity reasons. A characteristic
feature of the waste composition is the high proportion
of food waste that has major implications for the selec-
An optimisation model for regional integrated solid waste managment II. Model application and sensitivity analyses
47
Waste Management & Research
Fig. 1. Layout of the simulated region with locations of proposed
facilities
Table 1. Projected waste quantities in Northern Lebanon
County Population1 Daily Waste Quantities
(‘000 capita) (tons)
# Name
1 Tripoli 384 268
2 Akkar 161 112
3 Zgharta 107 74
4 El Koura 80 58
5 Bcharre 72 49
6 El Batroun 89 63
Total 893 624
1
Population calculated assuming 2.5 percent annual growth rate (ERM 1995)
Table 2. Main parameters labeling for the model’s case study
Tripoli Akkar Zgharta Koura Bcharre Batroun
Generation 1 2 3 4 5 6
Nodes, I
Processing 1 2 3 4 5 6
facilities, J
Biological treatment 1 2
facilities, K
Thermal treatment 1
facilities, R
Landfills, U 1 2 3 4 5 6
tion of appropriate disposal methods, making the waste
very suitable for composting. Moreover, high moisture
content (45 percent) and low energy content (3900 btu)
deem the waste as unsuitable for incineration without
further processing. Table 3 provides a summary for the
waste composition adopted for the model (Ayoub et al.
1994 and 1996).
Transportation distances between links
Transportation distances were approximated from Fig. 1
with two factors to consider. The first is the expected
road network length between the centre of any county
(generation node) and the proposed locations of the
treatment and disposal facilities. The second factor
accounts for the mountainous nature of some parts of
the area where steep slopes require more transportation
costs than links with the same distance, yet at milder
slopes.
Capacities
Minimum capacities for all treatment and disposal
facilities were set to zero to give the model the freedom
of selection. Sometimes setting minimum values for the
facilities may shift the whole waste management stream
into a new direction, however, these values may have a
significant effect in the sensitivity analysis where they
can be used to enforce a certain waste management
alternative. Maximum capacities, on the other hand,
were set at high values in order not to impose a sealing
on a certain waste management alternative or facility.
Lowering maximum capacities may play a major role in
the sensitivity analysis if the decision-maker wants to
force a shift from a certain waste management alterna-
tive which happens to be the cheapest, but not
necessarily the most popular.
Economic and environmental costs
To cover the “life cycle” of waste management
expenditure, economic costs were assumed to account
for the following three major cost categories (EPA 1997):
•
Up-front costs include the initial capital investments
required for the start-up of an MSW management
service;
•
Operating costs to cover the expenses of managing
MSW in a waste management facility during its
operational phase; and
•
Back-end costs to account for the proper closure of
MSW facilities after the end of their operational useful
life. They also include the costs of post-employment
health and retirement benefits for MSW facilities’
workers.
Considering only the direct economic costs associated
with MSW management alternatives does not capture
the full view of expenditure. All external costs need to be
evaluated and explicit dollar values must thus be
assigned to the various externalities (environmental
impacts). Environmental damage, and consequently its
associated cost, can occur at any stage of waste manage-
ment. For the collection and transport stages, the most
important environmental costs are related to the
collection and transport vehicle-induced emissions. At
the treatment and disposal stage, environmental costs
will vary according to the disposal method. In landfilling,
environmental costs are incurred through the leaching
of pollutants to surface and ground water, as well as
from releases of pollutants to the air from waste
48
Waste Management & Research
Table 3. Waste composition
Constituent Waste composition
Food waste 62
Paper and cardboard 14
Plastics 11
Textiles 3
Metals 3
Glass and ceramics 5
Others 2
Table 4. Operational cost figures for ISWM alternatives
Cost per ton
1
($)
Activity Operation Environmental
2
Collection 16
3
Processing 10
3
Composting
4
30
3
11
Incineration
4
70
5
36
Landfilling
4
20
5
26
1
Cost figures adopted in this study
2
Based on environmental cost figures (CIWMB 1990)
3
Based on cost figures obtained from surveys and waste management
contracts in Lebanon
4
Includes cost of collection and transportation to the facilities
5
Based on average values from World Bank, 1995
M. Abou Najm, M. El-Fadel, G. Ayoub, M. El-Taha, F. Al-Awar
decomposition. In incineration, environmental costs are
incurred through air emissions from combustion and
leaching of toxic materials contained in the incinerator
ash to be landfilled.
A study performed for the California Waste
Management Board by Tellus Institute considered
samples of 27 so as landfills to produce estimates for site
specific environmental remediation costs (CIWMB
1991). The valuation method adopted was the abate-
ment cost method where costs required to abate the
pollution resulting from waste management alternatives
are assumed to estimate the value of the damage (Parikh
& Parikh 1998; Bartelmus 1998).
To obtain cost ranges on ISWM alternatives, interna-
tionally suggested cost ranges were adopted because of
the lack of local data. Table 4 provides cost ranges
suggested by the World Bank for Lebanon and the
adopted average costs in this study. Note that the
obtained values are very rough estimates of the per ton
cost of waste management given the fact that ISWM
costs are greatly affected by the economies of scale and
the technology used. Transportation costs were assumed
at a rate of $0.25 ton
–1
of waste per kilometer–distance.
Adopted policies for household separation and waste
recycling
Since the waste recyclable portion does not exceed 35%
given that about 62% of the waste is putrescible organics
and that not all the remaining portion can be reserved
for recycling (Ayoub et al. 1994), a small amount of 5.5%
was adapted as an initial policy for the recycling
program. This amount will require the processing of at
least 32% of the waste for separation to provide the raw
materials given the small portion of recyclable waste and
that not all waste is recyclable. Table 5 provides a summa-
ry of the adopted base case waste recycling and household
separation policies in the model.
An optimisation model for regional integrated solid waste managment II. Model application and sensitivity analyses
49
Waste Management & Research
Table 5. Description of the base case waste recycling and household
separation policy
Waste Material Percentage
Recyclable portion Recycling policy Household
separation policy
Paper 70 20 0
Glass 90 15 0
Metals 95 30 0
Plastics 60 10 0
Table 6. List of assumptions adopted for the simulation
Parameter Value
Transportation cost 0.25 $ ton
–1
.km
Processing cost 10 $ ton
–1
(operational), 0 $ ton
–1
(environmental)
Composting cost* 40 $ ton
–1
(operational), 11 $ ton
–1
(environmental)
Incineration+ 70 $ ton
–1
(operational), 35 $ ton
–1
(environmental)
Landfilling 20 $ ton
–1
(operational), 26 $ ton
–1
(environmental)
Recycling~ The adopted recycling policy accounts for a maxi-
mum recycling percentage of 5.5 percent if 100 percent of the waste
was processed°
Assumptions
– All facilities are functional and no required construction or
expansion costs
– A minimum processing amount of 32 percent of total waste is
imposed by policy
– No household separation policy
– Capacity constraints were loosened to give the model the freedom of
selection
– Ratio of compostable and combustible waste are 0.4 and 0.1 (40
and 10%), respectively
– Volume reduction ratio for composting is 0.5 (50%)
– Volume reduction ratios for incineration are 0.7 and 0.95 (70 and
95%) for waste received directly from generation sources and
for waste received from processing facilities, respectively
– Ratio of material returned from composting plants and incinerator is
0 (0%)
* Revenues adopted for composting are $5 ton
–1
of waste
+ Revenues adopted for incineration with and without processing are
$8 and $5 ton
–1
of waste, respectively, due to the higher energy
content in the processed waste
~ Recycling revenues were varied at 0, 5, and 11.5 $ ton
–1
for
simulation 1
o This assumption is attributed to the high percentage of food waste
as well as the local current recycling conditions and
waste-processing experience
Fig. 2. Optimum waste distribution for the adopted scenario
Simulation results
This part presents an optimum scenario obtained by the
model for one specific simulation. A summary of the
assumptions adopted is presented in Table 6. The
optimum ISWM path obtained is presented in Fig. 2.
Sensitivity analyses
Sensitivity analysis to recycling revenues and operational
costs of waste management alternatives was conducted
using a series of simulations presented in Table 7. The
sensitivity of recycling revenues was tested by varying
the recycling revenues at 0, 5, and 11.5 $ ton
–1
for simu-
lation 1.
Recycling revenues
Model simulations were conducted for three scenarios
accounting for the returns from the recycling of waste
since this is the most unforeseeable parameter. The first
scenario (R=0) is a worst case scenario that considers
zero return from recycling. The second (R=5) and the
third (R=11.5) scenarios consider average return values
of $5 ton
–1
and $11.5 ton
–1
, respectively. Return values
were considered based on field surveys for the recycling
market in the region (Ayoub et al. 1994 and 1996). Fig.
3 summarises the simulated optimum waste management
strategies for the three scenarios.
Fig. 3 indicates that the cost structure favoured the
direct landfilling alternative from the generation nodes
being the cheapest option for the R=0 scenario.
Processing of waste was forced by policy and the model
yielded the minimum required processing amount of 32%
of the waste. As revenues increase, processing of
waste reached 37% and was no longer forced by policy for
the recycling revenue R=$5 scenario (since the policy
required a minimum of 32 percent processing). Similarly,
the cost structure favoured the processing and composting
option rather than the direct landfilling alternative for
the recycling revenue R=$11.5 scenario. Clearly, this
change occurred because of the high revenues associated
with the selling of recyclable materials that would cover
M. Abou Najm, M. El-Fadel, G. Ayoub, M. El-Taha, F. Al-Awar
50
Waste Management & Research
Table 7. A summary of the simulation scenarios
Simulation Variation of operational costs
# Processing Composting* Incineration+ Landfilling
1~ 10 30 70 20
2 5 30 70 20
315307020
420307020
510157020
610207020
710257020
810357020
910407020
10 10 45 70 20
11 10 30 55 20
12 10 30 60 20
13 10 30 65 20
14 10 30 75 20
15 10 30 70 10
16 10 30 70 15
17 10 30 70 18
18 10 30 70 22
19 10 30 70 25
20 10 30 70 30
21 10 30 70 35
22 10 30 70 40
23 10 30 70 45
24 10 30 70 50
Assumptions:
– Simulations are all conducted for the year 2000
– Environmental costs adopted are 0, 11, 35, and 26 $ ton
–1
for the
processing, composting, incineration, and landfilling, respectively
– The adopted recycling policy accounts for a maximum recycling
percentage of 5.5% if 100% of the waste was
processed. This assumption is attributed to the high percentage of
food waste as well as the local current recycling conditions and
waste-processing experience
* Revenues adopted for composting are $5 ton
–1
of waste
+ Revenues adopted for incineration with and without processing are
$8 and $5 ton
–1
of waste, respectively, due to the higher energy
content in the processed waste
~ Recycling revenues were varied at 0, 5, and 11.5 $ ton
–1
for
simulation 1
Fig. 3. Optimised waste distribution (tons day
–1
) corresponding to a
recycling revenue of 0, 5, 11.5 ($ ton
–1
)
the cost of waste processing which reached 100% and
was not forced by policy. Comparison among the three
scenarios revealed the importance of adopting a recycling
program in the ISWM stream. With policy enforcement at
the first stages of the plan, recycling programs are expect-
ed to generate revenues and ultimately affect the overall
ISWM strategy.
A comparison among available waste management
alternatives is expressed in Fig. 4. As the waste
management strategy shifts towards adopting
composting and recycling programs, an increase in
processing, composting, and recycling waste-
amounts is naturally counterbalanced by a decrease
in land-filling. This translates into lower land
requirements and environmental costs. Note that
increasing the recycling portion from 1.76% (R=0)
to 5.51% (R=$11.5) results in a decrease in
land-filling requirements from 85% to 58% of the total
amount of waste generated because recycling is
inherently coupled with composting in the
model.
A summary of cost indicators associated with the
simulated scenarios is shown in Fig. 5 where operational
costs, benefits, and net expenditures are indicated on a
per ton and per capita basis. Fig. 4 indicates that the
adoption of recycling programs results in an increase in
costs as waste management alternatives shift from
landfilling to composting and recycling. However, larger
increase in the benefits caused a slightly lower overall
net expenditure.
Operational costs
Sensitivity of operational costs was tested. Following are
the results obtained for the simulations summarised in
Table 7.
Processing facilities (Simulation 1–4)
Processing facilities are considered as cost sinks to the
model. They are not ultimate waste management
alternatives, however, they are necessary for launching
other waste management options like composting and
recycling. The sensitivity of the model to the processing
costs was tested by changing the operational costs of
processing facilities (Table 7. Simulations 1–4).
Decreasing the operational cost of processing to $5 ton
–1
(Simulation 2) gave the same optimum path as that of
Simulation 1 at R=$5 scenario. Increasing its cost above
$10 ton
–1
yielded the same optimum path of Simulation 1
with R=0 scenario. This result is expected since the high
processing cost will force the model to favour the direct
landfilling option and to limit the amounts of processing
to the minimum specified by the constraints (32%).
Compost plants (Simulation 5–10)
Composting is considered a competitive waste
management alternative. Its application, however, is
constrained by the fact that (1) composted waste needs
to pass through processing; and (2) 40% of waste is
An optimisation model for regional integrated solid waste managment II. Model application and sensitivity analyses
51
Waste Management & Research
Fig. 4. Comparison of waste management alternatives for the year
2000 with different recycling revenues
Fig. 5. Costs, benefits, and total expenditures in $ per ton of waste and
per capita for the year 2000 with different recycling revenues
Fig. 6. The variation of composting and landfilling quantities with
composting operational costs
compostable. The sensitivity of the model to composting
costs was tested by changing the operational costs of
composting facilities (Table 7. Simulations 5–10). Fig. 8
shows the variation of composting and landfilling
quantities as composting operational costs vary.
Operational costs as low as $15 ton
–1
(Simulation 5)
provided an optimum path similar to that of Simulation
1 at R=$5 scenario. It is worth noting that a $15
decrease in the operational cost of composting yielded
the same optimum path provided by a $5 increase in the
revenues associated with recycling (Fig. 3). This is
justified by the fact that only 40% of the waste that
reaches the processing facilities is allowed to go to the
compost plants. As a result, decreasing the operational
cost of composting by $5 generates only $2 savings for
every ton that enters the processing facility (if compost-
ing is the optimum alternative), however increasing the
recycling revenues by $5 ton
–1
results in $5 savings for
every ton that enters the processing facility.
Composting operational costs ranging from $20 ton
–1
to
$35 ton
–1
(Simulations 6–8) gave the same optimum path
as that of Simulation 1 at R=0 scenario. This range of
composting operational costs favoured the direct
landfilling option. Increasing composting operational costs
to $40 ton
–1
(Simulation 9) provides the optimum path
shown in Fig. 2. Composting will no longer be an attrac-
tive option at operational costs of $45 ton
–1
or
higher (Simulation 10). This result is expected since at
such high composting costs, the model will favour the
landfilling option even from the processing facilities stage.
Incinerators (Simulation 11–14)
Incineration was not considered as a competing option
in ISWM. The high operational and environmental costs
associated with this technology deemed incineration as
the most unwanted waste management option. The
sensitivity of the model to incineration costs was tested
by changing the operational costs of the proposed
incinerator (Table 7. Simulations11–14). Incineration
operational costs of $55 ton
–1
to $75 ton
–1
(Simulations
11–14) gave the same optimum path as that of
Simulation 1 at R=0 scenario. This decrease in opera-
tional costs was not adequate to deem incineration as a
competitive waste management option and decreasing
the cost further is technically not feasible at present.
Incineration was favoured at operational costs as low as
$10 ton
–1
because of its high operational and environ-
mental costs. Such low operational cost is practically
beyond reach, and no known technology is capable of
thermally managing the waste at this cost range. Note
that decreasing operational costs of incineration is
equivalent to raising the cost of landfilling and other
competing waste management alternatives.
Consequently, raising the cost of landfilling by $60 is
equivalent to decreasing the cost of incineration by the
same amount.
Landfills (Simulation 15–24)
Landfilling is considered the model’s most competitive
waste management alternative. Its low operational cost
and the ability to receive waste directly from generation
sources are two challenging properties that designate
landfilling as an unbeatable option. The sensitivity of the
model to landfilling costs was tested by changing the
operational costs of composting facilities (Table 7.
Simulations 15–24). Fig. 8 shows the variation of
composting and landfilling quantities as landfilling opera-
tional costs vary. Fig. 8. The variation of composting and
landfilling quantities with landfilling operational costs
Landfilling operational cost of $10 ton
–1
(Simulation
15) gave an optimum waste management strategy similar
to that of Simulation 9 (composting operational cost of
$40 ton
–1
). The similarity lies in that either increasing
the composting cost or decreasing the landfilling cost to
some level will favour the landfilling option even from
the processing facility stage.
Landfilling operational costs ranging from $15 ton
–1
to
$30 ton
–1
(Simulations 16–20) gave the same optimum
path as that of Simulation1 at R=0 scenario. This range
of landfilling operational costs favoured the direct
landfilling option, however, it preferred composting from
M. Abou Najm, M. El-Fadel, G. Ayoub, M. El-Taha, F. Al-Awar
52
Waste Management & Research
Fig. 7. The variation of composting and landfilling quantities with land-
filling operational costs
An optimisation model for regional integrated solid waste managment II. Model application and sensitivity analyses
53
Waste Management & Research
the processing level.
Increasing landfilling operational costs to a range of
$35 ton
–1
to $40 ton
–1
(Simulations 21 and 22) provides
the optimum path of Simulation 1 at R=$5 scenario.
Composting becomes a competitive waste management
alternative for some counties with no need for a policy to
set minimum amounts for waste processing. Further
increase in cost to $45–50 ton
–1
(Simulations 23 and 24)
provides the optimum path of Simulation 1 at R = $11.5
scenario.
Conclusions
Planning a regional waste management strategy is a
critical step that, if not properly addressed, will lead to an
inefficient ISWM system. Regional planning affects the
design, implementation, and efficiency of the overall
ISWM scheme. Consequently, decision-makers must look
for optimised regional waste management planning to
achieve a successful strategy. The optimisation of an
ISWM strategy for an area requires the knowledge of avail-
able waste management alternatives and technologies,
economic and environmental costs associated with these
alternatives, and their applicability to the specific area.
This study represents an attempt towards a better
planning and management of solid waste. It tackles the
planning phase of regional ISWM, being the first stage
that needs to be addressed. A waste management and
planning optimisation tool was developed to obtain
optimum waste management policies under prevailing
conditions (Abou Najm et al. 2002). It describes the
information required for making factual and analytical
decisions about the optimum waste management
alternative taking into consideration economic and
environmental impacts, all along with various con-
straints adopted to account for implemented or
suggested policies, mass balance, capacity limitations,
operation, finance, and site availability.
The sensitivity of waste management policies to
adopted recycling strategies was tested. Slight increase
in the recycling amounts (from 2.0–5.5% of waste)
resulted in major decrease in landfilling requirements
(from 58–83% of waste) at almost the same net
expenditure (considering both costs and benefits). The
underlying reason is that increasing recycling amounts
results in increasing the amount of waste reach-
ing the processing facilities and thus giving chance to
composting as a competitive waste management
alternative.
The sensitivity of operational costs of considered
waste management alternatives was also tested.
Processing facilities proved to be cost sinks since they are
not ultimate waste disposal alternatives. As their
operational costs increase, the optimum solution favors
direct landfilling rather than composting that needs to
pass through processing. Composting was considered a
competitive alternative at operational costs as low as $15
ton
–1
. It is still favoured for operational costs ranging
from $20–$35 ton
–1
if a minimum waste–processing
policy is implemented (Fig. 6). Incineration failed to
compete as a successful waste management alternative
due to the high operational and environmental costs
associated with the process. Landfilling was shown to be
the most competitive alternative (at the adopted costs).
For operational costs ranging from $10 – $40 ton
–1
, the
landfilling share ranged between 83 to 94% of the waste.
It was only at high operational costs (>$40 ton
–1
) that
composting gained ground and lowered the landfilling
share to 58%.
Acknowledgements
This study was funded by the University Research Board
of the American University of Beirut. Special thanks are
extended to the United States Agency for International
Development for its continuous support for the
Environmental Engineering and Science Programs at the
American University of Beirut.
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