optimal process network for municipal solid waste management in iskandar malaysia (tan, 2013)

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Journal of Cleaner Production xxx (2013) 1e11

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Journal of Cleaner Production

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Optimal process network for municipal solid waste management inIskandar Malaysia

Sie Ting Tan a, Chew Tin Lee b,*, Haslenda Hashim a, Wai Shin Ho a, Jeng Shiun Lim a

a Process System Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor, MalaysiabDepartment of Bioprocess Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor, Malaysia

a r t i c l e i n f o

Article history:Received 31 August 2013Received in revised form2 December 2013Accepted 3 December 2013Available online xxx

Keywords:Municipal solid waste (MSW)Resource processing networkWaste treatment technologiesOptimisationMixed integer linear programming (MILP)

* Corresponding author. Tel.: þ60 7 553 5594; fax:E-mail addresses: [email protected], [email protected]

T. Lee).

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Please cite this article in press as: Tan, S.T., etof Cleaner Production (2013), http://dx.doi.o

a b s t r a c t

Ineffective management of municipal solid waste (MSW) may cause degradation of valuable land re-sources and create long-term environmental and human health problems. A sustainable and efficientwaste management strategy is needed to balance the need for development, the quality of human lifeand the environment. This study aims to synthesis a MSW processing network to produce energy andvalue-added products for achieving economic and environmental competitiveness. An optimisationmodel that integrates four major utilisation technologies was incorporated to facilitate a cost-effectiveprocessing network. The model is able to predict the best mix of waste treatment technologies, fore-cast the production of by-product from waste treatment process, estimate the facility capacity, forecastthe greenhouse gas (GHG) emission of the system, and eventually generate an optimal cost-effectivesolution for municipal solid waste management (MSWM). Four scenarios for MSWM were consideredto analyse the economic impact of different waste utilisation alternatives: i) the business as usual (BAU)scenario as a baseline study, ii) the waste-to-energy (WTE) scenario, iii) the waste-to-recycling (WTR)scenario, and iv) the mixed technology (MIXTECH) scenario. The MIXTECH scenario was able to providethe best mix of waste utilisation technologies. The optimal waste allocation in terms of percentageinvolved landfill gas recovery system (LFGRS) (14%), mass burn incineration (3%), material recycling fa-cilities (MRF) (56%), and composting (27%). The optimal scenario would be able to achieve the renewableenergy (RE) target, achieve the recycling target and promote composting as the waste reduction alter-native for the region being studied. Sensitivity analyses were conducted for the optimal or MIXTECHscenario to examine the effect of the RE target and GHG emission reduction target with respect to thesystem cost and waste allocation to each technology. The proposed mixed integer linear programming(MILP) model was applied for Iskandar Malaysia (IM) as a case study.

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1. Introduction

Municipal solid waste (MSW) refers to waste generated fromresidential, commercial, institution and public parks (Fodor andKleme�s, 2012). Solid waste management (SWM) involves manytechnologies associated with controlling waste generation,handling and storage, transportation, processing and final disposal.The hierarchy of SWM was formed since 1970s, several evolution,different versions of solid waste treatment hierarchies exist. One ofthose affordable hierarchies is suggested by Finnveden et al. (2005),

þ60 7 5538003.om, [email protected] (C.

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al., Optimal process networkrg/10.1016/j.jclepro.2013.12.0

in the order of reduction of waste amount, reuse, recycle, compostor recovery through incineration and finally landfill disposal. Itexplained that the main objective of SWM is to treat the wastegenerated. In addition, energy and recyclable material can berecovered as by-products to achieve sustainable waste manage-ment that is environmental friendly, economically reasonable andsocially acceptable (Tchobanoglous and Kreith, 2002).

Rapid urbanisation, population growth and industrialisationcontribute towards large-scale increase of MSW in Malaysia. Thesefactors have changed the characteristics and composition of thesolid waste generated. The daily waste generation has also shownan upward trend. Waste generation was 16,200 t in year 2001. Thisamount increased to 19,100 t in 2005, 17,000 t in 2007 and 21,000 tin 2009 (Ahmad et al., 2011). Due to the increased populationgrowth rate, the daily solid waste generated is estimated to be31,000 t/d by 2020 (Johari et al., 2012).

for municipal solid waste management in Iskandar Malaysia, Journal05

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S.T. Tan et al. / Journal of Cleaner Production xxx (2013) 1e112

Depending on its characteristics, the MSW can be preferentiallyprocessed by different approaches. The present wastemanagementmethods in Malaysia are highly dependent on landfill as only 5.5%of the MSW is recycled and 1% is composted, while the remaining94.5% of MSW is disposed on the landfill site (Periathamby et al.,2009). The practice of waste segregation is random and unofficialin Malaysia. Waste recycling is mainly performed by garbagescavengers at the landfill sites. To date, SWM in Malaysia is at thestage of transition and planning towards sustainable and effectiveapproaches. Ineffective management of waste may cause degra-dation of valuable land resources, increase land costs, and createlong-term environmental and human health problems. Sustainableand more efficient waste management strategies are needed toreduce the heavy reliance on landfills. Malaysia aims to establish aholistic framework that considers the trade-off involved in thesegregation process and the economic performance of differentMSW practices to achieve the national MSW recycling rate (22% ofthe total MSW) by the year 2020 (Ministry of Housing and LocalGovernment, 2005). The segregation and recycling of waste areessential to improve the performance of waste processing.

1.1. Literature review on waste management model

The complexity of SWM includes the prediction of solid wastegeneration, selection of waste treatment technologies, selection offacility sites, estimation of facility capacity, operation of the facility,scheduling of the system and transportation of the waste. SWM canbe modelled through a system perspective (Seadon, 2010). Systemanalysis tools for supporting decision making in waste manage-ment were developed since 1970s, these models can be categorisedinto two groups: (1) system engineering (SE) models and (2) sys-tem assessment (SA) models (Pires et al., 2011). The SA models canbe used to analyse the performance of an existing waste manage-ment system, for example life-cycle assessment (LCA), risk assess-ment, and material flow analyses (Juul et al., 2013). For instance,Chen and Chang (2010) developed a range of SA models to assessthe performance of MSW recycling in Taiwan. The models includedthe diffusion effect and the organisational learning effect as the keyvariable for recycling performance, however, other variables suchas costing and environmental protection are not considered in themodels. The LCA tool is a popular tool to solve the complex issues ofSWM. For instance, Othman et al. (2013) reviewed the applicationof LCA for the assessment of integrated solid wastemanagement forseveral Asian countries. The study focused on the assessment ofenvironmental impacts of various waste treatment technologiesand concluded that recycling, anaerobic digestion and thermaltreatments are effective technologies for the Asian countries.Wanichpongpan and Gheewala (2007) used LCA as a decision toolto assess the environmental impact of landfill gas-to-energy systemin Thailand, they concluded that a centralised landfilling facility isenvironmental and economical beneficial as compared to smalllandfills. Poeschl et al. (2012a, b) used LCA to analyse the biogasproduction system and utilisation pathways from different inputsources including from MSW and feedstock. Liamsanguan andGheewala (2008) used LCA to assess the holistic impact of inte-grated solid waste management towards the mitigation of green-house gases emission in Phuket, Thailand. These LCA studies tendto assess the waste treatment technologies focussing on the envi-ronmental impact and with less consideration on the detailedmodelling and optimisation for the economical impact of the pro-cesses. While SA models focus on the assessment and analysis ofthe existing systems, SE models focus on the design and solution ofa waste management system. Methods such as multi-criteria de-cision models (MCDM), simulation models, forecasting models,cost-benefit analysis, and optimisation models are widely used in

Please cite this article in press as: Tan, S.T., et al., Optimal process networkof Cleaner Production (2013), http://dx.doi.org/10.1016/j.jclepro.2013.12.0

the SE approach (Pires et al., 2011). The optimisation modeldeveloped using SE model emphasises the design of a system by aspecific objective function which gives the best solution to theobjective function (Juul et al., 2013). Various types of techniqueshave been implemented as an optimisation model for SWM. Theseinclude the linear programming (LP), mixed integer linear pro-gramming (MILP), non-linear programming (NLP), multi-objectiveprogramming (MOP), stochastic programming, two-stage pro-gramming, fuzzy method programming, and hybrid models. Anoverview of the optimisation models for SWM are summarised inTable 1.

The early stage of SE model developed for SWM focused on thecost-effectiveness principle of LP with a single-objective optimi-sation scheme (Juul et al., 2013). For example, Münster andMeibom(2010, 2011) designed an energy system using the Balmorel modelto optimise the investment cost for different waste-to-energy(WTE) technologies in the northern Europe. In addition, a LPmodel was developed by Rathi (2007) to investigate the SWMtechnologies that focused on composting by taking into accountboth the economic and environmental impacts. Other LP model asdeveloped by Salvia et al. (2002) also addressed the similar issue ofwaste management and emphasised on the analysis of oneparticular technology. LP model is typically applied and limited to asingle process that does not support the evaluation and selection ofmultiple technologies. More powerful modelling tools are neededto conduct modelling work for SWM notably for the real casestudies that involve a range of uncertainties. For instance, morecomplex modelling methods including mixed integer linear pro-gramming (MILP), non-linear programming (NLP) (Chang et al.,1997; Shadiya et al., 2012), stochastic programming (Guo andHuang, 2009b), fuzzy logic (Yeh and Xu, 2013) and hybrid model(Xu et al., 2010; Li and Chen, 2011; Chang et al., 2012) weredeveloped to assess the complex scenarios of SWM in the realworld. MILP is relatively simple and can be applied to consider thecomplex scenario with uncertainties using the binary selectionfunction that facilitates the selection of multiple technologies anddynamic planning of resource network for SWM. Badran and El-Haggar (2006) proposed a MILP model for the optimal manage-ment of MSW at Port Said, Egypt, with the objective of minimisingthe waste collection and transportation costs. Dai et al. (2011)designed a MILP model to assess waste allocation issue and theexpansion of capacity for the waste treatment facility. Santibañez-Aguilar et al. (2013) determined the optimal supply chain networkfor waste utilisation using MILP. Ng et al. (2013) developed a MILPmodel to determine the waste-to-energy network that optimisedthe cost, waste energy potential utilisation, and the carbonfootprint.

As presented in Table 1, many models were developed based onvarious waste management technologies including composting,recycling and disposal to optimise the economical factor. Relativelyfewer models have simultaneously considered the economicalfactor based on energy system and WTE technologies such as thatby Münster and Meibom (2011). Ng et al. (2013) developed a WTEprocessing network with integrated consideration for economicaland environmental factor. However, the model did not incorporateother waste treatment alternatives such as recycling or composting.As a whole, it is of great challenge and interest to integrate both thewaste management system and energy system into the modellingworks to achieve optimal economical and environmentalconsideration.

1.2. Research objectives and scopes

In general, MSW in Malaysia is typically disposed in a bin orcontainer within the house premise and collected by the respective


Table 1Optimisation models for solid waste management.

Method Reference Objectives Focus Optimisation on

Energysystem

Wastemanagement

Economy Environment

Linear programming Münster and Meibom(2011, 2010)

To maximise economic utility of energyconsumers (Balmorel)

U U

Rathi (2007) To integrate the best feasible method ofwaste management in Mumbai, focuson composting.

U U

Salvia et al. (2002) To minimise total system cost for wasteand energy management

U U U

Mixed integer linear programming(MILP)

Badran and El-Haggar (2006) To minimise the waste collection andtransportation costs

U U

Dai et al. (2011) To minimise cost of waste flowallocation and facility capacityexpansion

U U

Santibañez-Aguilar et al. (2013) To determine the optimal supply chainnetwork for the waste utilisation

U U

Ng et al. (2013) To determine optimal processingnetwork for waste-to-energy system.

U U U

Non-linear programming (NLP) Chang and Chang (1998) To maximise short-term wastemanagement strategies based on cost,energy, and material recovery

U U U

Shadiya et al. (2012) To maximise profit, while minimizingwaste through source reduction

U U

Stochastic programming Guo and Huang (2009) Tominimise costs of capacity expansionand waste flows

U U

Fuzzy logic model Yeh and Xu (2013) To minimise the sum of the squareddifferences between individual e-wasteproducts’ best dimension sustainabilityscore

U U

Hybrid model Xu et al. (2010) Interval-parameter stochastic robustoptimisation, waste flows, revenuefrom WTE

U U

Li and Chen (2011) Fuzzy-stochastic-interval linearprogramming for supporting

U U

Chang et al. (2012) Multi-objective programming and cost-benefit criteria on global warmingimpact in waste management

U U U

S.T. Tan et al. / Journal of Cleaner Production xxx (2013) 1e11 3

regional private concessionaires. The wastes are firstly transferredto transfer stations for compaction in compacting containers beforebeing sent to thewastes disposal sites. Some of theMSW is recycledor composted; however, the predominant treatment methods forMSW are landfills or the open dumpsites. Although there arevarious models designed to address the MSW issues, the literatureindicates the lack of proposal for comprehensive waste manage-ment in Malaysia which integrated technology selection for solidwaste treatment and waste-to-energy (WTE) treatment, mitigationof GHG emission and optimisation of economical impact. Therefore,this study aims to synthesise a cost-effective processing networkfor integrated MSW in Malaysia that consider the following factors:

(a) Resource allocation(b) Production portfolio(c) Best available technology for an appropriate capacity and

time of construction(d) Economical and environmental optimal for integrated solid

waste treatment and waste-to energy (WTE) treatment.

The proposed processing network of MSW was designedthrough a MILP model. The MILP model integrated several wasteutilisation technologies, including the landfill gas recovery system(LFGRS), waste incineration with energy recovery, material recy-cling facilities (MRF), and composting. The proposed system aimedto maximise the profitability of MSW processing network thatconsidered the product demand (energy demand and recyclingdemand) and the carbon emission reduction target. The proposed


MILP model was applied in Iskandar Malaysia (IM) as a case study.The MSWM system has been tested using different scenarios,namely with or without the consideration of WTE and/or waste-to-recycling (WTR) strategies to analyse the feasibility and best po-tential for a future MSWM system in the IM region. It is envisagedthat the developed model can assist the waste managementplanner to design and schedule a profitable yet sustainable MSWMsystem for the region.

Section 2 presents the methodology of this study, a super-structure that primarily contains the resource inputs, technologyalternatives and product streamswas first constructed and then theoptimisation model and the constraints are presented. Section 3presents the case study with the data and assumptions. It alsoprovides the conversion yield for each process, the feasibilitycriteria for directing the segregated waste to a specific technology,the economic data for the technology, resources (wastes and endproducts) and incentives, and the flow rate of each waste directedinto a specific technology. In Section 4, the results covering productportfolio, economical and environmental analysis under differentscenarios are discussed and compared.

2. Research methodology

2.1. Superstructure for model development

This study presents a utilisation system for MSWM consideringa set of representative waste treatment technologies that canpotentially be implemented in the IM region. A superstructure is



developed to illustrate the flow configuration of the MSWM, asshown in Fig. 1. In this study, the waste source, i, will be segregatedby process, p, to be further classified into different categories of i(i.e. 1: food waste; 2: paper; 3: yard waste; 4: plastic; 5: glass andceramic; 6: metal; 7: textile). Assuming that the practice of wastesegregation exists, different types of waste will be processed ac-cording to their potential value. The system consists of four wastetreatment technologies, p, (denoted by a, b, c, d) that covered twotypes of WTE technologies, p (i.e., LFGRS and incineration) and twoWTR technologies, (i.e., composting and recycling through theMRF). For example, the food waste, i, will be allocated to threewaste treatment technologies (composting, incineration, andLFGRS). The four key waste treatment technologies would trans-form the segregated MSW into three key value-added products, i,(denoted by 8: compost; 9: recycled materials, and 10: electricity)to fulfil the demand for products and renewable energy (RE). Theproduct, i, will be distributed to market for selling, e, or electricityto the national grid, f.

2.2. Model formulation

The complexity of an MSWM system is described by thenumber of relationships among the components in the system.The mathematical analysis of the cost-effective processingnetwork for a MSW is conducted by applying the MILP modeldue to its simplicity and common use for solving the complexSWM issues. The objective and model constraints incorporatedseveral aspects of economics, energy, recycling, waste segrega-tion and waste to value-added product to represent the realscenario in Malaysia. Two-step programming approaches (i.e.simulation and optimisation) are used to evaluate the sustain-ability of the designed system. The simulation model is used todescribe the energy production from waste and landfill gasgeneration, while the optimisation model will provide anoptimal solution for the MSWM. The model is developed andcomputed using the General Algebraic Modelling System (GAMS,version 22.9) (GAMS Development Corporation, 2013), a com-puter software for solving mathematical programming andoptimisation problems.

2.2.1. Objective functionThe optimisation model is formulated with an objective func-

tion and several constraints. The objective function aims to maxi-mise the overall profit (PROFIT) of the MSWM system as describedby Eq. (1). This function consists of the revenue from productselling (REV), processing cost (PCOST), total capital cost (CCOST) andthe variable cost (VCOST).

PROFIT ¼ REV � PCOST � CCOST � VCOST (1)

Fig. 1. The superstructure


REV represents the product revenues from the MSWM system asdescribed in Eq. (2). PROit is the production rate of product i duringperiod t. PRICEit denotes the unit price of product i in period t, whichis obtained from the published price and manufacturing quotes.

REV ¼X

it

PRICEit � PROit (2)

PCOST is the total processing cost of producing the value-addedproduct, as shown in Eq. (3). MATipt denotes the input rate ofwaste i into process p during period t. UPCOSTpt is the unit pro-cessing cost of process p at period t.

PCOST ¼X

ipt

UPCOSTpt �MATipt (3)

VCOST represents the total variable operating and maintenancecosts of the system as described in Eq. (4). In Eq. (4), UVCostpt is theunit variable cost of the corresponding process p at time t.

VCOST ¼X

ipt

UVCostpt �MATipt (4)

The capital cost CCOST is described by Eq. (5), where YPpz is thebinary decision variable for purchasing technology p with capacityz, while ACPCOSTpzt is the annualised capital cost of technology pwith capacity z at period t.

CCOST ¼X

pztYPpz � ACPCOSTpzt (5)

2.2.2. ConstraintsTo define the relationship among the variables and parameters

in this model, several linear equality, inequality and matrixmanipulation constraints are developed in the following text.

2.2.2.1. Mass balance for resources. Two types of resources areintroduced in the MSWM system: the internal resource and theexternal resource. RESit is the amount of resource i in the systemduring time period t, as described in Eq. (6). EXRECit is the input rateof the external resource, i, during period t, while SGRESipt is thequantity of resource i generated within the system under process pthrough period t.

RESit ¼ EXRECit þX

pSGRESipt cict (6)

The waste resource RESi that is fed into the respective process, p,can be converted into a product PROit, either as a material product i,an energy product i, or both,with different conversion rate ((MATipt),

of the MSWM system.



as described in Eq. (7). The potential conversion of resource-to-product is explained via a superstructure as depicted in Fig. 1.

RESit ¼ PROit þX

pMATipt cict (7)

2.2.2.2. Mass balances for processing unit. The mass balance for theprocessing technology, p, is computed using Eqs. (8) and (9). In Eq.(8), PRESpt is the quantity of the resource being processed by pro-cess p at period t. The integration matrices of the correspondingprocess, including the material process selection matrix (MPSMip)and the process resource conversion matrix (PRCMpi), are describedby Eqs. (8) and (9).

PRESpt ¼X

i

MATipt �MPSMip cpct (8)

PRESpt � PRCMpi ¼ SGRESipt cicpct (9)

2.2.2.3. Process sizing. The amount to be processed is governed byCAPpzwhich is the capacity of the process with size z, as determinedin Eq. (10). The amount of processing material, PRESpt, must be lessthan or equal to the process capacity CAPpz with the binary variableYOPpzt to determine whether the process p should be operated atsize z during period t.

PRESpt �X

zYOPpzt � CAPpz cpct (10)

2.2.2.4. Product demands. The market demand for each productfluctuates over time. Thus, product, i, must fulfil the product de-mand PRODEMit at time t, as described by Eq. (11).

PROit � PRODEMit cict (11)

2.2.2.5. CO2 emissions. In addition to the economic constraints, thesystem is also bounded by an environmental limitation: the carbonemission of the waste treatment process CEPpt is the carbon emis-sion from process p at period t, while CERT is the carbon emissionreduction target for the system. The carbon emission of the wastetreatment process in year 2005, CEPpt2005, is used as the baselinevalue. To meet the annual carbon emission reduction target, theemission from the process must be equal to or less than thereduction requirement, as shown by Eq. (12).X

p

�CEPpt2005 � CEPpt

� �X

pð1� CERTÞ � CEPpt2005 ct (12)

The carbon emission of each process, CEPpt, is denoted by Eq.(13), where EFp is the emission factor for process p.

CEPpt ¼X

i

MATipt � EFp ct (13)

2.3. Scenario setting

To evaluate the impacts of different waste management optionson the utilisation system for MSW, four scenarios were constructedin this study.

2.3.1. Business as usual (BAU) scenarioThe BAU scenario represents the baseline study for the current

scenario in IM. Under this scenario, year 2005was set as the currentpractice year where most of the MSW was landfilled and a smallpercentage was recycled. The BAU scenario assumes that no othereffort of MSWM is introduced other than the landfilling.


2.3.2. Wasteeto-energy (WTE) scenarioUnder this scenario, the WTE production will be maximised

according to the energy demand targeted by the policy makers(Blueprint for Iskandar Malaysia, 2010b). To maximise the prefer-ence for energy produced from waste, the incentives (feed e intariff) for promoting the use of MSW for energy production areincluded in the model formulation. Under this scenario, the WTEpractice including landfill gas recovery system (LFGRS) and wasteincineration were incorporated.

2.3.3. Waste-to-recycling (WTR) scenarioUnder the WTR scenario, MSW utilisation through MRF and

organic waste recycling via composting technology were intro-duced. Other technologies relevant to WTE were not considered.

2.3.4. Mixed technology (MIXTECH) scenarioThe mixed technology (MIXTECH) scenario introduces all po-

tential MSWM practices available in the case study, there areLFGRS, incineration, MRF, and composting. This scenario isdesigned to achieve themaximum net profit for the proposedMSWprocessing network without exceeding the product demand. Anoptimal solution for MSWM is anticipated to fulfil every demandwithout over- or under-production.

3. Case study and input data

3.1. Case study e Iskandar Malaysia (IM)

Iskandar Malaysia (IM) is the third largest metropolis and themost developed region in the Southern Peninsula of Malaysia. IMaims to be transformed into a metropolitan by 2020. IM covers anarea of approximately 2217 km2 and has a population of 1.7 millionwith five flagship zones: Zone A (JB city centre), Zone B (Nusajaya),Zone C (Western Gate Development), Zone D (Eastern GateDevelopment), and Zone E (Senai-Skudai), as shown in Fig. 2. IMwas established in the year 2008 from four different municipalitieswith an expanding population base and increased economic ac-tivity due to its rapid development since 2008. Solid waste gener-ation in IM increased by approximately 30% from 2005 to 2010 andis expected to increase 50% by 2025. More than 95% of the waste isdirectly disposed in three final disposal landfill sites located aroundthe region, as shown in Fig. 2. Only a small portion of the waste isrecycled informally. Of the three final disposal landfill sites in IM,only one site involves a sanitary landfill with LFGRS; another twoare conventional landfills or dumpsites. Table 2 projects the annualwaste generation in IM.

In an effort to improve the current SWM in IM, the city councilcould introduce several measures, such as waste separation atsource, upgrading the current landfill into a sanitary landfill,establishing a new MRF, and adapting WTE facilities to utilise thewaste, as outlined in the Blueprint for Iskandar Malaysia (2010a, b).

3.2. Input data

3.2.1. Waste dataThe MSW in IM are categorised into seven fractions, namely

food, yard, paper, plastics, glass and ceramic, metal, and textilewastes. The composition of the MSW is shown in Table 3. Organicwaste is the main component of MSW in IM, representing morethan 40% of the total waste. The MSW in IM has an average calorificvalue of 16.68 MJ/kg (Low Carbon Society Blueprint for IskandarMalaysia 2025, 2012). The value was calculated based on themoisture content, combustible content and ash fraction of 57%, 35%,and 8.2% respectively.


Fig. 2. Five major flagship zones (A to E) of Iskandar Malaysia (Iskandar Malaysia, 2013) and the three major landfill sites.

Table 2Projection of annual waste generation in IM from year 2012 to 2025 (Blueprint forIskandar Malaysia, 2010a).

Year 2012 2014 2016 2018 2020 2022 2024 2025

Annual wastegeneration (Mt)

0.766 0.859 0.963 1.080 1.198 1.358 1.523 1.577


3.2.2. Waste treatment technologies and related dataThe four waste treatment technologies considered in this case

study included LFGRS, waste incineration with energy recovery,MRFs, and large-scale composting facilities. The types of wasteallocated to each technology are shown in Table 4. The wastetreatment system would treat waste effectively by reducing thevolume and also generate by-products. For instance, the energyproduced from incineration and LFG was assumed to be convertedto electricity. The residue of the waste incineration processincluded bottom ash and fly ash would be sold as the by-product,while the treatment cost of the residue was included in the incin-eration cost. Other by-products fromwaste treatment technologiesincluded compost and recycled materials. The input for eachtechnology in terms of cost analysis and emission rate is shown in

Table 3The composition and waste-related data in IM.

Types Composition (%)a LHV (MJ/kg)a Carbon Content (%)b

Food 41.1 5.26 41.47Yard 2.5 0.48 37.37Paper 20.9 3.08 42.61Plastic 22.2 5.38 60.93Glass/Ceramic 3.6 0.01 0Metal 2.0 �0.01 0Textile 7.7 2.48 60.42Total/average 100 16.68 34.68

a Low Carbon Society Blueprint for Iskandar Malaysia 2025, 2012.b Tchobanoglous et al., 1993.


Table 5. The by-product selling price is presented in Table 6. Toincrease the development of RE in Malaysia, feed-in tariff policieswere designed to offer guaranteed prices for fixed periods of timefor RE, depending on the types of technologies and capacity (PusatTenaga Malaysia, 2010). The feed-in tariff for MSW in Malaysia ispresented in Table 7. Table 8 presents the RE demand for IM.

3.3. System boundaries and major assumptions

Several assumptions were made for the model developed in thisstudy:

(a) The practice of waste segregation assumed with a rate of100%, the ideal value. Different types of waste were pro-cessed according to their respective potential values.

(b) The recovery factors used for MRF is assumed to be 90%.(c) The study aimed to synthesise an optimal processing

network for wastemanagement. The system did not considerthe transfer and transportation cost of MSW.

(d) At least one year was required to construct the WTR plants(MRF and composting) and three years for WTE plants(LFGRS and incineration).

4. Results and discussion

In this section, themodelling and optimisation results generatedfor all four scenarios involving different waste treatment

Table 4Waste allocation to technologies.

Food Yard Paper Plastic Glass Metal Textile

LFGRS U U U U

Incineration U U U U U U U

Composting U U U

MRF U U U U U


Table 5Cost analysis and emission rate of technologies (EIA, 2010).

Capital cost O&M cost Variable cost Operationtime

GHGemissions

USD/t USD/t USD/t d/y kCO2/tLRGRS 500 0.4 1.3 360 0.2Incineration 800 0.6 2.0 292 0.5Composting 250 0.2 0.5 292 0.05MRF 200 0.3 0.8 292 3.6

Table 7Feed-in tariff for RE from MSW in Malaysia (Pusat Tenaga Malaysia, 2010).

RE utilisation Year RM/kWh Degression (%)

Biomass<10 MW 16 0.31 0.510 MW, �20 MW 16 0.29 0.520 MW, �30 MW 16 0.27 0.5Bonus for MSW 16 0.10 1.8Biogas<4 MW 16 0.32 0.54 MW, �10 MW 16 0.30 0.510 MW, �30 MW 16 0.28 0.5Bonus for landfill 16 0.08 0.5


technologies are presented and discussed. Sensitivity analyseswereperformed based on the optimal solution to evaluate the impact offluctuations in the product price, product demand and GHGreduction demand on the system configuration with regards to theprofitability of the MSWM system.

The data were input to the MILP model for the MSWM systemand optimised with the CPLEX solver (version 12.3) from GAMSsoftware (version 22.9) (GAMS Development Corporation, 2013).

4.1. Comparison of different waste management scenarios

Table 9 shows the analysis of the MSWM system for all fourdifferent scenarios in IM, namely BAU, WTE, WTR, and MIXTECH.

The BAU scenario assumed that there was no introduction ofalternative waste treatment technologies and maintained the cur-rent practices of MSWM. Under the BAU scenario, 93% of wastegenerated in IM was directly allocated to the landfill site, and theremainder (7%) was recycled by the MRF. Meanwhile, there was noenergy or fertiliser production from the waste to achieve themaximum value of waste utilisation. A negative annual profit waspredicted in the BAU scenario due to the high maintenance andoperation cost for managing the landfills without any revenuegenerated from any value-added products from the waste. The BAUscenario also produces the highest carbon emission (3.285 Mt CO2

eq/y) among all four scenarios.The WTE scenario was designed to anticipate a MSWM system

that considered two major WTE treatment technologies, namelythe LFGRS and waste incineration. A total of USD 276.52 M/y of netprofit was obtained for theMSWM system under theWTE scenario,where 15% and 59% of waste was allocated for incineration andLFGRS respectively. Only 27% of the waste was recommended forrecycling by the model. The energy production from the WTEscenario was extremely high (8594.13 GWh) compared to the tar-geted renewable energy (RE) demand of IM (2285.71 GWh)(Blueprint for Iskandar Malaysia, 2010b). The overproduction ofenergy in the form of electricity from MSWM increased the in-vestment cost (USD 7692.86 M/y) and resulted in energy waste.Nonetheless, the WTE scenario showed the highest environmentalprotection with the lowest carbon emission (0.195 Mt CO2 eq/y)among all scenarios.

As an alternative to landfilling, maximal recycling and com-posting capabilities were introduced under the WTR scenariowithout the consideration of WTE. The lowest positive profit (USD34.67 M/y) was predicted under theWTR scenario compared to the

Table 6Selling price of by-product.

Price Reference

Electricity, USD/kWh 124.40 Hashim and Ho, 2011Compost, USD/t 153.37 Rodionov and Nakata, 2011Paper, USD/t 38.16 MHLG, 2012Plastic, USD/t 204.16 MHLG, 2012Glass/Ceramic, USD/t 45.08 MHLG, 2012Metal, USD/t 229.01 MHLG, 2012Textile, USD/t 45.08 MHLG, 2012


WTE and MIXTECH scenarios because the selling of by-productsfrom recycling and composting alternatives was not financiallyfavourable compared to energy as a by-product. Waste (98%) wassuggested to be recycled (56% to MRF, 42% to composting), whileonly 2% of unrecyclable waste was suggested to be landfilled.

To achieve the optimal solution of MSWM that would fulfil thetargets of recycling and RE in IM, the MIXTECH scenario was pro-posed. The optimisation result from Table 9 shows that the optimalprofit of USD 101.85 M/y was achieved annually under the MIX-TECH scenario. The MIXTECH scenario suggested a best mix ofwaste utilisation technologies to be implemented in IM with themaximum net profit and without exceeding the product demand.The percentage of waste allocation were recommended to be 3, 14,56, and 27 for incineration, LFGRS, MRF and composting, respec-tively. The MIXTECH scenario promoted waste recycling and com-posting due to their lower investment cost while introducing WTEtechnology (LFGRS and incineration) to fulfil the RE target for IM.The LFGRS exhibited good potential to generate sufficient energyfor the IM region. Waste incineration was less attractive due to itshigh investment cost. TheMIXTECH scenario achieved the RE targetof IM (2285.71 GWh/y) with lowered GHG emissions (0.992 Mt CO2

eq/y) compared to the other scenarios.

4.2. Optimal planning under MIXTECH scenario

The MIXTECH scenario successfully planned an optimal solutionfor the MSWM system as proposed for the IM region. Three mainobjectives for effective and sustainable MSWM planning wereachieved under the MIXTECH scenario, these included a best mix ofwaste utilisation technology with i) maximising the profit of theMSWM system, ii) achieving the RE target demand, and iii) pro-moting recycling and composting. The optimiser suggested acombination of incineration, LFGRS, MRFs and composting to beimplemented in IM. The MSWM planning for the period of year2013 to year 2025 is presented in Fig. 3.

As indicated in Fig. 3, the earliest construction of waste treat-ment technologies would begin in 2012. The results suggested thatan incineration power plant of 10 Mt/y should be constructed by2012 in IM to achieve the RE target. In addition, two LFG plantsshould be constructed in 2013 and 2018 with capacities of 45 Mt/yand 50 Mt/y. In addition, three recycling plants were suggested tobe constructed in 2013, 2014, and 2015 with capacities of 40 Mt/y,40 Mt/y and 22 Mt/y, respectively. Composting plants with capac-ities of 34 Mt/y and 36 Mt/y were recommended to be constructedby 2013 and 2020 as the landfill sites would become limited due torapid development of IM in year 2013 and beyond.

Table 8RE demand from MSW in IM (Blueprint for Iskandar Malaysia, 2010b).

Key targets for RE 2010 2015 2020 2025

RE from MSW (MW) e 25 50 50


Table 9Analysis of four scenarios for MSWM system in Iskandar Malaysia (IM).

Unit BAU WTE WTR MIXTECH

Financial planning

Net profit M USD/y �43.09 474.04 34.67 101.85

Total system cost M USD/y 58.53 7692.86 233.37 1787.14Total revenue M USD/y 15.44 8164.29 268.04 1889.29Unit treatment cost M USD/t 0.0015 0.1100 0.0034 0.0400GHG emission Mt CO2 eq/y 3.285 0.195 1.459 0.992Energy production GWh/y n/a 9594.13 250.30 2285.71Percentage of waste allocation to technologiesIncineration % n/a 15 0 3Landfill % 93 n/a n/a n/aLFGRS % n/a 59 2 14MRF % 7 27 56 56Composting % n/a n/a 42 27GHG emission percentageIncineration % n/a 35 0 6Landfill % 98 n/a n/a n/aLFGRS % n/a 58 1 23MRF % 2 7 9 3Composting % n/a n/a 90 67

-80% -60% -40% -20% 0% 20% 40% 60%

Energy production

GHG Emission

Compost production

Unit treatment cost

Total system cost

Total revenue

Net profit

Change in overall economic potential of MSWM

Sensitivity analyses of RE demand

50%30%20%-20%-30%-50%

Change of energydemand

Fig. 4. Sensitivity analyses for RE demand on the overall costing and GHG emissions ofthe MSWM system.


4.3. Sensitivity analyses

Sensitivity analyses were conducted to observe the effects ofenergy demand and the GHG emission reduction target on theconfiguration of the MSWM system in terms of waste allocation toeach technology. As MIXTECH scenario was found to be the bestscenario in this study, sensitivity analyses were conducted solely onthis scenario with respect to the change of the RE target and thereduction of GHG emission target.

60

70

80

90

100

on to

tec

hnol

ogie

s (%

)

Sensitivity analyses of energy demand on waste utilisation

4.4. Sensitivity analyses on the RE target

One of the main products of the MSWM system was RE pro-duction in the form of electricity. Fluctuation in energy demandsfrom MSW was expected to affect the configuration, profitabilityand GHG emissions of the MSWM system under the optimal sce-nario. Sensitivity analyses were conducted on the MIXTECH sce-nario by adjusting the energy demand within �0e50% incrementsand decrements (i.e., �20%, �30% �50%, 20%, 30%, and 50%). Fig. 4shows the change of RE target towards the profitability and GHGemissions of the MSWM system. The increase in energy demand of20%, 30% and 50% provided a positive impact on the overall eco-nomic potential of the MSWM system in terms of net profit, totalsystem cost, unit treatment cost and total revenue, and vice versa.However, the net profit did not necessarily follow the incrementaltrend as at some point, a decrease of net profit was observed withhigher energy demand (þ30% to þ50%). This was mainly due to theincrease in investment or capital cost of the WTE technology at alarger capacity. The change in energy demand also tended to affect

0

10

20

30

40

50

60

Cap

acit

y of

pla

nt (

Mt)

Year

Incineration

LFG Reocvery system

MRF

Composting plant

Fig. 3. MSWM system under the MIXTECH scenario.


the GHG emissions. The increase in energy demand by 30% and 50%would reduce the GHG emissions to 25% and 14%, because both ofthe WTE technologies (LFG and incineration) were implementedwith methane gas or energy recovery, which emitted less GHG tothe environment compared to the WTR technologies that did notconsider methane gas recovery (i.e., during anaerobic composting).As the RE demand decreased to a lower level (i.e., 20%,�20%,�30%,and �50%), an increment of GHG emissions was observed becausethe waste utilisation by WTR technologies increased when the REdemand decreased. Composting was found to be the majorcontributor to GHG emissions among all four waste treatmenttechnologies proposed for the system. The composting processwhich converted the MSW to compost through anaerobic digestionreleased methane gas as the by-product. The methane gas was notcaptured for reuse during composting in this study. Consequently,the analyses tended to favour the behaviour of theWTE process dueto reduced GHG emissions.

Fig. 5 presents the sensitivity analyses correlating energy de-mandwith the change inwaste utilisation in term of percentage (%)of waste allocation to each waste treatment technology. As energy

0

10

20

30

40

50

-50% -30% -20% 0% 20% 30% 50%

Per

cent

age

of w

asre

allo

cati

change of energy demand

Incineration Landfill gas capture Recycling Composting

Fig. 5. Sensitivity analyses of RE demand for different percentage of waste allocation tothe MSWM system.


-350%-300%-250%-200%-150%-100% -50% 0% 50%

Compost production

Total system cost

Total revenue

Net profit

Change on overall economic potential of MSWM

Sensitivity analyses of GHG emission

60%

40%

20%

Change of GHGemission reduction

Fig. 6. Sensitivity analyses for GHG emission reduction on the overall economic po-tential and the production of compost in the MSWM system.


demand increased, the percentage of waste utilisation by WTEtechnologies would increase. Under the MIXTECH scenario, as theenergy demand increased from the base level ((i.e. 0% or no change)to 30%, waste allocation to LFGRS increased from 14% to 18% whileincinerated waste decreased. As the energy demand increased to50%, the percentage of waste allocated to incineration increased to20% while landfilled waste decreased to 4%. These results signifi-cantly illustrated that the type and capacity of waste allocationcould significantly influence the cost forMSWM. As energy demandchanges within �0e50%, only three technologies (incineration,LFGRS and composting) would result in significant change of thepercentages of waste allocation, recycling is least influenced by theenergy demand.

4.5. Sensitivity analyses on the target of GHG emission reduction

To examine the effect of the GHG emission reduction target onthe system configuration and profitability, a sensitivity analysis wasconducted based on the optimal result of theMIXTECH by adjustingthe GHG emission reduction targets with increments of 20%, 40%,and 60% from the baseline year of 2005. Referring to Fig. 6, as theGHG reduction target increased from 20% to 40%, no significantchange was observed in the overall economic potential in terms ofthe total system cost, total revenue, and the net profit because theGHG emissions generated under the MIXTECH scenario werealready in the range of lower than 40% of the reduction target in

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60%

Per

cent

age

of w

aste

allo

cati

on t

o te

chno

logi

es

(%)

Change of GHG emission reduction

Sensitivity analyses of GHG emission reduction on waste utilisation

Incineration Landfill gas capture Recycling Composting

Fig. 7. Sensitivity analyses for the change of GHG emission reduction target on thepercentage of waste allocation in the MSWM system.


GHG emissions compared to the baseline. As the reduction target ofGHG emissions increased to 60% compared to the BAU scenario, anegative impact of�300% to net profit was observed for the system.A high percentage of the GHG reduction target would require theintegration of large portions of WTE energy technology. This inte-gration significantly increased the investment cost and henceoverweighed the increase in the revenue generated. The incrementin the GHG reduction target also tended to reduce the production ofcompost in the MSWM system. When the GHG emission targetsincrease by 20%, 40%, and 60% from the baseline, the production ofcompost would be reduced by 17%, 33%, and 42%, respectively. Thisresult indicated that the composting technology emitted higherlevels of GHG through the anaerobic digestion process without therecovery of methane.

Fig. 7 presents the configuration of waste utilisation for thesensitivity analysis of GHG emission reduction. At the lower level ofGHG emission reduction targets (20% and 40%), there was no sig-nificant impact on the configuration of the waste allocation. As theGHG emission reduction target increased to 60%, the percentage ofwaste utilisation by WTE technologies would increase by 18% forincineration and 9% for LFGRS. In addition, the allocation of wastefor composting would decrease from 27% to 17%. This result illus-trated again that the composting technology was the key contrib-utor to GHG emissions.

5. Conclusions

A multi-period MILP model for optimising the MSWM systemwas developed for the IM region in this study. This study indicatedthat waste treatment technologies including incineration, LFGRS,composting, and MRFs could provide attractive economic benefitsand RE options compared to the existing MSWM system in the IMregion. The developed model was tested using different scenariosettings to analyse the economic feasibility and potential of aneffective MSWM system. The model with the MIXTECH scenarioemphasised a cost-effective waste processing network that couldprovide a maximum net profit of USD 101.85 M/y in IM. The bestmix of waste utilisation technologies in terms of % of waste allo-cation to the following technologies were LFGRS (14%), incineration(3%), recycling (56%) and composting (27%). The best mix of tech-nology selection would be able to achieve the RE target and therecycling target and promote composting as an improved wastereduction strategy for the studied region. The results of the sensi-tivity analyses explained that the technology selection of theMSWM system was highly influenced by the costs of technologies,product (RE) targets and GHG emission reduction targets.

This model could be extended to include the costs of land areafor LFG recovery and composting plants, as both technologies couldbe economically beneficial but may be restricted by the availabilityof land in a country. Moreover, the cost of transporting waste to theprocessing plants, the variety of waste treatment technologies andproducts, environmental factors and the locations of waste treat-ment plants should be considered in the future.

Acknowledgements

The authors gratefully acknowledge the Ministry of Higher Ed-ucation (MOHE) and University Teknologi Malaysia (UTM) forproviding the research grant under Vote No. Q.JI3.2525.01H52. Theauthors also acknowledge the Japan International CooperationAgency (JICA) under the scheme of Science and TechnologyResearch Partnership for Sustainable Development (SATREPS) forthe project entitled Development of Low Carbon Scenarios for AsianRegion.



Nomenclature

AcronymsBAU Business as usual scenarioGAMS General Algebraic Modeling SystemGHG Greenhouse gasIM Iskandar MalaysiaLFGRS Landfill gas recovery systemLP Linear programmingMILP Mixed integer linear programmingMIXTECH Mixed technologies scenarioMOP Multi-objective programmingMRF Material recycling facilitiesMSW Municipal solid wasteMSWM Municipal solid waste managementNLP Non-linear programmingO&M Operation and maintenanceRE Renewable energyRM Ringgit MalaysiaSWM Solid waste managementUSD United State DollarWTE Waste-to-energy scenarioWTR Waste-to-recycling scenario

Setsi Resource/productp Process/technologyt Periodz Capacity of plant

ParameterACPCOSTpzt Annualised capital cost of technology p with capacity z

at period t (USD/t)CERT Carbon emission reduction target for the systemEFp Emission factor for process p.MPSMip Material process selection matrixPRCMpi Process resource conversion matrixPRICEit Unit price of product i in period t (USD/unit)PRODEMit Product demand for product i at time t (units/y)RESit Amount of resource i in the system during time period t

(t/y)UPCOSTptUnit processing cost of process p in period t (USD/unit)UVCostpt Unit variable cost of the corresponding process p in time t

(USD/unit)

VariablesCAPpz Capacity of the process p with size zCCOST Capital cost (USD)CEPpt Carbon emission from process p in period t (t/y)EXRECit Input rate of external resource i during period t (t/y)MATipt Input rate for material i into process p during period t

(units/y)PCOST Total processing cost of the resource (USD)PRESpt Quantity of the resource being processed by process p at

period t (units/y)PROit Production rate of product i during period t (units/y)PROFIT Overall profit (USD)REV Product revenues of the MSWM system (USD)SGRESipt Quantity of resource i generated within the system under

process p through period t (t/y)VCOST Total variable operating and maintenance cost of the

system (USD)YOPpzt Binary decision variable to decide whether the process p

should be operated at size z during period t


YPpz Binary decision variable for purchasing technology pwithcapacity z

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