Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW)

Energy Conversion and Management xxx (2014) xxx–xxx

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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW) Wendy Pei Qin Ng a,⇑, Hon Loong Lam a, Petar Sabev Varbanov b, Jirˇí Jaromír Klemeš b a

Department of Chemical and Environmental Engineering, The University of Nottingham, Malaysia Campus, Broga Road 43500, Semenyih, Selangor, Malaysia } Faculty of Information Technology, University of Pannonia, Centre for Process Integration and Intensification – CPI2, Research Institute of Chemical and Process Engineering – MUKKI, Egyetem u. 10, H-8200 Veszpre˙m, Hungary b

a r t i c l e

i n f o

Article history: Available online xxxx Keywords: Waste-to-Energy Supply chains Boundaries Municipal Solid Waste Cogeneration

a b s t r a c t MSW has been identified as one of the alternative energy sources that can be used for electricity and/or power generation. This appears to be one enhanced channel to tackle MSW disposal problem. WTE concept is incorporated into the MSW management system in this work. The integrated system is modelled to study its practicability and significance. The proposed model is illustrated with a case study involving the supply network design and the utilisation of MSW from urban sources. The modelling steps involve the generation of a superstructure, mathematical model construction, optimisation and solution interpretation. The MSW availability and its utilisation are investigated through its supply network design. Optimal locations of processing hubs and facilities are determined. Following this, boundaries and sizes of the processing hubs are calculated. The benefits of WTE strategy from MSW is analysed and its energy generation potential is demonstrated. This WTE strategy acts as one potential MSW management scheme for all interested parties. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction The global trend of energy use is moving towards sustainable development and the waste-to-energy (WTE) concept is being highly promoted as a part of this effort. Over years biofuel production from agricultural crops and biomass as well as energy recovery from MSW have been actively investigated. Energy security issues and the degradation of environment due to human activity raised global concern at the same time. MSW contributes to this environmental impact issue, yet it acts as a potential energy source for energy recovery [1]. With proper waste handling and management practice, MSW treatment can reduce environmental impacts and it can replace some part of primary energy currently supplied by fossil fuels [2]. This utilisation of MSW for WTE acts as one potential solution for modern MSW management and reduction. Municipal solid waste (MSW) comprises of waste generated from residential, commercial, institution and public parks [3]. The rate of MSW generation goes proportionally with global population growing rate. The acceleration in urbanisation and increasing

⇑ Corresponding author. Tel.: +60 3 8924 8716; fax: +60 3 8924 8017. E-mail addresses: [email protected] (W.P.Q. Ng), HonLoong.Lam@ nottingham.edu.my (H.L. Lam), [email protected] (P.S. Varbanov), [email protected] (J.J. Klemeš).

income per capita further speeded up the MSW generation rate. This MSW generation burdens the local governments especially developing countries to collect, handle and dispose of MSW efficiently. The general pathways of MSW treatment are introduced in Section 2. Proper MSW management ensures environmental, economic and social sustainability [4]. MSW processing reduces the volume sizes of MSW, which in turn, extends the lifetime of dump sites. Certain MSW processing can reduce environmental impacts through net reduction in greenhouse gases emission and material recovery from process residues [5]. In this work, a mathematical model is developed to investigate the feasibility of these MSW processing application and the economic performance of the model under the application of these technologies. Proper supply network model is generated and optimal MSW integrated system is proposed as a suggestion to efficient MSW management. In the mathematical model multiple objective functions, which catch the interest of MSW processing and MSW supply network design are introduced. They include economic criteria, waste volume reduction and so on. All these objectives are optimised simultaneously by introducing a single parameter, which integrates all objectives into one via fuzzy optimisation. Fuzzy optimisation allows for multi-criteria modelling. It tolerates additional information together with the cost-benefit relations, which in turn, gives

http://dx.doi.org/10.1016/j.enconman.2014.01.004 0196-8904/Ó 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Ng WPQ et al. Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW). Energy Convers Manage (2014), http://dx.doi.org/10.1016/j.enconman.2014.01.004

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W.P.Q. Ng et al. / Energy Conversion and Management xxx (2014) xxx–xxx

Nomenclature a b Bb,j

F6max b

set index of MSW source points set index of potential hub binary variable denoting the selection of technology j in hub b total waste collection fee, which the fee includes the MSW processing facility operating cost, landfill disposal cost for residues and any revenues from value-added products/energy recovered/electricity generated (USD/d) total transportation cost assuming a flat rate of charges (USD/d) fraction of component i in MSW (1) distance from source a to hub b (km) flowrate of component i in MSW (t/d) recyclables flowrate (t/d) flowrate left-over MSW after removal of recyclables (t/d) flowrate of MSW sent to technologies j for conversion (t/d) product flowrate in hub b produced from technology j (t/d) maximum product flowrate in hub b produced from technology j (t/d) minimum product flowrate in hub b produced from technology j (t/d) maximum MSW weight reduction in hub b (t/d)

F6min b

minimum MSW weight reduction in hub b (t/d)

C1

C2 COMi DTa,b F1a,i F2a,i F3a,b F4b,j F5b,j,k F5max b;j;k F5min b;j;k

more precise solution in many real situations [6]. In fuzzy optimisation model, fuzzy parameter is introduced. This parameter, a continuous interdependence variable, is maximised subject to predefined individual upper and lower limits of each objective. It is represented to achieve a level of satisfaction when multiple objectives are considered. This theory of decision-making parameter which satisfies multiple objectives in fuzzy environment was first introduced by Bellman and Zadeh [7]. The approach is then further extended by Zimmermann [8] to solve multiple fuzzy objective functions and programming problems.

2. General MSW processing technologies The MSW is generally treated in three ways [9]: i. Thermal conversion. ii. Biochemical conversion. iii. Landfilling. Thermal conversion of MSW uses heat energy to reduce the volume of MSW and generate biofuels, e.g. syngas, char, bio-oil, etc. Typical thermal conversion technologies include incineration, pyrolysis and gasification. Biochemical conversion of MSW uses enzymes and micro-organisms to break down organics for biogas production and collection of value-added products. Biochemical conversion processes include anaerobic digestion, fermentation and composting. Nevertheless, all thermal and biochemical conversion processes leave MSW residue which is inert and has to be landfilled. Landfilling appears to be the terminus step for MSW and MSW residue disposal. Fig. 1 illustrates the general MSW processing technologies and their typical products. However, it shall be noted that there is less common and higher value product produced from MSW, such as alcohol. Several important studies of MSW utilisation which developed from this conventional MSW processing trend are listed in Table 1.

i j k lata latb LBj lona lonb MSW MSWa OBJ PRj,k r RECi SPAj TC TEG TTCmax TTCmin TIPj UBj

set index for MSW components i in MSW set index for MSW processing technologies degree of satisfaction/fuzzy variable (1) radian coordinates – latitude of source point a radian coordinates – latitude of destination (hub) b lower bound of the technology’s operating capacity (20% of the maximum operating capacity) radian coordinates – longitude of source point a radian coordinates – longitude of destination (hub) b municipal solid waste MSW flowrate from source a (t/d) model objective function conversion rate of each technology j earth radius (6371 km) recycling fraction of MSW type i (1) amount of MSW reduced in weight fraction (1) transportation cost parameter (0.10 USD/t of material delivered) total amount of electricity generated (kW h/d) maximum waste collection and transportation cost incurred from MSW processing (USD/d) minimum waste collection and transportation cost incurred from MSW processing (USD/d) waste collection fee for each individual MSW conversion technology (USD/t of MSW processed) upper bound of the technology’s operating capacity (t/d)

3. Problem statement A specific Malaysian scenario with waste recycling and MSW treatment is introduced, and a novel operation and supply network for MSW management is proposed. MSW recycling is focused on commonly recycled items: paper, plastic and metal. MSW screening and recycling is assumed to be carried out at MSW source points. The general MSW handling flow is illustrated in Fig. 2. MSW i 2 I is collected at landfill sources a. These MSW are first screened and classified based on their properties. A portion, X%, of the recyclables, e.g. plastics, papers and metals are screened out and sent to recycle centres for the material recycle. Organics such as food waste, yard waste, and combustibles, e.g. non-recyclable plastics, papers, textiles, rubbers, are sent for further treatment under various processing technologies j 2 J at processing hubs b. Various types of products k 2 K are produced and the rest of residues/ inert are sent for landfilling. The superstructure of the supply network model is shown in Fig. 3. The optimum allocation of MSW to each processing hub b is studied. The number and location of processing hub is to be determined by the optimisation model. Integrated waste management system that implements more than one MSW processing facility is to be practiced in each hub. Optimal MSW supply network design is to be generated and the feasibility of MSW management practice in local application investigated. 4. Model formulation The MSW consists of components i, e.g. organic waste, papers, plastics, etc. The amount of each component i in MSW, F1a,i (t/d) is represented:

F1a;i ¼ MSW a  COMi

8a 2 A; i 2 I

ð1Þ

where MSWa is the MSW flowrate from source a (t/d); COMi is the fraction of component i in MSW.

Please cite this article in press as: Ng WPQ et al. Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW). Energy Convers Manage (2014), http://dx.doi.org/10.1016/j.enconman.2014.01.004

W.P.Q. Ng et al. / Energy Conversion and Management xxx (2014) xxx–xxx

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Fig. 1. Typical MSW processing technologies and their products [10].

Table 1 Development of MSW utilisation from conventional MSW processing technologies. Development of MSW Utilisation

References

Ferrous scrap recovery from MSW incineration

Quality of ferrous scrap from MSW incinerators [11] Recovery of packaging steel scrap [12] Environmental impact assessment: bioethanol from MSW [13] MSW for alcohol production [14] Effective utilisation for Zeolite synthesis [15] Feasibility study of MSW power generation [16] Energy utilisation from MSW incineration [17] Energy generation potential under different technologies [18] MSW energy generation in India [19] WTE scenario with pretreatment [20] Potential MSW energy contribution to electricity demand [21] Co-digestion of leather fleshing waste using MSW [22] Environment and economic impact analysis [23] Effect of MSW compost on plant nitrogen uptake [24]

Bio-ethanol/alcohol production from MSW MSW and coal co-combustion waste ash utilisation MSW as a source of energy/power generation

Optimising biogas production Energy network evaluation Compost

Sources a

MSW collection

Processing hubs b MSW processing - incineration - anaerobic digestion - gasification - pyrolysis - composting

MSW

MSW screening & classification

organics, combustibles, etc.

inerts recyclables

value-added products / biofuels

inerts / residues MSW landfilling

MSW landfilling

Fig. 2. Facilities and material flow of the proposed MSW handling model at each site/location.

Recyclable MSW collected at source a is screened and classified for recycling:

F2a;i ¼ F1a;i  REC i

8a 2 A; i 2 I

ð2Þ

where F2a,i is the recyclables flowrate (t/d); RECi is the recycling fraction of MSW type i (0.80). The non-recycled MSW from source a is transferred to potential centralised processing points (hub) b for further processing, which these potential hubs b are proposed to be located in source points a. Therefore, the locations of a = b.

X X F3a;b ¼ F1a;i  F2a;i b2B

8a 2 A

ð3Þ

i2I

where F3a,b is the flowrate of left-over MSW (t/d). Note, index i for component reference is disregarded from this point onwards. It is

assumed that MSW enters the following processing technologies as a whole without any further MSW classification/extraction. In hub b, MSW received is sent to technologies j with flowrate F4b,j (t/d) for further conversion to value-added products or heat energy for electricity generation:

X X F3a;b ¼ F4b;j  Bb;j a2A

8b 2 B

ð4Þ

j2J

F4b;j  PRj;k ¼ F5b;j;k

8b 2 B; j 2 J; k 2 K

ð5Þ

where Bb,j is the binary variable denotes the selection of technology j in hub b; PRj,k is conversion rate of each technology j; F5b,j,k is the product flowrate in hub b produced from technology j (t/d). The MSW sent to all technologies j in hub b is constrained to its availability from source a and all MSW sent to hub b from source a

Please cite this article in press as: Ng WPQ et al. Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW). Energy Convers Manage (2014), http://dx.doi.org/10.1016/j.enconman.2014.01.004

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W.P.Q. Ng et al. / Energy Conversion and Management xxx (2014) xxx–xxx

Fig. 3. Superstructure of WTE supply network model.

should go through a technology j either for processing or landfill disposal:

X X F4b;j ¼ F3a;b

8b 2 B

ð6Þ

a2A

j2J

The amount of MSW sent to each technology j is capped at an upper bound for its maximum operating capacity and lower bound for its minimum operating capacity to achieve efficient equipment investment and operation:

F4b;j 6 UBj

8b 2 B; j 2 J

F4b;j P LBj  Bb;j

8b 2 B; j 2 J

ð7Þ ð8Þ

where UBj and LBj are upper bound and lower bound of the technology’s operating capacity. The lower bound is set at 20% of the maximum operating capacity as a minimum operational flowrate of equipment. For MSW processed through technology j except landfill, waste volume reduction is achieved. The amount of MSW reduced, F6b, is:

F6b ¼

X F4b;j  SPAj

8b 2 B

ð9Þ

where DT is distance (km); lata, latb, lona, lonb are the radian coordinates of respective source and destination (hub) points; r is the earth radius (6371 km) [26]. The transportation cost for MSW, C2 (USD/d), is estimated using the following equation, assuming a flat rate of charges:

X

C2 ¼

C1b ¼

X F4b;j  TIPj

8b 2 B

ð10Þ

j2J

where TIPj is the waste collection fee for each individual MSW conversion technology (USD/t of MSW processed). The length of distance travelled for MSW delivery contributes to the optimality of supply network design. The great-circle distance of MSW delivered from its source point to its processing hub is estimated using Spherical Law of Cosines [25]:

TTC ¼

X C1b þ C2

ð13Þ

b2B

The total amount of electricity generated from MSW processing, TEG (kW h) is given by the following equation:

X

TEG ¼

F5b;j;k

ð14Þ

b2B;j2J;k2electricity

The total capital cost incurred TC3 (USD) is calculated using Eq. (15):

TC3 ¼

X Bb;j  CAPj

ð15Þ

j2J

where CAPj is the capital cost of each MSW processing unit operation (USD). This integrated MSW management can be optimised to achieve optimal MSW processing technologies selection and supply network design. The model can be optimised based on several criteria: (1) Maximum economic performance through minimum cost incurred. (2) Maximum space allowance through maximum MSW volume/weight reduction. (3) Maximum energy recovery for local energy security enhancement through maximum electricity generation. The respective interdependent fuzzy variable, which forms the limiting parameter for objectives satisfaction is formulated:

DT a;b ¼ a cosðsinðlata Þ  sinðlatb Þ þ cosðlata Þ  cosðlatb Þ

TTC max  TTC

 cosðlonb  lona ÞÞ  r

8a 2 A; b 2 B

ð12Þ

where TC is transportation cost parameter (0.10 USD/t of material delivered) [27]. The total waste collection and transportation cost for MSW, TTC (USD/d) is given by the following equation:

j2J

where SPAj is amount of waste reduced in weight fraction. The MSW received from each source is charged a waste collection fee, C1 (USD/d), which the fee includes the MSW processing facility operating cost, landfill disposal cost for residues and any revenues from value-added products/energy recovered/electricity generated:

DT a;b  F3a;b  TC

a2A;b2B

ð11Þ

TTC max  TTC min

Pk

ð16Þ

Please cite this article in press as: Ng WPQ et al. Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW). Energy Convers Manage (2014), http://dx.doi.org/10.1016/j.enconman.2014.01.004

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W.P.Q. Ng et al. / Energy Conversion and Management xxx (2014) xxx–xxx

X F6b  F6min b b

F6max  F6min b b

TEG  TEGmin TEGmax  TEGmin

ð17Þ

Pk

ð18Þ

Pk

where k is the degree of satisfaction/fuzzy variable; TTCmax and TTCmin are the maximum and minimum waste collection plus transportation cost incurred from MSW processing and MSW delivery respectively; F6max and F6min are the maximum and minimum b b MSW weight reduction in hub b; TEGmax and TEGmin are the maximum and minimum amount of electricity produced from all hub b (kW h). The optimisation objective of this model is to minimise cost and maximise MSW weight reduction as well as the electricity production. Fuzzy multi-objectives optimisation approach is employed to determine the compromising optimisation objectives. The objective function, OBJ, is to maximise the fuzzy variable:

OBJ ¼ MAXk

ð19Þ

5. Illustrative case study A Malaysian local case study in Selangor is presented to demonstrate the application of the proposed model. The case study data is adjusted based on actual MSW statistics, MSW sources distribution, locally available/feasible MSW processing technologies and

so on. Local MSW landfill sites and MSW transfer stations are taken as MSW source points. Table 2 shows the estimated global positioning system (GPS) coordinates of identified MSW source points, a. All these source points act as potential processing hubs (centralised or decentralised processing facility), b, for MSW processing. The model is let free for hub selection, which centralize or decentralise processing hub has equal chances to be selected based on system favourability. This potential processing hub(s) b is/are assumed to be built within the MSW source points a. In this model, eight MSW source points are identified with their respective MSW availability flowrate listed in Table 2. In order to allow for possible double installation or double capacity of MSW processing facility, two potential hubs b are set available at the same coordinates. If both hubs at the same coordinates are chosen, these hubs are assumed to combine to form larger hub. Generally, MSW has unique characteristic components for each source. The MSW collected at each source point is assumed to have the same characteristic components. The generalised MSW characteristic components in Malaysia as compiled by Johari et al. [4] are taken as the MSW characteristics for these MSW sources (Table 3). MSW can be used to generate biofuels, value-added products or directly disposal by landfilling as shown in Fig. 1. In this case study a recyclable MSW is screened and classified at the source points. These recyclables, according to the fraction amount being recycled as shown in Table 5, are sent for recycle and the rest of the MSW is sent for a list of possible processing technologies at hub b. In this local case study, the processing cost associated to MSW screening

Table 2 GPS coordinates and MSW availability flowrate of each MSW source points. MSW source point

Latitude (°)

Longitude (°)

MSW flowrate from source a (t/d)

Potential hub points

a1 a2 a3 a4 a5 a6 a7 a8

3.191008 2.742300 3.496289 3.220230 3.063897 3.690656 3.605833 3.425594

101.367372 101.524700 101.480653 101.664840 101.552211 100.963906 101.540500 101.549056

2000 1000 1500 2100 1200 1000 1000 1000

b1/b9; b1 [ b9 = b101 b2/b10; b2 [ b10 = b102 b3/b11; b3 [ b11 = b103 b4/b12; b4 [ b12 = b104 b5/b13; b5 [ b13 = b105 b6/b14; b6 [ b14 = b106 b7/b15; b7 [ b15 = b107 b8/b16; b8 [ b16 = b108

Note: There can be only one hub b is formed at one source point a, however, if two hubs b are formed at the same coordinates, these two hubs are assumed to combine to form a larger hub with double units of MSW processing facilities. For example, if b1 and b9 are chosen in the model, b1 and b9 combine to form b101.

Table 3 The average MSW characterisation in Malaysia. General MSW component

Weight percentage (%)

General MSW component

Weight percentage (%)

Organic Plastic Paper Textile Rubber

38.00 18.92 16.78 8.48 1.32

Yard waste Glass Metals Others

6.96 2.68 3.40 3.46

Table 4 List of possible technologies for MSW processing, their individual capacities, waste collection fee charges for each unit of MSW processed, % of MSW reduction by weight after processing and products from each technology for every t of MSW processed. Technology j

Composting Anaerobic digestion Gasification Pyrolysis Incineration Landfill

Capacity (t/d)

Capital cost (USD)

Waste collection fee (USD/t MSW processed)

MSW weight reduction (wt% of MSW processed)

Products k Slag (t/t MSW)

Metal (t/t MSW)

Electricity (kW h/t MSW)

1000 1115 312 350 520 –

45  106 95  106 82.7  106 83  106 27  106 –

70 130 131 200 67 46

47.5 76.5 90.0 83.5 75.0 –

– – 0.082 – – –

– – 0.009 – – –

– 187.5 1000 490 340 –

Please cite this article in press as: Ng WPQ et al. Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW). Energy Convers Manage (2014), http://dx.doi.org/10.1016/j.enconman.2014.01.004

W.P.Q. Ng et al. / Energy Conversion and Management xxx (2014) xxx–xxx

Table 5 Model parameters. Parameter

Value

Recyclables Paper Plastic Glass Lower limit of MSW technology (if chosen) Transportation cost

70 wt% 70 wt% 70 wt% 20% of technology capacity 0.10 USD/t of MSW delivered

and classification is not considered to be considerable and in the simplified study can be not considered. It is a Malaysian local practice that recyclables separation are carried out at source point by residents and then collected by private recycling companies. If the screening of MSW were to be carried out at the processing site, it is assumed that the separation cost can be covered by the revenue from selling recyclables to recyclables collector. However, it should be noted that in some countries, cost associated to MSW separation may be significant, e.g., Santibañez-Aguilar et al. [28] estimated the separation cost to be 0.235 USD/kg in Mexico and in that case should be considered. Table 4 shows the list of MSW processing technologies considered, their corresponding capacities, waste collection fee for each t of MSW processed, MSW weight reduction after processing and products k produced from each t of MSW processed. In this case study, only a few of commonly practised MSW processing technologies are considered. It should be noted that they are several less common MSW processing technologies available, e.g., densification, depolymerisation, etc. [4]. The model parameters, e.g. recycling percentage, etc., are listed in Table 5. This case study information is applied to the equations under the previous section ‘Model formulation’. This MINLP model is solved using the modelling system ‘‘General Algebraic Modeling System – GAMS’’ 23.4.3/BARON [29]. These equations are solved simultaneously to satisfy the objective criteria, either to maximise or minimise an objective function to fulfil a specific desire. The model is solved under several optimisation criteria: i. Minimum total incurred cost. ii. Maximum total electricity generated. iii. Maximum total space saving from MSW reduction (TF6) through MSW processing. Fig. 4 shows the results of four different optimisation scenarios. The optimisation result values are normalised by taking the results from multi-objective model as a reference. Although total capital investment is included in the plot, it is not taken as an optimisation criterion. It is assumed that these capital investments is partially subsidised by government and the cost is included in the waste collection fee. The most significant characteristics of MSW processing are waste volume reduction and energy recovery. It has been observed from Fig. 4 that model which considers only the economic criterion do not achieve these objective with no energy and waste reduction being achieved. This is resulted from the allocation of MSW to landfills due to the relatively low waste collection cost of landfilling. The model which takes maximum space saving from MSW processing gives high electricity generation and MSW reduction. However, it results in high costs incurred. An intermediate is obtained for models, which considers maximum electricity generation and multiple objectives. Model that takes maximum electricity generation results in high electricity generation and MSW reduction, yet, it gives high waste collection cost and higher capital investment. Model that considers all objectives gives relatively lower value of electricity generation and MSW reduction, yet it incurs lower waste collection fee and capital investment. This multi-objectives model is believed in the current

Normalised value (model result / multiple-objectives model result )

6

model for minimum collection and transporation cost model for maximum electricity generation model for maximum MSW weight reduction model for multiple objectives 2

1

0 Waste Transportation Capital cost collection cost cost

Electricity generation

MSW weight reduction

Optimisation criteria Fig. 4. Results comparison of four different optimisation scenarios (the results are scaled by dividing the result values obtained from each optimisation model with the result value of multiple-objectives model, this gives a unity value of 1 for all multiple-objective model results).

trend to be more attractive [30] to promote MSW processing as it incurs lower initial expenses. However, with more concerns to environmental degradation and energy security in the coming future, the MSW management trend may follow the result trend showed by maximum total electricity generated model. Users may willingly pay for higher waste collection fee or be charged through forced movement by MSW management authority for more efficient MSW management. The optimum MSW allocation scheme (from source a to hub b) based on multiple objectives optimisation model is tabulated in Table 6. Table 7 shows the MSW allocation to each technology and their respective products in each hub. The limiting parameters for fuzzy optimisation and results of the model are tabulated in Table 8. The result in Table 7 shows that only the techniques gasification, pyrolysis and incineration are chosen for MSW processing. This matches the published investigation by Sakawi [5] that several MSW processing technologies are not matured and economically infeasible: biological treatment or composting of MSW. Especially in Malaysia they are not attractive due to its high cost of investment, possibility of secondary pollution, large area requirement and low value of compost products [5]. Though it is not shown in Table 7, it should be noted that MSW disposal by landfilling exists in each hub as the final solution to the residues formed by each MSW processing techniques. The total amount of MSW reduction in weight is found to be 4388 t/d, which is equivalent to 64% of the initial MSW sent for MSW processing through technology j. In this case study, it is assumed that the MSW processed generates only electricity instead of both steam and electricity via combined heat and power (CHP) system. The vicinity of the Malaysian case study has less focus and demand for steam/heating due to the country’s tropical weather. However, the increase utilisation of recovered heat in tropical country is possible through the set-up of infrastructure for residential use of heat. The use of waste heat has been studied by Bojic´ et al. [31] for low temperature radiant heating; Bruno et al. [32] for absorption cooling, and so on which recovered heat can be used for both heating and cooling purposes. CHP system is proven to be able to recover more energy and achieve higher system efficiency [33]. Based on a heat to power ratio of 1.5 and the amount of electricity generated in this model, a gross estimation of 6859 MW h of steam energy can be generated on top of the proposed 4573 MW h of electricity energy recovered. The generation of cooling effect other than heat and electricity, which is commonly known as, combined cooling, heating and

Please cite this article in press as: Ng WPQ et al. Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW). Energy Convers Manage (2014), http://dx.doi.org/10.1016/j.enconman.2014.01.004

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W.P.Q. Ng et al. / Energy Conversion and Management xxx (2014) xxx–xxx Table 6 MSW allocation from each source a to hub b. Flowrate, F3a,b (t/d)

a1 a2 a3 a4 a5 a6 a7 a8

b101

b102

b103

b104

b105

b106

b107

b108

1462.68 – – – – – – –

– – – – – 326.63 – –

– – 1097.01 – – – – –

– – – – – – 124.00 –

– – – – – – – 124.00

– 731.34 – 1535.81 877.61 – – –

– – – – – – 607.34 –

– – – – – 404.71 – 607.34

Table 7 MSW allocation to each technology j and product k produced in each hub. Hub b

b101 b102 b103 b104 b105 b106

b107 b108 Total

Flowrate, F4b,j (t/d)

624.00 838.68 186.63 140.00 624.00 473.01 124.00 124.00 400.00 624.00 140.00 940.76 607.34 624.00 388.05 6858.47

Technology j

Product k flowrate, F5b,j,k

Gasification Incineration Gasification Pyrolysis Gasification Incineration Gasification Gasification Composting Gasification Pyrolysis Incineration Gasification Gasification Incineration

Slag (t/d)

Electricity (kW h)

5.62 – 1.68 – 5.62 – 1.12 1.12 – 5.62 – – 5.47 5.62 – 31.84

51.17 – 15.30 – 51.17 – 10.17 10.17 – 51.17 – – 49.80 51.17 – 290.11

624,000 285,151 186,634 68,600 624,000 160,823 124,000 124,000 – 624,000 68,600 319,859 607,340 624,000 131,936 4,572,943

Table 8 Limiting parameters and results from the optimisation model of multiple objectives. Parameter and results Fuzzy optimisation constrains Maximum waste collection and transportation cost, TTCmax Minimum waste collection and transportation cost, TTCmin P Maximum MSW weight reduction, b2B F6max b P Minimum MSW weight reduction, b2B F6min b P Maximum electricity generation, b2B;j2J;k2electricity F5max b;j;k P Minimum electricity generation, b2B;j2J;k2electricity F5min b;j;k Total Total Total Total

waste collection cost transportation cost capital cost (based on 20 y lifespan and 330 d/y) electricity generation

Fuzzy variable, k

Unit

Value

USD/d 1,478,574 USD/d 363,330 t/d

Total MSW reduction, F6b (t/d)

Metals (t/d)

6775

t/d

0

kW h

6,416,171

kW h

0

USD/d USD/d USD/d kW h/ d –

7,386,178 39,684 272,606 4,028,774 0.63

power (CCHP) system, is an extension from the conventional CHP system. New fuel sources for CCHP system have been paid more attention to drive the CCHP system. Udomsri et al. [34] has studied the CCHP system fuelled by MSW and stated that CCHP is sustainably and economically attractive. CCHP with higher system efficiency generates electricity and cooling output for the cooling output may overcome the scenario that little need of heat is typically observed in tropical countries as compared to cooling need. It can be observed from the model that MSW processing favour the generation of the technology gasification. This is contributed by its moderate waste collection fee charge, high MSW weight reduction after processing and one of the model objectives to maximise electricity generation. This coincides with the reported statistics by

561.60 284.87 561.60 111.60 111.60 1648.50

546.61 561.60 4387.98

SWANA [10] that MSW processing by gasification is rated as the second most popular commercially applying MSW processing technology globally. Despite its relatively high capital cost, gasification releases lower air emissions than incineration technology and it achieves net reduction in greenhouse gases emission. This case study has been dealing with Malaysian commercial landfill sites and MSW transfer stations as MSW sources. The existing developed MSW collection from urban areas to these sites is not taken into consideration. With the realisation of this MSW management plan, re-structuring of MSW collection and transfer routes can be studied for more efficient logistic management. MSW can be collected and sent directly to the intended MSW processing hub instead of sending to the model source points or landfill sites and, later on, transfer to their respective processing hub. By considering the logistics management, more efficient supply network can be achieved and further cost or emission resulted from this logistic activity can be reduced.

6. Error analysis of the model The cost function incurred for MSW processing – waste collection cost includes the operating cost, capital cost and residue disposal cost by landfilling. The capital cost is not accessed independently and therefore, the payback period and return on investment from setting up these processing hubs are not investigated. This limits the attractive and practicability of the proposed MSW management. However, MSW management and MSW disposal are essential and currently practiced by all countries. MSW disposal has been a critical issue for it takes land space and it incurs continuous cost to all government. It should be noted that proper employment of these MSW processing technologies

Please cite this article in press as: Ng WPQ et al. Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW). Energy Convers Manage (2014), http://dx.doi.org/10.1016/j.enconman.2014.01.004

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prolongs the lifetime of landfilling sites, reduces MSW volume as well as subsidizing part of the MSW management cost incurred by trading off value-added products produced or energy recovered from MSW processing. In addition, the capacity cost of the technologies employed in cost calculation is based on fixed-capacity basis. This may modify the cost potential of each technology at higher capacity since the unit capacity cost at high capacity is typically lower. However, the model developed in this work is formulated to have capacity increment at folds. It is assumed that if higher capacity of a technology is selected, an additional processing line of the initial capacity is installed and therefore, folds the capital cost. It shall be noted that the solution involving many processing facilities or hubs may increase the safety risks of operation, which safety aspect is not considered in this work. Several assumptions are made during model configuration in the case study. It is assumed that MSW collected from all sources have same and constant MSW component composition. The amount of electricity energy and products produced as calculated in the model is based on conversion parameter per unit of MSW processed. This is less realistic and the result figures act only as rough estimation due to the variation of MSW components and properties in feedstock. MSW in reality varies due to different population behaviour and living practices for compositions at each source. The variation of the MSW feedstock composition has not been considered in this model and this simplification affects the accuracy of modelling result. Composition variation in MSW feedstock affects technology efficiencies and product conversion rates. Since MSW is assumed to have constant and same composition in the case study, these technology efficiencies and conversion variations are not investigated. Due to local data limitation, the conversion and cost data used in this Malaysian case study is adapted from North American statistics. However, the country has different MSW composition and technology/operating cost as a result of area technology advancement and labour/utility availability. All these parameters are obtained from the same source of SWANA [10]. This ensures model consistency and relative accuracy of modelling result. 7. Conclusions This paper evaluated the possibilities to utilise MSW for energy generation via WTE in Malaysia, as a tropical developing country. At present, the ‘‘profit’’ of MSW utilisation is not too convincing mainly because of the cost for WTE technologies has not reached to a sufficiently economic level yet. The local government has to deal with the challenge in the trade-off of cleaner environment and the maintenance cost. Based on the WTE trend in Europe and other developed countries [35], this should be one of the solution for a tropical country that generates a lot of MSW in the society. For those reasons the MSW network should be continually developed parallel with the development in new technologies and the society and community being served. Future work should be directed towards the investigation of the set-up of MSW collection/processing facility at different scales, e.g. household, neighbourhood, town, regional scales, etc. The emission effect due to MSW processing/disposal should be incorporated into the model by taking consideration the installation of post-emission treatment facilities. Acknowledgement The authors would like to acknowledge the financial support from the corporate grant sponsored by Global Green Synergy Sdn Bhd, the financial supports by The University of Nottingham Early

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Please cite this article in press as: Ng WPQ et al. Waste-to-Energy (WTE) network synthesis for Municipal Solid Waste (MSW). Energy Convers Manage (2014), http://dx.doi.org/10.1016/j.enconman.2014.01.004