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Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management
Applied Energy xxx (2016) xxx–xxx
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management Wai Shin Ho a,⇑, Haslenda Hashim a, Jeng Shiun Lim a, Chew Tin Lee b, Kah Chiin Sam b, Sie Ting Tan a a b
Process System Engineering Centre (PROSPECT), Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor, Malaysia Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
h i g h l i g h t s A novel method known as Waste Management Pinch Analysis (WAMPA) is presented. WAMPA aims to identify waste management strategies based on specific target. WAMPA is capable to examine the capacity of waste management strategies through graphical representation.
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Article history: Received 15 August 2015 Received in revised form 26 December 2015 Accepted 19 January 2016 Available online xxxx Keywords: GHG emission Pinch Analysis (PA) Solid waste management (SWM) Waste-to-Energy (WtE) Reduce, reuse, and recycle (3R)
a b s t r a c t Improper waste management happened in most of the developing country where inadequate disposal of waste in landfill is commonly practiced. Apart from disposal, MSW can turn into valuable product through recycling, energy recovery, and biological recovery action as suggested in the hierarchy of waste management. This study presents a method known as Waste Management Pinch Analysis (WAMPA) to examine the implication of a dual-objective – landfill and GHG emission reduction target in sustainable waste management. WAMPA is capable to identify the capacity of each waste processing strategy through graphical representation. A general methodology of WAMPA is presented through a demonstration of a SWM case followed by a detailed representation of WAMPA for five waste types. Application of the WAMPA is then applied on a case study for sustainable waste management planning from year 2015 to 2035. Three waste management strategies are incorporated into the case study – landfill, Waste-toEnergy (WtE), and reduce, reuse, and recycle (3R). The results show a 13.5% of total GHG emission reduction and 54.6% of total reduction of landfill are achieved. The major contributor of GHG emission which are from food waste (landfill emission) and plastic (WtE emission) is reduced. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction The generation of municipal solid waste (MSW) has increased in parallel to the rapid population growth, and with changing consumption patterns, economic development and rapid urbanization. Improper waste management happened in most of the developing country where inadequate disposal of waste in landfill is commonly practiced. Improper waste management causes long-term impacts to the environmental, such as pollution of air, soil, surface and ground water, in addition, reduces valuable land space due to landfilling. One of the major consequences of landfill is the generation of methane (CH4) gas from the decomposition of MSW, where ⇑ Corresponding author. Tel.: +60 16 4401186. E-mail address: [email protected] (W.S. Ho).
CH4 contributes to about 21% of global greenhouse gasses (GHG). The negative consequences of landfill are the driving force that pushes governments and municipalities to identify better solutions for waste management planning. Apart from disposal, MSW can turn into valuable product through recycling, energy recovery, and biological recovery action as suggested in the hierarchy of waste management [1]. As the process to identify the optimal strategy for waste management can be rather complex. Regional solid waste management (SWM) strategy are often performed via optimization tool which is often optimized in a ‘‘black-box” mathematical optimization approach, emphasizes the design of a system by a specific objective function that gives the best solution to the objective function [2]. Various types of techniques have been implemented as an optimization model for SWM, such as Linear Programing, Mixed
http://dx.doi.org/10.1016/j.apenergy.2016.01.044 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Nomenclature 3R CEPA CO2 CH4 EROI GHG MILP MSW
reduce, reuse and recycling Carbon Emission Pinch Analysis carbon dioxide methane Energy Return on Energy Investment greenhouse gasses Mixed Integer Linear Programming municipal solid waste
Integer Linear Programming (MILP), Non-linear Programming (NLP), stochastic programming, fuzzy logic and hybrid model. Mathematical optimization approach required the specific mathematical modelling knowledge to develop the models, which might prevent decision and policy makers to understand fully the reason in obtaining the optimal solution. Among the optimization techniques present, Pinch Analysis (PA), which has been largely applied through many applications, is significantly important and has the advantage of allowing users to easily grasp and understand the optimization procedure as PA is often presented graphically. PA was first developed based on thermodynamic principles for the synthesis of heat exchanger networks in the 1970s [3]. The fundamental concept of PA is to maximise the process-to-process heat recovery and minimise the external utility loads. Since then, it has been applied to processing problems beyond heat and energy application. For instance, ElHalwagi and Manousiouthakis [4] adopted PA into mass exchange networks of a chemical process. Wan Alwi and Manan [5] proposed a new graphical tool known as STEP to simultaneously target and design heat exchanger network, which later on, the authors [6] extended to consider the placement of utilities with flue gas. Miah et al. [7] proposed a new practical integration framework to solve complex and diverse production line, involving analysis at zonal and factory level. Liew et al. [8] proposed a PA centric framework to perform the heat integration for a total site problem. PA was further adapted in power system planning by Bandyopadhyay [9] to design an off-grid PV/Battery system. Alwi et al. [10] continue to improve the power for hybrid renewable energy sources known as Power Pinch Analysis (PPA). Ho et al. [11] extend the PPA approach by employing new ways of utilising the Demand and Supply Composite Curve methods for the design of an off-grid hybrid energy systems. Giaouris et al. [12] continue to improve the work on PPA by introducing the Power Grand Composite Curves (PGCC) method to adaptively adjust the system operation in short-term power requirements. Other than energy and power, Manan et al. [13] proposed the used of Water Pinch to target the minimum water flow rate. Ng et al. [14] adopted Water Pinch for wastewater recycling issue. Foo et al. [15] developed algebraic and graphical targeting techniques to design the chilled water and cooling water network. Conventional Pinch Analyses are used to define the target (demand chain) of process system based on the information of stream quantities and quality (supply chain) for a micro-scale industries planning. With contrast to the conventional Pinch approaches, Tan and Foo [16] developed the Carbon Emission Pinch Analysis (CEPA) to address the GHG emission constraints issue of the energy sectors for macro scale regional planning. In CEPA, the carbon reduction target from the energy sector was set based on national or regional development plan, then emissions reduction action is decided to achieve the set target. Tan et al. [17] extends the conventional PA technique from industrial sites to broader macro-scale applications into electricity generation sector to
NLP PGCC PPA SWM WAMPA WSC WtE
Non-linear Programming Power Grand Composite Curves Power Pinch Analysis solid waste management Waste Management Pinch Analysis Waste Supply Curve Waste-to-Energy
optimize the generation mix based on demand/emissions targeting. Walmsley et al. [18] combined the CEPA and Energy Return on Energy Investment (EROI) to perform macro level energy planning for New Zealand. This study presents a new application of PA for SWM planning. The proposed Waste Management Pinch Analysis (WAMPA) is analogous to the existing CEPA. Similar to the existing CEPA, users will have to identify the constraint for GHG emission and then adjust a non-carbon emitting option to meet the targeted demand while maintaining the GHG emission at the appropriate level. While CEPA is applied for energy management, WAMPA is for solid waste. In CEPA, the non-carbon emitting option is renewable energy while in WAMPA is reduce, reuse, and recycling (3R) strategy. In addition to that, WAMPA also include an additional target for landfill reduction target as well as provides a more detailed step by step analysis compared to the previous CEPA, to balance the three general strategies in waste management (Waste-to-Energy (WtE), landfilling, and 3R). WAMPA is able to reveal the capacity for landfilling, WtE, and 3R to meet a future scenario where waste generation is increasing and more stringent constraint is set on GHG emission and landfilling. In this paper, WAMPA is also demonstrated for the five major categories of municipal solid waste, food waste, paper, plastic, metal and glass. In this article, the general methodology of WAMPA will be presented through a demonstration of a SWM case followed by a detailed representation of WAMPA for five waste types. Application of the technique is then applied on a hypothetical case study for sustainable waste management planning. It is noted that the figures presented in the methodology chapter reflects the hypothetical case study. It is then followed by the result and discussion, and finally conclusion. 2. Waste Management Pinch Analysis (WAMPA) WAMPA is developed base on CEPA approach that identify the optimal strategies based on the defined target for a waste management system. WAMPA introduces a step-by-step algorithm, which involves a more systematic approach for waste management system. 2.1. Assumption of WAMPA To conduct WAMPA, several assumptions were made: 1. The supply side of waste management is depicted by the capacity of waste processing and disposal technologies, which are such as recycling, WtE, and landfilling. 2. The demand side of waste management is depicted by the amount of waste source. 3. Waste are assumed to be segregated, therefore different types of waste would be processed according to the characteristic. 4. It is assumed that no GHG is release from metal and glass waste.
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
W.S. Ho et al. / Applied Energy xxx (2016) xxx–xxx
5. The study only consider GHG emission from CO2 and CH4 sources, which is represented as total CO2 equivalent. CH4 are emitted from landfills while CO2 are emitted from WtE technologies. 6. WtE technologies are generalized to emit a similar amount of CO2 emission. 7. 3R referred to waste reduce, reuse, and recycling activities which are assumed to not release any GHG during processing. 2.2. The basic steps for WAMPA development In Pinch Analysis the Demand (source) and Supply (sink) Composite Curve is constructed and the point where the Demand and Supply Curve meets is known as Pinch Point, base on that several information can be extracted. In WAMPA, the Pinch Point is defined by the users as the GHG emission constraint target. The Demand Curve represent a straight line from the origin to the Pinch Point. Base on the Pinch Point and the Demand Curve, the Supply Curve is constructed via WAMPA such that it meets the Demand Curve at the Pinch Point. The Supply Curve is represented by the Landfill Curve, WtE Curve and the 3R Curve. Landfill Curve: GHG emission from landfill refers to emission of CH4 due to decomposition of organic waste decomposition. WtE Curve: GHG emission from WtE refers to emission of CO2 during conversion of WtE. 3R Curve: 3R is considered to not emit any GHG. WAMPA is presented on a two-dimension graph with X-axis as the cumulative of waste amount and Y-axis as the cumulative of GHG emission. Fig. 1 shows the graphical concept of WAMPA. WAMPA is developed to identify the requirement of landfill, WtE and 3R capacity from a current to a future scenario where waste quantity is expected to increase subjected to GHG emission and landfilling constraint. The methodology of WAMPA is therefore divided into the construction of two Supply Curve, one Curve for current and another for future scenario. Step-by-step explanation of WAMPA methodology is as below [19]: Step 1: Construct the existing Waste Supply Curve (WSC). Step 2: Set a target of GHG emission (Pinch Point) and waste to landfill reduction. Step 3: Construct the new WSC starting from the Pinch Point to the origin. Start by shifting the Landfill Curve from the existing
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WSC to the Pinch Point. Noted that only a portion of the Landfill Curve is shifted. The length of the Landfill Curve that can be shifted depends on the waste to landfill reduction target. Step 4: Shift the existing WtE Curve to the new WSC (beginning from the end of the Landfill Curve). Step 5: Extend the WtE Curve so that it touches the x-axis (maintaining the gradient of the line), the extended portion of the Curve represent additional WtE capacity to be implemented. If the existing WtE curve crosses the x-axis, it indicates WtE capacity have to be reduced to the extent that it touches the x-axis. WtE is given priority over 3R as the energy produce can also be applied to avoid GHG emission from fossil fuel, WtE is also more economical than 3R. However as WtE process emits CO2 and due to the GHG constraint only a limited amount of WtE can be imposed. Step 6: Maintain the existing 3R Curve (at origin). Step 7: Extend the 3R curve to the WtE curve, the extended portion of the curve represent additional 3R to be implemented. It is noted that the construction of the Demand Curve is not required in WAMPA since it is a straight line from the origin to the Pinch Point and it does not affect the decision for constructing the WSC. A general illustration of WAMPA algorithm is shown in Fig. 2. In summary, WAMPA methodology for GHG emission and landfill reduction of waste management is capable to identify the capacity of landfill, WtE, and 3R. Depending on waste type, certain Curves (Landfill, WtE, and/or 3R) may not exist during the analysis and these curves may also be portrayed differently due to differences in GHG emissions. In this paper, 5 waste types will be discussed, paper, plastic, food waste, metal and glass. Few obvious differences in depiction of WAMPA for different waste type are such as; (1) For food waste, there is no 3R curve as organic waste cannot be recycled or reuse as a new source of food; (2) For plastic waste, as plastic is not bio-degradable, Landfill Curve of plastic waste is depicted as a horizontal line similar to 3R Curve; and (3) For metal and glass waste, WtE Curve is not present, the Landfill Curve is also depicted as a horizontal line for metal and glass waste as no CO2 and CH4 is emitted. Only paper wastes WAMPA illustrates each Curves as explained in Fig. 2.
Fig. 1. Conceptual illustration of Waste Management Pinch Analysis (WAMPA).
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Fig. 2. General WAMPA algorithm.
Fig. 3. WAMPA for plastic waste.
Fig. 4. WAMPA for food waste.
For food, metal and glass waste, the algorithm is slightly different than that for paper and plastic, as food waste do not require 3R Curve to be constructed while metal and glass waste do not require WtE Curve to be constructed. The step by step methodology of
WAMPA for each food, metal and glass is shown below. The illustration of the methodology graphically is shown in Fig. 2 for paper waste, Fig. 3 for plastic waste, Fig. 4 (feasible) and Fig. 5 (infeasible) for food waste, and Fig. 6 for metal and glass.
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
W.S. Ho et al. / Applied Energy xxx (2016) xxx–xxx
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Fig. 5. WAMPA for food waste at 40% landfill reduction instead of 80% (infeasible).
Fig. 6. WAMPA for metal and glass waste.
(a) Food waste Step 1: Construct the existing Waste Supply Curve (WSC). Step 2: Set a target of GHG emission (Pinch Point) and waste to landfill reduction. Step 3: Construct the new WSC starting from the Pinch Point to the origin. Start by shifting the Landfill Curve from the existing WSC to the Pinch Point. Step 4: Shift the existing WtE Curve to the new WSC (beginning from the end of the Landfill Curve). Step 5: Extend the WtE Curve so that it touches the x-axis (maintaining the gradient of the line). If the WtE Curve touches the y-axis first (Fig. 4), it shows that the GHG emission limit for processing food waste is not yet met, indicating that further reduction of GHG is possible (further reduction is presented as the distance from the origin to the intersection of WtE Curve and y-axis). If the Curve touches the x-axis first (Fig. 5) the case is infeasible. The distance from the origin to the intersection of WtE curve and x-axis represent the amount of waste that is not processed due to landfill capacity and GHG emission constraints.
(b) Metal and glass waste For metal and glass waste, as there is no GHG emission, all Curve are represented as horizontal lines divided into two categories, 3R Curve and Landfill Curve. Step 1: Construct the existing Waste Supply Curve (WSC). Step 2: Set a target of GHG emission (Pinch Point) and waste to landfill reduction. Step 3: Construct the new WSC starting from the Pinch Point to the origin. Start by shifting the Landfill Curve from the existing WSC to the Pinch Point. Step 4: Maintain the existing 3R Curve (at origin). Step 5: Extend the 3R curve to the WtE curve. 3. Case study Sustainable waste management is identified as the synergy concept of economically affordable, socially acceptable and environmentally effective for waste management. Recovery of resources
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Table 1 Data for WAMPA analysis.
4. Results and discussion Waste generation (t)
Paper waste Plastic waste Food waste Metal waste Glass waste Landfill emission of food waste (t/t waste) [20] Landfill emission of paper waste (t CO2/t waste) [20] Emission from WtE (Paper and food waste) (t CO2/t waste) [20] Emission from WtE (Plastic) (t CO2/t waste) [21]
Year 2015
Year 2035
40,000 120,000 230,000 30,000 15,000 0.59
65,000 160,000 300,000 40,000 20,000
Landfill reduction target (%)
GHG emission reduction target (%)
40 40 80 5 5
20 40 15 – –
0.37
0.28
0.68
from waste decrease the depletion of non-renewable resource is identified as the key strategy for sustainable waste management. Sustainable planning for waste management has been widely conducted at city level. In order to demonstrate WAMPA in an actual case scenario, a hypothetical case study is applied to illustrate the analysis and optimization capability of WAMPA in sustainable waste management. With the aim to be developed into a sustainable city, the hypothetical city in year 2015 has set a target to reduce GHG emission and dependency of landfilling by year 2035. The specific target for GHG emission reduction and landfill reduction from each type of waste are presented in Table 1. The city aimed to reduce the waste disposal to landfill with 40% reduction for both paper and plastic waste, 80% for food waste and 5% for both metal and glass, at the same time reduce the GHG emission with 20% for paper waste, 40% for plastic waste, and 15% of food waste. The other data such as the coefficient of GHG emission for each waste and waste generation are also summarised in Table 1. Table 2 explained the detail data for the case study to demonstrate WAMPA. The illustration of the analysis for paper, plastic, food waste, and metal, and glass are shown in Figs. 3–6. Detailed explanation of the analysis is explained in Section 4.
The overall result of the analysis is shown in Table 3 while the overall waste management strategy in year 2015 (before conducting WAMPA) is shown Fig. 7 and the overall waste management strategy in year 2035 (after conducting WAMPA) is shown in Fig. 8. Base on the case study, the total GHG emission after reduction target for paper waste is 8160 t, plastic waste is 24,480 t and food waste is 107,400 t with consideration on further reduction (gap on the y-axis), resulting in the total GHG emission of 140,080 t a reduction of 13.5% from year 2015 (161,900 t). With references to Figs. 7 and 8, it can be seen that waste to landfill has been greatly reduced, from 249,000 t to 113,050 t (54.6% reduction), WtE increased from 150,000 t to 309,286 t (106.2% increment) and 3R increase from 36,000 t to 162,664 t (351.8%). From Fig. 7, it can be seen that the major contributor of GHG emission which is from food waste (landfill) and plastic (WtE) is reduced. Plastic (WtE) is reduced in amount while food waste (landfill) is replaced by food waste (WtE). In year 2035, food waste (WtE) is depicted as the major contributor of GHG emission, however it should be noted that the energy produced through WtE will replace fossil fuel energy and could result in further reduction of GHG emission. (a) Paper waste In this case study, it was proposed that up to 40% of paper waste to landfill should be reduced by year 2035 compared with year 2015. In addition to landfill reduction, GHG emission from paper waste management should be reduced by 20%. In order to achieve the targets, WtE strategy is increased from 10,000 t to 13,286 t, a 32.86% increase. As no more allowance for GHG emission is available, additional paper waste is sent for 3R, which shows an increase of up to 39,714 t, 29,714 t more than the amount in year 2015. (b) Plastic waste For plastic waste, up to 40% of landfill and GHG emission reduction is expected by year 2035. As plastic is non-biodegradable, no CO2 is release from the landfill and the only way to reduce GHG emission is through the reduction of plastic WtE strategy. A reduce of 24,000 t for WtE is recorded while 3R increase by 80,000 t. The large increment in 3R is due to high reduction in both GHG emission and landfilling. The outcome to reduce WtE for plastic is sensible and accordance to the fact that combustion of plastic should be minimize at it release high amount of GHG and toxic fumes [22].
Table 2 Data for the case study to demonstrate WAMPA.
Paper waste Plastic waste Food waste Metal waste Glass waste
3R (t)
3R GHG emission (t)
WtE (t)
WtE GHG emission (t)
Landfill (t)
Landfill GHG emission (t)
Total GHG emission (t)
10,000 20,000 – 5000 1000
– – – – –
10,000 60,000 30,000 – –
2800 40,800 8400 0 0
20,000 40,000 200,000 25,000 14,000
7400 0 118,000 0 0
10,200 40,800 126,400 0 0
Table 3 Results of WAMPA.
Paper waste Plastic waste Food waste Metal waste Glass waste
3R (t)
3R carbon emission (t)
WtE (t)
WtE carbon emission (t)
Landfill (t)
Landfill carbon emission (t)
Total carbon emission (t)
39,714 100,000 – 16,250 6700
– – – – –
13,286 36,000 299,429 0 0
3,720 24,480 83,840 0 0
12,000 24,000 40,000 23,750 13,300
4440 0 23,600 0 0
8160 24,480 107,400 0 0
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Fig. 7. Waste management in year 2015.
Fig. 8. Waste management in year 2035 (After conducting WAMPA).
Fig. 9. Reducing landfill target.
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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W.S. Ho et al. / Applied Energy xxx (2016) xxx–xxx
Fig. 10. Reducing carbon emission target.
(c) Food waste Up to 80% of landfill reduction is expected from food waste management by year 2035. This is accompanied by a GHG reduction target of 15%. Base on the analysis shown in Fig. 4, there is an additional reduction of 11,040 t of GHG that could be achieved. The analysis indicates that an additional WtE of 230,000 t is required. For demonstration purposes, the initial landfill reduction target for food waste is modified to 40% instead of 80%. In this scenario, the amount of WtE is decreased and due to the GHG constraint, up to 49,143 t of food waste cannot be disposed. Adjustment is therefore required to ensure that all the food waste can be sustainably disposed. Adjustment can be made by decreasing the amount of food waste to the landfill, and increasing the allowance for GHG emission. By decreasing the amount of food waste to landfill, the gap between the Landfill Curve and the x-axis will increase, this allow for more WtE strategies to be implemented at a lower GHG emission rate than landfilling. In another word, the WtE Curve can be shifted upward. As long as the WtE Curve intersect the origin or the y-axis, the proposed strategy is feasible. The same concept is applied by decreasing the GHG emission target. An example of shifting the Food Waste WtE Curve upward is shown in Fig. 9 for landfill target modification and Fig. 10 for GHG emission target modification. (d) Metal and glass As metal and glass are not possible to be converted through WtE, a balancing between landfilling and 3R is required. The result shows that with a reduction of 5% to landfill for both metal and glass in year 2035 compared to year 2015, an additional amount of 11,250 t for metal and 5700 t for glass is required. 5. Conclusion Base on the overall outcome of the study, while WAMPA is able to identify the capacity of each strategy, it does not select the type of technology and its corresponding capacity to be implemented. Selection of technologies which usually is subjected to other factors of consideration could be done with other optimization technique such as mathematical programming or with additional heuristics and guideline to WAMPA. Incorporation of cost and technologies in processing will be discussed in future works. Nevertheless, with the current state, WAMPA could simplified the search by revealing the targets for total required capacity for each waste management strategy.
Acknowledgements The authors would like to acknowledge the financial support from the Ministry of Higher Education (MOHE) granted under the research project with Vote No. R.J130000.7842.4F771 and from Universiti Teknologi Malaysia (UTM) under the research project with Vote No. Q.J130000.2501.10H28 and Q.J130000.21A2.02E02.
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Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management Wai Shin Ho a,⇑, Haslenda Hashim a, Jeng Shiun Lim a, Chew Tin Lee b, Kah Chiin Sam b, Sie Ting Tan a a b
Process System Engineering Centre (PROSPECT), Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor, Malaysia Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
h i g h l i g h t s A novel method known as Waste Management Pinch Analysis (WAMPA) is presented. WAMPA aims to identify waste management strategies based on specific target. WAMPA is capable to examine the capacity of waste management strategies through graphical representation.
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Article history: Received 15 August 2015 Received in revised form 26 December 2015 Accepted 19 January 2016 Available online xxxx Keywords: GHG emission Pinch Analysis (PA) Solid waste management (SWM) Waste-to-Energy (WtE) Reduce, reuse, and recycle (3R)
a b s t r a c t Improper waste management happened in most of the developing country where inadequate disposal of waste in landfill is commonly practiced. Apart from disposal, MSW can turn into valuable product through recycling, energy recovery, and biological recovery action as suggested in the hierarchy of waste management. This study presents a method known as Waste Management Pinch Analysis (WAMPA) to examine the implication of a dual-objective – landfill and GHG emission reduction target in sustainable waste management. WAMPA is capable to identify the capacity of each waste processing strategy through graphical representation. A general methodology of WAMPA is presented through a demonstration of a SWM case followed by a detailed representation of WAMPA for five waste types. Application of the WAMPA is then applied on a case study for sustainable waste management planning from year 2015 to 2035. Three waste management strategies are incorporated into the case study – landfill, Waste-toEnergy (WtE), and reduce, reuse, and recycle (3R). The results show a 13.5% of total GHG emission reduction and 54.6% of total reduction of landfill are achieved. The major contributor of GHG emission which are from food waste (landfill emission) and plastic (WtE emission) is reduced. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction The generation of municipal solid waste (MSW) has increased in parallel to the rapid population growth, and with changing consumption patterns, economic development and rapid urbanization. Improper waste management happened in most of the developing country where inadequate disposal of waste in landfill is commonly practiced. Improper waste management causes long-term impacts to the environmental, such as pollution of air, soil, surface and ground water, in addition, reduces valuable land space due to landfilling. One of the major consequences of landfill is the generation of methane (CH4) gas from the decomposition of MSW, where ⇑ Corresponding author. Tel.: +60 16 4401186. E-mail address: [email protected] (W.S. Ho).
CH4 contributes to about 21% of global greenhouse gasses (GHG). The negative consequences of landfill are the driving force that pushes governments and municipalities to identify better solutions for waste management planning. Apart from disposal, MSW can turn into valuable product through recycling, energy recovery, and biological recovery action as suggested in the hierarchy of waste management [1]. As the process to identify the optimal strategy for waste management can be rather complex. Regional solid waste management (SWM) strategy are often performed via optimization tool which is often optimized in a ‘‘black-box” mathematical optimization approach, emphasizes the design of a system by a specific objective function that gives the best solution to the objective function [2]. Various types of techniques have been implemented as an optimization model for SWM, such as Linear Programing, Mixed
http://dx.doi.org/10.1016/j.apenergy.2016.01.044 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Nomenclature 3R CEPA CO2 CH4 EROI GHG MILP MSW
reduce, reuse and recycling Carbon Emission Pinch Analysis carbon dioxide methane Energy Return on Energy Investment greenhouse gasses Mixed Integer Linear Programming municipal solid waste
Integer Linear Programming (MILP), Non-linear Programming (NLP), stochastic programming, fuzzy logic and hybrid model. Mathematical optimization approach required the specific mathematical modelling knowledge to develop the models, which might prevent decision and policy makers to understand fully the reason in obtaining the optimal solution. Among the optimization techniques present, Pinch Analysis (PA), which has been largely applied through many applications, is significantly important and has the advantage of allowing users to easily grasp and understand the optimization procedure as PA is often presented graphically. PA was first developed based on thermodynamic principles for the synthesis of heat exchanger networks in the 1970s [3]. The fundamental concept of PA is to maximise the process-to-process heat recovery and minimise the external utility loads. Since then, it has been applied to processing problems beyond heat and energy application. For instance, ElHalwagi and Manousiouthakis [4] adopted PA into mass exchange networks of a chemical process. Wan Alwi and Manan [5] proposed a new graphical tool known as STEP to simultaneously target and design heat exchanger network, which later on, the authors [6] extended to consider the placement of utilities with flue gas. Miah et al. [7] proposed a new practical integration framework to solve complex and diverse production line, involving analysis at zonal and factory level. Liew et al. [8] proposed a PA centric framework to perform the heat integration for a total site problem. PA was further adapted in power system planning by Bandyopadhyay [9] to design an off-grid PV/Battery system. Alwi et al. [10] continue to improve the power for hybrid renewable energy sources known as Power Pinch Analysis (PPA). Ho et al. [11] extend the PPA approach by employing new ways of utilising the Demand and Supply Composite Curve methods for the design of an off-grid hybrid energy systems. Giaouris et al. [12] continue to improve the work on PPA by introducing the Power Grand Composite Curves (PGCC) method to adaptively adjust the system operation in short-term power requirements. Other than energy and power, Manan et al. [13] proposed the used of Water Pinch to target the minimum water flow rate. Ng et al. [14] adopted Water Pinch for wastewater recycling issue. Foo et al. [15] developed algebraic and graphical targeting techniques to design the chilled water and cooling water network. Conventional Pinch Analyses are used to define the target (demand chain) of process system based on the information of stream quantities and quality (supply chain) for a micro-scale industries planning. With contrast to the conventional Pinch approaches, Tan and Foo [16] developed the Carbon Emission Pinch Analysis (CEPA) to address the GHG emission constraints issue of the energy sectors for macro scale regional planning. In CEPA, the carbon reduction target from the energy sector was set based on national or regional development plan, then emissions reduction action is decided to achieve the set target. Tan et al. [17] extends the conventional PA technique from industrial sites to broader macro-scale applications into electricity generation sector to
NLP PGCC PPA SWM WAMPA WSC WtE
Non-linear Programming Power Grand Composite Curves Power Pinch Analysis solid waste management Waste Management Pinch Analysis Waste Supply Curve Waste-to-Energy
optimize the generation mix based on demand/emissions targeting. Walmsley et al. [18] combined the CEPA and Energy Return on Energy Investment (EROI) to perform macro level energy planning for New Zealand. This study presents a new application of PA for SWM planning. The proposed Waste Management Pinch Analysis (WAMPA) is analogous to the existing CEPA. Similar to the existing CEPA, users will have to identify the constraint for GHG emission and then adjust a non-carbon emitting option to meet the targeted demand while maintaining the GHG emission at the appropriate level. While CEPA is applied for energy management, WAMPA is for solid waste. In CEPA, the non-carbon emitting option is renewable energy while in WAMPA is reduce, reuse, and recycling (3R) strategy. In addition to that, WAMPA also include an additional target for landfill reduction target as well as provides a more detailed step by step analysis compared to the previous CEPA, to balance the three general strategies in waste management (Waste-to-Energy (WtE), landfilling, and 3R). WAMPA is able to reveal the capacity for landfilling, WtE, and 3R to meet a future scenario where waste generation is increasing and more stringent constraint is set on GHG emission and landfilling. In this paper, WAMPA is also demonstrated for the five major categories of municipal solid waste, food waste, paper, plastic, metal and glass. In this article, the general methodology of WAMPA will be presented through a demonstration of a SWM case followed by a detailed representation of WAMPA for five waste types. Application of the technique is then applied on a hypothetical case study for sustainable waste management planning. It is noted that the figures presented in the methodology chapter reflects the hypothetical case study. It is then followed by the result and discussion, and finally conclusion. 2. Waste Management Pinch Analysis (WAMPA) WAMPA is developed base on CEPA approach that identify the optimal strategies based on the defined target for a waste management system. WAMPA introduces a step-by-step algorithm, which involves a more systematic approach for waste management system. 2.1. Assumption of WAMPA To conduct WAMPA, several assumptions were made: 1. The supply side of waste management is depicted by the capacity of waste processing and disposal technologies, which are such as recycling, WtE, and landfilling. 2. The demand side of waste management is depicted by the amount of waste source. 3. Waste are assumed to be segregated, therefore different types of waste would be processed according to the characteristic. 4. It is assumed that no GHG is release from metal and glass waste.
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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5. The study only consider GHG emission from CO2 and CH4 sources, which is represented as total CO2 equivalent. CH4 are emitted from landfills while CO2 are emitted from WtE technologies. 6. WtE technologies are generalized to emit a similar amount of CO2 emission. 7. 3R referred to waste reduce, reuse, and recycling activities which are assumed to not release any GHG during processing. 2.2. The basic steps for WAMPA development In Pinch Analysis the Demand (source) and Supply (sink) Composite Curve is constructed and the point where the Demand and Supply Curve meets is known as Pinch Point, base on that several information can be extracted. In WAMPA, the Pinch Point is defined by the users as the GHG emission constraint target. The Demand Curve represent a straight line from the origin to the Pinch Point. Base on the Pinch Point and the Demand Curve, the Supply Curve is constructed via WAMPA such that it meets the Demand Curve at the Pinch Point. The Supply Curve is represented by the Landfill Curve, WtE Curve and the 3R Curve. Landfill Curve: GHG emission from landfill refers to emission of CH4 due to decomposition of organic waste decomposition. WtE Curve: GHG emission from WtE refers to emission of CO2 during conversion of WtE. 3R Curve: 3R is considered to not emit any GHG. WAMPA is presented on a two-dimension graph with X-axis as the cumulative of waste amount and Y-axis as the cumulative of GHG emission. Fig. 1 shows the graphical concept of WAMPA. WAMPA is developed to identify the requirement of landfill, WtE and 3R capacity from a current to a future scenario where waste quantity is expected to increase subjected to GHG emission and landfilling constraint. The methodology of WAMPA is therefore divided into the construction of two Supply Curve, one Curve for current and another for future scenario. Step-by-step explanation of WAMPA methodology is as below [19]: Step 1: Construct the existing Waste Supply Curve (WSC). Step 2: Set a target of GHG emission (Pinch Point) and waste to landfill reduction. Step 3: Construct the new WSC starting from the Pinch Point to the origin. Start by shifting the Landfill Curve from the existing
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WSC to the Pinch Point. Noted that only a portion of the Landfill Curve is shifted. The length of the Landfill Curve that can be shifted depends on the waste to landfill reduction target. Step 4: Shift the existing WtE Curve to the new WSC (beginning from the end of the Landfill Curve). Step 5: Extend the WtE Curve so that it touches the x-axis (maintaining the gradient of the line), the extended portion of the Curve represent additional WtE capacity to be implemented. If the existing WtE curve crosses the x-axis, it indicates WtE capacity have to be reduced to the extent that it touches the x-axis. WtE is given priority over 3R as the energy produce can also be applied to avoid GHG emission from fossil fuel, WtE is also more economical than 3R. However as WtE process emits CO2 and due to the GHG constraint only a limited amount of WtE can be imposed. Step 6: Maintain the existing 3R Curve (at origin). Step 7: Extend the 3R curve to the WtE curve, the extended portion of the curve represent additional 3R to be implemented. It is noted that the construction of the Demand Curve is not required in WAMPA since it is a straight line from the origin to the Pinch Point and it does not affect the decision for constructing the WSC. A general illustration of WAMPA algorithm is shown in Fig. 2. In summary, WAMPA methodology for GHG emission and landfill reduction of waste management is capable to identify the capacity of landfill, WtE, and 3R. Depending on waste type, certain Curves (Landfill, WtE, and/or 3R) may not exist during the analysis and these curves may also be portrayed differently due to differences in GHG emissions. In this paper, 5 waste types will be discussed, paper, plastic, food waste, metal and glass. Few obvious differences in depiction of WAMPA for different waste type are such as; (1) For food waste, there is no 3R curve as organic waste cannot be recycled or reuse as a new source of food; (2) For plastic waste, as plastic is not bio-degradable, Landfill Curve of plastic waste is depicted as a horizontal line similar to 3R Curve; and (3) For metal and glass waste, WtE Curve is not present, the Landfill Curve is also depicted as a horizontal line for metal and glass waste as no CO2 and CH4 is emitted. Only paper wastes WAMPA illustrates each Curves as explained in Fig. 2.
Fig. 1. Conceptual illustration of Waste Management Pinch Analysis (WAMPA).
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Fig. 2. General WAMPA algorithm.
Fig. 3. WAMPA for plastic waste.
Fig. 4. WAMPA for food waste.
For food, metal and glass waste, the algorithm is slightly different than that for paper and plastic, as food waste do not require 3R Curve to be constructed while metal and glass waste do not require WtE Curve to be constructed. The step by step methodology of
WAMPA for each food, metal and glass is shown below. The illustration of the methodology graphically is shown in Fig. 2 for paper waste, Fig. 3 for plastic waste, Fig. 4 (feasible) and Fig. 5 (infeasible) for food waste, and Fig. 6 for metal and glass.
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Fig. 5. WAMPA for food waste at 40% landfill reduction instead of 80% (infeasible).
Fig. 6. WAMPA for metal and glass waste.
(a) Food waste Step 1: Construct the existing Waste Supply Curve (WSC). Step 2: Set a target of GHG emission (Pinch Point) and waste to landfill reduction. Step 3: Construct the new WSC starting from the Pinch Point to the origin. Start by shifting the Landfill Curve from the existing WSC to the Pinch Point. Step 4: Shift the existing WtE Curve to the new WSC (beginning from the end of the Landfill Curve). Step 5: Extend the WtE Curve so that it touches the x-axis (maintaining the gradient of the line). If the WtE Curve touches the y-axis first (Fig. 4), it shows that the GHG emission limit for processing food waste is not yet met, indicating that further reduction of GHG is possible (further reduction is presented as the distance from the origin to the intersection of WtE Curve and y-axis). If the Curve touches the x-axis first (Fig. 5) the case is infeasible. The distance from the origin to the intersection of WtE curve and x-axis represent the amount of waste that is not processed due to landfill capacity and GHG emission constraints.
(b) Metal and glass waste For metal and glass waste, as there is no GHG emission, all Curve are represented as horizontal lines divided into two categories, 3R Curve and Landfill Curve. Step 1: Construct the existing Waste Supply Curve (WSC). Step 2: Set a target of GHG emission (Pinch Point) and waste to landfill reduction. Step 3: Construct the new WSC starting from the Pinch Point to the origin. Start by shifting the Landfill Curve from the existing WSC to the Pinch Point. Step 4: Maintain the existing 3R Curve (at origin). Step 5: Extend the 3R curve to the WtE curve. 3. Case study Sustainable waste management is identified as the synergy concept of economically affordable, socially acceptable and environmentally effective for waste management. Recovery of resources
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Table 1 Data for WAMPA analysis.
4. Results and discussion Waste generation (t)
Paper waste Plastic waste Food waste Metal waste Glass waste Landfill emission of food waste (t/t waste) [20] Landfill emission of paper waste (t CO2/t waste) [20] Emission from WtE (Paper and food waste) (t CO2/t waste) [20] Emission from WtE (Plastic) (t CO2/t waste) [21]
Year 2015
Year 2035
40,000 120,000 230,000 30,000 15,000 0.59
65,000 160,000 300,000 40,000 20,000
Landfill reduction target (%)
GHG emission reduction target (%)
40 40 80 5 5
20 40 15 – –
0.37
0.28
0.68
from waste decrease the depletion of non-renewable resource is identified as the key strategy for sustainable waste management. Sustainable planning for waste management has been widely conducted at city level. In order to demonstrate WAMPA in an actual case scenario, a hypothetical case study is applied to illustrate the analysis and optimization capability of WAMPA in sustainable waste management. With the aim to be developed into a sustainable city, the hypothetical city in year 2015 has set a target to reduce GHG emission and dependency of landfilling by year 2035. The specific target for GHG emission reduction and landfill reduction from each type of waste are presented in Table 1. The city aimed to reduce the waste disposal to landfill with 40% reduction for both paper and plastic waste, 80% for food waste and 5% for both metal and glass, at the same time reduce the GHG emission with 20% for paper waste, 40% for plastic waste, and 15% of food waste. The other data such as the coefficient of GHG emission for each waste and waste generation are also summarised in Table 1. Table 2 explained the detail data for the case study to demonstrate WAMPA. The illustration of the analysis for paper, plastic, food waste, and metal, and glass are shown in Figs. 3–6. Detailed explanation of the analysis is explained in Section 4.
The overall result of the analysis is shown in Table 3 while the overall waste management strategy in year 2015 (before conducting WAMPA) is shown Fig. 7 and the overall waste management strategy in year 2035 (after conducting WAMPA) is shown in Fig. 8. Base on the case study, the total GHG emission after reduction target for paper waste is 8160 t, plastic waste is 24,480 t and food waste is 107,400 t with consideration on further reduction (gap on the y-axis), resulting in the total GHG emission of 140,080 t a reduction of 13.5% from year 2015 (161,900 t). With references to Figs. 7 and 8, it can be seen that waste to landfill has been greatly reduced, from 249,000 t to 113,050 t (54.6% reduction), WtE increased from 150,000 t to 309,286 t (106.2% increment) and 3R increase from 36,000 t to 162,664 t (351.8%). From Fig. 7, it can be seen that the major contributor of GHG emission which is from food waste (landfill) and plastic (WtE) is reduced. Plastic (WtE) is reduced in amount while food waste (landfill) is replaced by food waste (WtE). In year 2035, food waste (WtE) is depicted as the major contributor of GHG emission, however it should be noted that the energy produced through WtE will replace fossil fuel energy and could result in further reduction of GHG emission. (a) Paper waste In this case study, it was proposed that up to 40% of paper waste to landfill should be reduced by year 2035 compared with year 2015. In addition to landfill reduction, GHG emission from paper waste management should be reduced by 20%. In order to achieve the targets, WtE strategy is increased from 10,000 t to 13,286 t, a 32.86% increase. As no more allowance for GHG emission is available, additional paper waste is sent for 3R, which shows an increase of up to 39,714 t, 29,714 t more than the amount in year 2015. (b) Plastic waste For plastic waste, up to 40% of landfill and GHG emission reduction is expected by year 2035. As plastic is non-biodegradable, no CO2 is release from the landfill and the only way to reduce GHG emission is through the reduction of plastic WtE strategy. A reduce of 24,000 t for WtE is recorded while 3R increase by 80,000 t. The large increment in 3R is due to high reduction in both GHG emission and landfilling. The outcome to reduce WtE for plastic is sensible and accordance to the fact that combustion of plastic should be minimize at it release high amount of GHG and toxic fumes [22].
Table 2 Data for the case study to demonstrate WAMPA.
Paper waste Plastic waste Food waste Metal waste Glass waste
3R (t)
3R GHG emission (t)
WtE (t)
WtE GHG emission (t)
Landfill (t)
Landfill GHG emission (t)
Total GHG emission (t)
10,000 20,000 – 5000 1000
– – – – –
10,000 60,000 30,000 – –
2800 40,800 8400 0 0
20,000 40,000 200,000 25,000 14,000
7400 0 118,000 0 0
10,200 40,800 126,400 0 0
Table 3 Results of WAMPA.
Paper waste Plastic waste Food waste Metal waste Glass waste
3R (t)
3R carbon emission (t)
WtE (t)
WtE carbon emission (t)
Landfill (t)
Landfill carbon emission (t)
Total carbon emission (t)
39,714 100,000 – 16,250 6700
– – – – –
13,286 36,000 299,429 0 0
3,720 24,480 83,840 0 0
12,000 24,000 40,000 23,750 13,300
4440 0 23,600 0 0
8160 24,480 107,400 0 0
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
W.S. Ho et al. / Applied Energy xxx (2016) xxx–xxx
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Fig. 7. Waste management in year 2015.
Fig. 8. Waste management in year 2035 (After conducting WAMPA).
Fig. 9. Reducing landfill target.
Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044
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Fig. 10. Reducing carbon emission target.
(c) Food waste Up to 80% of landfill reduction is expected from food waste management by year 2035. This is accompanied by a GHG reduction target of 15%. Base on the analysis shown in Fig. 4, there is an additional reduction of 11,040 t of GHG that could be achieved. The analysis indicates that an additional WtE of 230,000 t is required. For demonstration purposes, the initial landfill reduction target for food waste is modified to 40% instead of 80%. In this scenario, the amount of WtE is decreased and due to the GHG constraint, up to 49,143 t of food waste cannot be disposed. Adjustment is therefore required to ensure that all the food waste can be sustainably disposed. Adjustment can be made by decreasing the amount of food waste to the landfill, and increasing the allowance for GHG emission. By decreasing the amount of food waste to landfill, the gap between the Landfill Curve and the x-axis will increase, this allow for more WtE strategies to be implemented at a lower GHG emission rate than landfilling. In another word, the WtE Curve can be shifted upward. As long as the WtE Curve intersect the origin or the y-axis, the proposed strategy is feasible. The same concept is applied by decreasing the GHG emission target. An example of shifting the Food Waste WtE Curve upward is shown in Fig. 9 for landfill target modification and Fig. 10 for GHG emission target modification. (d) Metal and glass As metal and glass are not possible to be converted through WtE, a balancing between landfilling and 3R is required. The result shows that with a reduction of 5% to landfill for both metal and glass in year 2035 compared to year 2015, an additional amount of 11,250 t for metal and 5700 t for glass is required. 5. Conclusion Base on the overall outcome of the study, while WAMPA is able to identify the capacity of each strategy, it does not select the type of technology and its corresponding capacity to be implemented. Selection of technologies which usually is subjected to other factors of consideration could be done with other optimization technique such as mathematical programming or with additional heuristics and guideline to WAMPA. Incorporation of cost and technologies in processing will be discussed in future works. Nevertheless, with the current state, WAMPA could simplified the search by revealing the targets for total required capacity for each waste management strategy.
Acknowledgements The authors would like to acknowledge the financial support from the Ministry of Higher Education (MOHE) granted under the research project with Vote No. R.J130000.7842.4F771 and from Universiti Teknologi Malaysia (UTM) under the research project with Vote No. Q.J130000.2501.10H28 and Q.J130000.21A2.02E02.
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Please cite this article in press as: Ho WS et al. Waste Management Pinch Analysis (WAMPA): Application of Pinch Analysis for greenhouse gas (GHG) emission reduction in municipal solid waste management. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.01.044