572177

search-article2015

WMR0010.1177/0734242X15572177Waste Management & ResearchThiKimOanh et al.

Original Article

Modelling and evaluating municipal solid waste management strategies in a megacity: The case of Ho Chi Minh City

Waste Management & Research 2015, Vol. 33(4) 370­–380 © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0734242X15572177 wmr.sagepub.com

Le ThiKimOanh1, Jacqueline M Bloemhof-Ruwaard2, Joost CL van Buuren3, Jack GAJ van der Vorst2 and Wim H Rulkens3

Abstract Ho Chi Minh City is a large city that will become a mega-city in the near future. The city struggles with a rapidly increasing flow of municipal solid waste and a foreseeable scarcity of land to continue landfilling, the main treatment of municipal solid waste up to now. Therefore, additional municipal solid waste treatment technologies are needed. The objective of this article is to support decision-making towards more sustainable and cost-effective municipal solid waste strategies in developing countries, in particular Vietnam. A quantitative decision support model is developed to optimise the distribution of municipal solid waste from population areas to treatment plants, the treatment technologies and their capacities for the near future given available infrastructure and cost factors. Keywords Waste management, municipal solid waste, treatment technology, cost, modelling, decision support, Vietnam

Introduction Among the environmental issues in Ho Chi Minh City (HCMC), the largest city of Vietnam, municipal solid waste (MSW) management has the highest government priority (DONRE HCMC, 2009). The selection of MSW treatment technologies is a complex matter as many options are possible and many factors impact the choice for treatment technologies (DONRE HCMC, 2006a). It becomes even more complicated if transportation and treatment options are considered together. Moreover, multiple goals are at stake, i.e. reduction of pollution and production of valuable products from waste (DONRE HCMC, 2006a). Decision support models can help policy makers to select and design sustainable and cost-effective MSW management systems (Banias et al. 2011; Chaabane et al. 2011; Jain et al. 2005; Quariguasi et al. 2008). Many decision support models for waste management can be found in literature (Achillas et al., 2013; Chang and Wang, 1996; Karmperis et al., 2013). The review of Achillas et al. (2013) makes an inventory of decision processes in various wasterelated fields. The article focuses on multi-criteria decision-making analysis as a decision support tool. Karmperis et al. (2013) take an even wider view and review four classes of decision frameworks, which are life cycle analysis (LCA), cost benefit analysis (CBA), multi-criteria decision analysis (MCDA) and game theory. The reviews make clear that the decision support in solid waste management focuses on two areas: optimal location of treatment plants and optimal management strategy (the choice of technologies).

Eleven decision support models for solid waste management have been reviewed taking into account the following factors. •• Type of decision (location allocation models/ treatment technologies). •• Assessment criteria (cost-based models or also environmental impacts). •• Modelling approach (optimisation or generic decision support models). One of the first published decision models is Organic Waste Research - a simulation tool for waste management (Eriksson et al. 2002), developed in the 1990s in Sweden to calculate substance flows, energy flows, environmental impacts and financial costs of MSW. It includes waste collection, transportation, recycling, several treatment processes and the utilisation of heat, electricity, biogas and organic fertiliser. The model can be used 1Van

Lang University, Ho Chi Minh City, Vietnam Research and Logistics, Wageningen University, Wageningen, The Netherlands 3Environmental Technology, Wageningen University, Wageningen, The Netherlands 2Operations

Corresponding author: Le ThiKimOanh, Van Lang University, 45 Nguyen KhacNhu, District 1, Ho Chi Minh City, Vietnam. Email: [email protected]

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ThiKimOanh et al. Table 1.  Classification of the literature on decision support tools for solid waste management. Authors

Chang and Wang (1996) Barlishen and Baezt (1996) Valeo et al. (1998) Eriksson et al. (2002) Fiorucci et al. (2003) Jain et al. (2005) Ghose et al. (2006) Kirkeby et al. (2007) Abeliotis et al. (2009) Su et al. (2010) Banias et al. (2011)

Type of decision

Assessment criteria

Modelling approach

Locationallocation

Costs

Optimisation

x x x x

Treatment systems

Environmental impacts

x x x x

x x x x x

for comparison of environmental impacts and costs of waste management strategies. A general conclusion for European cities is that landfilling is the least preferable waste treatment technology. The model makes clear that environmental impacts and costs strongly depend on the context of the system under study. Chang and Wang (1996) dealt with the allocation of recycling and incineration plants in Taiwan. Their tool is a graphical, interactive, problem-structuring tool for managing MSW based on statistical analysis. Barlishen and Baezt (1996) combined knowledge-based systems with MSW management and planning models to assist with waste forecasting, technology evaluation, recycling and composting programme design, facility sizing, location and investment timing, and waste allocation. Valeo et al. (1998) developed a location-allocation model using Geographical Information Systems (GIS) software to design a recycling depot scheme for a community. Fiorucci et al. (2003) developed a technical and economic optimisation model to plan the number of landfills and treatment plants and to determine the quantities of waste fractions to be sent to treatment plants, landfills and recycling. Optimisation occurs typically with respect to costs. Jain et al. (2005) created a model to determine the leastcosts treatment and disposal system and energy production for a given solid waste management problem in India. Ghose et al. (2006) proposed a GIS routing model to optimise the routing system for collection and transport of solid waste in the Asansol Municipality Corporation (AMC) of West Bengal State (India). EASEWASTE is another well-published model for MSW management (Kirkeby et al. 2007). In this model, the focus is on environmental comparison of proposed waste management strategies and technology choices. This model does not calculate the financial costs and benefits of options. Abeliotis et al. (2009) applied ReFlows, a computer-aided decision support system for solid waste management. ReFlows evaluates the performance of existing or planned waste management systems and configurations under different strategies with respect to quantitative targets defined by solid waste management policies. Su et al. (2010) used MCDA to compare the performance of waste

x x x x

x x

x x

x x

x x x x

x

Generic decision support x x x x     x   x x  

reduction policies in Taoyuan County (Taiwan) with respect to social, economic and management criteria. Banias et al. (2011) developed a web-based decision support system to identify the optimal construction and demolition waste management strategy in Greece, minimising total costs and maximising material recovery. Table 1 summarises the findings of the literature review. In general, decision support models for solid waste management focus only on a few of the aspects mentioned in Table 1. Most decision support models are based on a complex mathematical approach with many assumptions, constraints and variables, developed for a specific geographical area. It should be noted that the more complex the model, the less marketable and applicable it is (Powell, 2000). The aim of our study is to take a flexible modelling approach, typically useful for location-allocation studies (mixed integer linear programming), to integrate the typical conditions of the area of study (suitable MSW treatment technologies, land use, marketable products, prices, legislation, etc.) in an easy way. This modelling approach builds upon an extensive study of the current MSW management system in HCMC, a study of the feasible MSW treatment technologies for cities in developing countries, and a cost analysis of each of the proposed technologies (Kim Oanh, 2012). This approach makes it possible to model and evaluate potential MSW management strategies for a range of circumstances, just by replacing objective functions and constraints in the model. The criteria used for the selection of the potential treatment technologies are subdivided into four groups: (1) technical efficiency, (2) environmental and health performance, (3) social manageability and (4) economic affordability. Technologies should be able to deal with the quality and quantity and the climatic conditions of the city under study. MSW in Vietnam has a high content of organic matter and moisture, making biological processes suitable. Temperature makes composting and anaerobic digestion at mesophilic conditions attractive (Kim Oanh et al., 2007). Technologies should comply with environmental and public health requirements, satisfying Vietnamese standards for noise,

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Waste Management & Research 33(4)

discharge of pollutants into air and surface water. Technologies require control mechanisms that should fit in the existing institutional infrastructure of HCMC. Application of high-tech options requires time and money for training and education, and entails a high risk of failure. Finally, the financial results of the technologies depend highly on the effective demand for end products (especially electricity and compost) of the MSW systems on the local markets. At the moment, the price of electricity in HCMC is low and the demand for high quality compost products is increasing. The application of these criteria resulted in a list of eight potentially feasible technologies in the area of composing, digestion, incineration and landfilling, which are listed in the first column of Table A.4 (in Appendix, available online). Residue landfills are needed to deposit the residue rejected from composting, anaerobic digestion and incineration. Therefore, the modelling includes in total nine technologies. It is obvious that each technology has advantages and disadvantages as well as requirements to be fulfilled before using them. Relatively low costs and the possibility of disposal of all kinds of waste make sanitary landfilling a widely applied treatment method in developing countries (Barrett and Lawlor, 1995: 129). However, land scarcity, increasing environmental requirements and high leachate treatment costs lead to decreasing appreciation for this method (DONRE HCMC, 2006b: 34). As a consequence, bioreactor landfilling is an attractive alternative. Bioreactor landfills are basically sanitary landfills in which leachate is recirculated, so that moisture and micro-organisms are better spread through the waste mass. Compared with sanitary landfilling, the capital costs and complexity of bioreactor landfilling are higher, but it produces more biogas leachate treatment costs are reduced (ITRC, 2006: 12) and it requires less land owing to more rapid stabilisation and settlement of biodegradable organic matter (5–10 years in a bioreactor landfill as compared with 25–50 years in a sanitary landfill). Besides landfilling, composting is the most commonly used technique to convert organic solid waste (Tchobanoglous et al., 1993: 684) as it is simple, cheap and a proven technique in developing countries like Vietnam (DONRE HCMC, 2006b: 43). Its success, however, depends to a considerable degree on the effective demand for compost (survey information at Vietstar composting plant in 2009 and 2011, Dong Xanh and Tam SinhNghia composting plant in 2011). Over 50% of the MSW of an average city in a developing country is compostable (Hoornweg, 1999: 3). With the increasing emphasis on energy production, anaerobic digestion is gaining preference over composting owing to the green energy it produces and the value of the digestate as a raw material for the production of soil conditioner (DONRE HCMC, 2006b: 52). However, this technology needs adjustments for applicability in developing countries (Kim Oanh, 2009). The aim of incineration is to reduce the volume and polluting potential of MSW and produce energy from waste (World Bank, 1999). In developing countries, incineration is hardly selected owing to its high investment and operation costs, high requirements of staff skills

and its sensitivity for high moisture content in the MSW (UNEP2 webpage, 2012). (International source book on environmentally sound technologies for MSW management, section 1.5.1. UNEP – technical publication series). An important part of the costs of incineration can be balanced with the financial benefits from the energy generated from waste. Since the electricity price in developing countries is usually still low, incineration is attractive only if land is a crucially scarce good (World Bank, 1999) and/or significant financial benefits from selling carbon credits (CERs) can be obtained (DONRE HCMC, 2009: 126). The residue waste from composting, anaerobic digestion, and the rejected waste and ash from incineration should be deposited in a separated landfill called ‘residue landfill’.

Materials and methods Before presenting the essence of the decision support model, the main input data for the model are discussed, namely the geography of the MSW management system in HCMC and the relevant parameters of the selected technologies.

Geography of the MSW management system in HCMC Given the high growth rate of population and economic conditions in HCMC, the amount of MSW increases quickly, which has a high impact on the budget and land use (DONRE HCMC, 2009). At the moment, six landfill sites exist in HCMC. Four of them are closed for further waste disposal and will not be considered in this research. Only two MSW treatment zones in HCMC can be used for future expansion of waste treatment, namely Da Phuoc in the commune Cu Chi (Zone 1) and Phuoc Hiep in the commune Binh Chanh (Zone 2) (DONRE HCMC, 2009). These zones currently are the location of sanitary landfills. The areas for future use amount to 233 ha at Da Phuoc and 276 ha at Phuoc Hiep. The government needs to select technologies that satisfy environmental and technical requirements, are cost-effective and fit the available land. Figure 1 shows the map of HCMC with the districts of HCMC and the locations of the two treatment zones. Three modes of MSW collection and transport can be distinguished. First, MSW is collected and transported to gatheringpoints with pedalled or motorised handcarts. Then, the MSW is loaded into small trucks (2–4 t) and moved to the transfer station of the district. From there, big trucks (7–12 t) transport MSW to one of the treatment zones. Second, handcarts collect and transfer MSW to gathering points and big trucks transport the MSW to the treatment zones without using the transfer stations. Third, MSW is gathered in street containers (240–660 L) along the road or collected from concentrated sources, such as supermarkets and commercial centres. The content of the containers is loaded into small trucks heading for transfer stations or into big trucks and transported to a treatment zone directly. Here gathering points are not used.

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ThiKimOanh et al.

Figure 1.  Map of the 24 districts of Ho Chi Minh City province with treatment Zones 1 and 2 (indicated with stars) (Van Buuren and Potting, 2011).

Costs and benefits of MSW treatment plants Costs are determined using data from existing treatment plants in HCMC and adapted data on European treatment plants (Kim Oanh, 2012). For the technologies available in Vietnam (composting and landfilling), current prices are used as investment costs. For the other technologies, European Union prices are raised with import costs. The costs of investments in construction and operation are taken according to the conditions of Vietnam (labour salaries, electricity prices, etc.). Fixed costs include mortgages, interest on borrowed investment capital, depreciation, repair and maintenance of assets and insurance (in million US$ y-1). Operation costs include costs of labour, materials and operation of equipment (US$ t-1). The financial benefits come from sales of energy (biogas, electricity), compost, plastics and metals. The estimates show considerable economy of scale effects for all treatment technologies in the studied capacity range of 100,000 to 1,100,000 t of MSW y-1. Free availability of land is assumed, as this land is assigned to waste treatment by

the government. If land would have a cost, this can be added to the objective function, favouring treatment systems that require less land. The costs analysis of the treatment technologies at different capacities can be found in the Appendix, available online (Table A.4).

Structure of the decision support model Figure 2 shows the overall structure of the developed decision support model. It presents the relevant model parameters (input data), performance indicators (the output of the model) and decisions taken. The values of the key parameters can be found in the Appendix, available online (Table A.1). In the mixed integer linear programming model, Xijt and Yjt are decision variables. Xijt is the amount of MSW transported from District i to the plants with treatment technology j with capacity level t. Yjt is an integer variable (0, 1, 2, 3 …) that represents the number of plants of technology j at capacity t. Assumptions for model parameters.

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Waste Management & Research 33(4) Per district • Supply waste mass flow • Typical characteristic of MSW per treatment zone • Available land area INPUT Transport between locations • Costs per km • Distance • Limited in transport routes

Supply district i (i= 1, 2, .., 24)

Model parameters Per treatment technology • Fixed costs per year • Operation costs per unit • Land use • Massbalance of each technology • Processed volume per time unit (capacity) • Type, volume, price of products

Yjt Treatment zone (1 and 2) Technology j (j= 1,2,…,18) Capacity t (t=1,2,…,5)

Xijt

DECISION VARIABLES

Decision taken • Allocation of waste flows • Per treatment zones: selection of Technologies with capacity levels

• • • • • • •

Performance indicators Technology mix Total costs and cost distribution Transport kilometers Electricity production Type and amount of products Plant utilization Land use in zones

OUTPUT

Figure 2.  Structure of the decision support model.

•• To grasp the real system in the model structure, a number of assumptions have been made. The amount of MSW to allocate is the average yearly amount of MSW for the considered period (2013–2032), not treated yet by current treatment facilities. This average amount is estimated at about 3.6m t y-1. This estimation is based on the statistic data of the population growth rate (3.5%, Statistical Office of Ho Chi Minh City, 2011), the MSW growth rate over the previous 10 years (6%–8%/year) and current waste reduction programmes in HCMC (DONRE HCMC, 2009). The amount of MSW in each of the 24 districts of HCMC can be found in the Appendix, available online (Table A.2). •• HCMC is divided into 24 districts that transport MSW to the two treatment zones. The current transport routes are the shortest and acceptable regarding the capacity of roads (Appendix, available online (Table A.3)). •• The government is willing to invest in new waste treatment technologies, like batch and continuous anaerobic digestion, incineration with energy recovery, bioreactor landfilling and residue landfilling. Aerated static pile and in-vessel composting and sanitary landfills do already exist in Vietnam. •• Land for treatment facilities in HCMC is free of charge based on an existing regulation of the government. Regarding the use of land, two groups of technologies are distinguished. First, the process-oriented technologies comprising composting, anaerobic digestion and incineration. These technologies produce end-products that leave the site of the facilities (flow-through principle) and residues that have to be

disposed of at a residue landfill. The second group includes the bioreactor landfilling and sanitary landfilling. These are accumulation systems in which the deposited wastes gradually fill the site. Also residue landfilling belongs to this group. •• Processing leads to valuable products: the polyethylene (PE) plastics or other recyclable wastes are processed to endproducts only when the technology includes a separation system. Among the eight technologies assessed, the technologies with a separation system are aerated static pile and in-vessel composting, and batch and continuous anaerobic digestion. The separation system applied for these four technologies is the same; therefore the benefits from PE plastic and other recyclable wastes are assumed to be the same for these four technologies. Ash is a by-product of incineration, while aluminium and iron are recovered from ash.

Model formulation The model uses the following notation. •• Index sets: i∈I= {1,2,., 24}= set of 24 districts; j∈J1= {1,2,…, 9} = set of nine treatment technologies to be applied in treatment Zone 1; j∈J2= {10,…,18} = set of nine treatment technologies to be applied in treatment Zone 2; t∈T= {1, 2, 3, 4, 5}= capacity levels of the treatment plants.

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ThiKimOanh et al. •• Parameters: tij : unit transport costs from district i to plants with treatment J 2 (US$ t-1); technology j j ∈ J1 ri : amount of MSW at district i (t y-1); fjt : fixed costs of plants with treatment technology j j ∈ J1 J 2 with capacity level t (US$ y-1); bjt : operation costs per unit in plants with treatment technology j j ∈ J1 J 2 with capacity level t (US$ t-1); njt : negative costs (benefits from products) of plants with treatment technology j j ∈ J1 J 2 with capacity level t (US$ t-1); ljt : land use of treatment plant j j ∈ J1 J 2 at capacity level t (ha); vj,t : capacity of plants with treatment technology j and capacity level t (t); z1, z2 : total area of treatment Zones 1 and 2 (ha); k1 : fraction from composting and anaerobic digestion plants to residue landfilling (0.25 of MSW input to the plants); k2 : fraction from incinerators to residue landfilling (0.1 of MSW input to the plants).

(

(

∪ )

∪ )

(

∪ )

(

∪ )

(

)



•• Decision variables:

Minimize

∑ ∑ ∑ ∑

∑ ∑ t X +∑ ∑ ∑ t X ∑ f Y +∑ ∑ ∑ b X + ∑ f Y +∑ ∑ ∑ b X − ∑ ∑ X n −∑ k X +  ∑ ∑  ∑ b ∑ ∑ X n +  k  X ∑ ∑ ∑   k X +  ∑ ∑  ∑ + b k  X ∑ ∑  ∑  i∈I

j∈J 1

t∈T

ij

ijt

i∈I

j∈J 1

t∈T

jt jt

i∈I

j∈J 1

j∈J 2

t∈T

jt jt

i∈I

j∈J 2

i∈I

j∈J 1

t∈T

ijt

jt

j∈J 2

t∈T

t∈T

t∈T

jt

ij

ijt

j∈J 2

t∈T

The solution space is defined by a set of constraints, defining feasible solutions. The constraints (1) show that all MSW of each district has to be transported to one of the treatment plants:

ijt

t∈T

ijt

i∈I

i∈I

jt

j =1

9t

6

2

i∈I

j =5

t∈T

ijt

i∈I

j =9

t∈T

ijt

18t

14

2

i∈I

j =13

t∈T

ijt

t∈T

X ijt +



j∈J 2



t∈T

X ijt = ri∀i ∈ I (1)

Constraints (2) state that the amount of MSW that is transported to each MSW treatment plant must be equal or smaller than the total capacity of the number of open plants with capacity t:

∑X ⩽ v Y , ∀j ∈ J ∪J , ∀t ∈ T (2)



ijt

1

jt jt

2

i∈I

Constraints (3) and (4) require the land use for the treatment plants and landfill of MSW and land use for the residue landfills in each treatment zone to be smaller than the available area of the treatment zones:



j = j1







j= j 2



12

1



j∈J 1

t∈T

t∈T

l jt Y jt ⩽ z1 (3) l jt Y jt ⩽ z2 (4)

Constraints (5) explain that the amount of residue from composting, anaerobic digestion and incineration technologies to the residue landfill in treatment Zone 1 should be smaller than the capacity of that residue landfill. Here the amount of residue from composting and anaerobic digestion plants is equal to 25% (k1 = 0.25) of input MSW to these plants and the amount of residue from incineration is equal to 10% (k2 = 0.1) of input MSW. Similar to constraints (5), constraints (6) are formulated for treatment Zone 2:

ijt

jt



+

4

1

ijt

Constraints



Xijt : amount MSW from district i to treatment plant j at capacity level t (t y-1); Yjt : integer variable (0, 1, 2, 3 …): the number of treatment plants with treatment technology j at capacity level t. Yjt represents the possibility to use a certain technology at a certain capacity. Yjt can be 0 (it means that plant cannot be used with capacity t) or = 1,2,3,4… (the number of plants with treatment technology j at capacity level t). The objective of the model is to find a solution with total minimum transportation costs, fixed yearly costs, operation costs, negative costs (benefit from products) and extra operational costs of residues from composting, anaerobic digestion and incineration technologies. It can be expressed by the following equation:

{

Note that it is relatively easy to replace this objective function with maximising electricity production (which is discussed in Kim Oanh, 2012), or minimising emissions, etc. Cost minimisation is chosen, since the aim is to find cost-efficient waste management strategies (this objective is also the most common in the reviewed literature). The environmental issues are dealt with in an indirect way by selecting technologies that all satisfy the environmental standards of HCMC (Kim Oanh, 2012: Ch 4). Global warming, resource consumption and land use are addressed in the model in its focus on utilisation of electricity, heat and biogas from waste and the constraint on land use. Global warming is, for example, combated by avoiding methane emissions from landfills (anaerobic digestion (AD) and incineration), and by avoiding conventional fossil fuels, replacing them by energy from waste.

}

∑ ∑ ∑ ∑ X ⩽∑ v k1



4

i∈I

t∈T

j =1

ijt

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t∈T

t∈T

9t

X ijt + k2 Y9t

∑ ∑ i∈I

6 j =5

(5)

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Waste Management & Research 33(4) Sustainability performance criteria

Scenario 1: No limited land in Treatment zone 1 & 2

Technologies

Scenario 2: Treatment zone 1= 276ha Treatment zone 2= 233 ha

Opmizaon model

Minimized total net cost objecve

Locaon/ allocaon Selected technologies Cost analysis Products quanty

Network definion: 2 zones sources,

Scenario 3: Treatment zone 1= 140 ha Treatment zone 2= 120 ha

Figure 3.  Solution approach for three scenarios.



∑ ∑ ∑ ∑ X ⩽∑ v k1

12

i∈I

t∈T

j =9

ijt

t∈T

t∈T

18t

X ijt + k2

∑ ∑ i∈I

14 j =13

(6)

Y18t

Constraint (7) shows that Yjt number of treatment plants with treatment technology j at capacity level t. Therefore, Yjt is nonnegative and integer:

Y jt = 0, 1, 2, .. for all j , t (7)

The residue landfill is designed to dump the residue only, not commingled MSW. Therefore, constraints (8) and (9) show that commingled MSW must not be dumped in residue landfills:

∑ ∑

t∈T



∑ ∑

t∈T

i∈I

i∈I

X i 9t = 0 (8) X i18t = 0 (9)

Constraints (10) state that the amount of MSW transported to the treatment plants is of course non-negative.

X ijt ⩾ 0 for all i, j , t (10)

Scenarios Based upon the data and insights gathered in earlier research (Kim Oanh, 2009), land is the most pressing limiting factor in HCMC. Therefore, solutions to the model are calculated for three scenarios (Figure 3). 1. No limitation for the availability of land for waste treatment. 2. The land use is according to the presently still available areas for MSW treatment at Phuoc Hiep (Zone 1) and Da Phuoc communes (Zone 2), which are 267 and 233 ha, respectively. 3. Only about half of the land of Scenario 2 will be available, i.e. 140 and 120 ha for treatment Zone 1 and 2. Here the assumption is that the government is not successful in the clearance of area (remove households inside the planned area), which occurs rather frequently in Vietnam.

For Scenario 1, constraints (3) and (4) are redundant: there is no limited land use. For Scenario 2, Zone 1 is 276 ha and Zone 2 is 233 ha; for Scenario 3, Zone 1 is 140 ha and Zone 2 is 120 ha (about half of Scenario 2). The selection of potential treatment technologies based on sustainability performance criteria (environment, health and social conditions) and the technology requirements lead to nine treatment technologies that can be chosen in the optimisation model. Final input is the geography of HCMC, which are two treatment zones and 24 districts that deliver MSW. Cost minimisation is chosen as the first driver to find MSW management strategies. Alternative drivers can be, for example, maximisation of green energy production. The outcomes for each scenario are the allocation of MSW of all districts to the treatment zones, the treatment technologies chosen (with capacity) and the amount of products from the treatment technologies.

Results and discussion For the three options of land use, the outcomes of the optimisation model, driven by cost minimisation, are given in Table 2 and Figure 4. Scenario 1 has no land use restrictions. The model selects bioreactor landfilling as the predominant treatment technology as it is the cheapest in terms of net costs. For Scenario 2 (current available land use), the model results in partial replacement of the bioreactor landfills from Scenario 1 by batch anaerobic digestion, which requires less land. For the third scenario (half of the available land can be used), the selected technologies now partially change to continuous anaerobic digestion and incineration with energy recovery, options with relatively low land use per tonne of waste treated. Treatment Zone 2 is closer to the waste collection districts than treatment Zone 1, therefore, most waste is transported to treatment Zone 2. As a consequence, technologies with low use of land are preferable in treatment Zone 2. The results show that, with decreasing availability of land, the preferred technology changes from bioreactor landfilling (Scenario 1) to batch anaerobic digestion (Scenario 2) and then to incineration with energy recovery (Scenario 3).

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ThiKimOanh et al. Table 2.  Selected MSW treatment plants and capacities in the two treatment zones (between brackets the percentages of MSW treated by the technology). Scenario

Treatment Zone 1

Treatment Zone 2

1

- Bioreactor landfilling (31%): 1,100,000 t y-1 × 1 plant

- Batch anaerobic digestion (8%): 300,000 t y-1 × 1 plant - Bioreactor landfilling (61%): 1,100,000 t y-1 × 2 plants - Residue landfilling: 100,000 t y-1 × 1 plant - Batch anaerobic digestion (56%): 500,000 t y-1 × 4 plants - Residue landfilling: 500,000 t y-1 × 1 plant   - Continuous anaerobic digestion (14%): 500,000 t y-1 × 1 plant - Incineration with energy recovery (50%): 600,000 t y-1 × 3 plants - Residue landfilling: 300,000 t y-1 × 1 plant

    2



- Batch anaerobic digestion (14%): 500,000 t y-1 × 1 plant - Bioreactor landfilling (30%): 1,100,000 t y-1 × 1 plant - Residue landfilling: 100,000 t y-1 × 2 plants - Batch anaerobic digestion (33%): 500,000 t y-1 × 2 plants 200,000 t y-1 × 1 plant - Sanitary landfilling (3%): 100,000 t y-1 × 1 plant



- Residue landfilling: 300,000 t y-1 × 1 plant

    3

3

100

Sanitary landfill

Selected technologies (%)

90

30

80

50

70 60 50

92

40

14

70

30

0

Connuous anaerobic digeson Bioreactor landfill

20 10

Incineraon with energy recovery

33 8

Scenario 1

Scenario 2

Scenario 3

Batch anaerobic digeson

Figure 4.  Selected technologies for the three scenarios of land use availability. Table 3.  Net costs of the optimal solutions for the three scenarios.

Total net costs per year (million US$ y-1) Net costs per ton treated MSW (US$ t-1) Net costs per person per year (US$ person-1 per year)

Cost analysis Table 3 presents the net costs and Figure 5 shows the cost analysis of the outcomes of the three scenarios of MSW management in HCMC. As expected, Scenario 1 results in the lowest costs and Scenario 3 in the highest costs. However, the results of the model show that the availability of land strongly affects the costs of MSW management. The net treatment costs of Scenario 2 are not much higher than the net treatment costs of Scenario 1 (about 5%, 13.9 vs. 13.3 US$ t-1). It means that the land available at the two treatment zones in Scenario 2 is only slightly lower than the maximum area required for the least expensive treatment strategy until the planning horizon. The limited land availability in

Scenario 1

Scenario 2

Scenario 3

47.4 13.3  5.0

49.5 13.9  5.2

61.5 17.2  6.5

Scenario 3 results in a significant 24% increase of the total costs in comparison to Scenario 1 (17.2 vs 13.3 US$ t-1). The valuable products from MSW management are compost, biogas and electricity, heat energy and recycled wastes. Electricity is produced out of biogas from anaerobic digestion and by incineration. The amounts of products per year for the three scenarios are summarised in Table 4. The amount for each product is the sum for all treatment plants in both treatment zones at the average yearly amount of treated MSW of 3.6m t. Based on these results, decision makers can make a planning to market these products. Depending on the applied technologies, the products are different among the three scenarios. Depending on the market, the products can be utilised and compensate for the costs of MSW management.

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14.8

Costs (USD/ton)

30

6.2

10.2

18.1

15

7.2

7.6

-18.3

-19

Scenario 1

Scenario 2

20 10 0 -10

Negave costs

22.5

Operaon costs 7.3

Fixed costs Transport

-27.4

-20 -30

Scenario 3

Figure 5.  Cost analysis of the optimal solutions for the three scenarios of land use availability. Table 4.  Calculated yearly amounts of products. Products Compost (t) Biogas (m3) Electricity (kWh) Heat (kWh) PE (t) Recyclable material (t) Aluminium (t) Iron (t)

Scenario 1 103

60* 348* 106 0 0 9.6* 103 5.4* 103 0 0

Scenario 2 103

500* 210* 106 0 0 80* 103 45* 103 0 0

Scenario 3 340* 103 75* 106 581* 106 (1163* 106) 54* 103 31* 103 1.6* 103 28* 103

Heat (between brackets) from incineration is used in the pretreatment (drying) process of the incineration technology and therefore not for sale. PE: polyethylene. *multiply.

The product mix found under Scenario 1 consists mostly of biogas from bioreactor landfills, while the optimal solution of Scenario 2 yields biogas, compost and also recyclable products (from anaerobic digestion). The optimal solution of Scenario 3 produces all types of products, especially electricity and heat from incineration. Biogas can be converted to electricity and heat with a conversion rate of 1 m3 biogas equalling 1.9 kWh electricity and 3.8 kWh heat. Heat energy in Vietnam is used for industry, not for residential consumption. Therefore, an integrated planning between solid waste management and industrial management could be needed to support the heat market. The optimal solution of Scenario 2 produces the highest amount of PE recyclables, 80,000 t y-1. Based on data of Vietstar Company (2011), the amount of plastic product from the PE recycling process is 40% of total PE recyclables input. Therefore, the amount of plastic product is about 32,000 t y-1. The optimal solution of Scenario 3 yields more products than those of Scenarios 1 and 2. Therefore, the expected financial benefits of products (negative costs) are considerably bigger (Figure 5). These benefits are, however, more sensitive to fluctuations in market prices of products. Sensitivity analysis.  The outcomes of the model depend on the possibility to market the products. To investigate the impact of

the market demand of compost, the amount of compost product is limited with an upper (300,000  t  y-1) and a lower bound -1 (200,000 t y ), adding the following constraints to the model:



∑ ∑ ∑ n∑ ∑ ∑ n

2

i∈I

t∈T

10

i∈I

j =9

∑ ∑ ∑ n∑ ∑ ∑ n



j =1

t∈T

2

i∈I

j =1

t∈T

10

i∈I

j =9

t∈T

X ijt +

(11)

X ijt ⩾ p

X ijt + X ijt ⩽ q

(12)

The results for Scenario 1 and 2 are 62% bioreactor landfill and 38% of batch anaerobic digestion, which is between the optimal solutions of the base case Scenarios 1 and 2. For Scenario 3, the optimal solution contains 34% batch anaerobic digestion and 66% incineration. This shows to what degree the limitation in the compost market leads to the selection of MSW treatment technologies that produce less compost, such as bioreactor landfilling and incineration. Further, the benefit from heat energy and selling CERs under the CDM (Clean Development Mechanism) programme has been taken into account in the objective function. This results in a 100% bioreactor landfill in Scenario 1, dropping to 61% in Scenario 2, with 6% batch anaerobic digestion and 33% incineration to 100% incineration in Scenario 3. Under these constraints the model has selected a bigger share of incineration, since the heat and energy this technology produces from wastes may avoid the use of fossil fuels. The outcomes show the sensitiveness to the market yields of electricity, heat and the possibilities to sell carbon credits. The sensitivity analysis on the price and amounts of products (compost and green energy) results in the following considerations. •• Less compost produced from 1 t of wet commingled MSW (from 20% to 15%) leads only to minor changes in the solution. •• An increase in the price for electricity equal to that of Thailand and Malaysia (from 8 to 12 cent US$ kWH-1, results in a

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ThiKimOanh et al. significant change in treatment choice, that is 100% incineration and a reduction of total costs of 66%. •• An increase of price of PE plastic (related to increased price of diesel oil) from 450 to 600 US$ t-1 results in almost 100% batch anaerobic digestion.

Conclusion This article focused on designing a decision support model that minimises total net costs of transportation and treatment of MSW in a large city. The results show that the model is able to assess different scenarios for a constant annual amount of MSW. The model gives information on: (1) the distribution of MSW from the population areas to the two treatment zones and to each treatment plant; (2) selection of the best technologies and their capacities for both treatment zones; (3) costs analysis regarding transport costs, fixed costs, operation costs, benefits and residue deposition costs. The model is experienced by decision makers as flexible and can easily be adapted by adding more constraints. In order to minimise transportation costs, the model selects the allocation to treatment zones, under the constraints of land availability, the application of certain technologies, the product market, etc. The treatment zone closer to the city is proposed to locate the treatment technologies that need less land, such as incineration. The treatment zone further away locates technologies with low costs and a potentially high land requirement, such as the bioreactor landfilling. From the perspective of low treatment costs, the bioreactor landfilling ranks first, batch anaerobic digestion second and incineration without energy recovery last, while other technologies rank in between, given the set of constraints in the model. Based on low land use, incineration is the first priority, continuous anaerobic digestion is second and the last one is sanitary landfilling. As a consequence, to minimise the costs at a fixed availability of land, the model proposed a mix of technologies commonly consisting of bioreactor landfilling, batch anaerobic digestion and incineration with energy recovery. The results of Scenario 2 show that, in the case of HCMC, batch anaerobic digestion (70% of total amount of MSW) and bioreactor landfilling (30%) should be applied to treat MSW in the coming 20 years. Incineration would be the preferred option if the government is not capable of making available the land needed for the cheaper but more land-intensive technologies. Further research will be focused on developing models dealing with a rolling horizon, to simulate a long-term period of investments where early investments do impact later investment decisions.

Declaration of conflicting interests The authors declare that there is no conflict of interest.

Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Modelling and evaluating municipal solid waste management strategies in a mega-city: the case of Ho Chi Minh City.

Ho Chi Minh City is a large city that will become a mega-city in the near future. The city struggles with a rapidly increasing flow of municipal solid...
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