Environ. Sci. Technol. 2005, 39, 3732-3735
Computer Model for Municipal Solid Waste Treatment in Developing Countries AMIT JAIN,† HARSANGEET KAUR, AND SUNIL KHANNA* Department of Biotechnology and Environmental Sciences, Thapar Institute of Engineering and Technology, Patiala 147004, India
Many integrated solid waste management (ISWM) models are available but are of little use to developing countries such as India since they do not take into account typical developing countries municipal solid waste characteristics such as high organic content, poor performance of formal sector control and support, high activity of scavengers and waste pickers, etc. The goal of this study is to create a computer program to determine the least cost treatment and disposal system for a given solid waste management problem. To demonstrate its applicability, the model was applied to the Indian city Amritsar. A typical Indian city like Amritsar generates about 500 ton of MSW/d with 45% moisture content, 30% volatile matter, and calorific value of 1500 kcal/kg. The computer model was run for various technologies. Results show that for Amritsar city incineration an expenditure of U.S. dollars (USD) 6.62 is incurred, whereas landfilling, composting, and biomethanation digester give an income of USD 0.13, USD 0.20, and USD 0.23 per ton of MSW, respectively. This empirical exercise not only reveals the model’s strengths such as highlighting important interdependencies in the waste management sector but also its requirement for quality data.
Introduction In India, the per capita waste generation in urban areas ranges from 0.2 to 0.6 kg, leading to a generation of 38 million ton of municipal solid waste (MSW) per year (1). The Ministry of Urban Development (MoUD) in India estimates that the rate of collection [ton of MSW collected by municipal corporation/ton of MSW generated by city) × 100] is 75100% for urban areas, while The Energy and Research Institute (TERI) estimates the rate at 72.5% (2). Urban local bodies spend about U.S. dollar (USD) 10-30 per ton on solid waste for collection, transportation, treatment, and disposal (3) of which about 60-70% is spent on manpower for street sweeping and waste collection, 20-30% on transportation, and less than 5% on final disposal of waste. This indicates that very little attention is given to the scientific and safe disposal of waste (4). Several quantitative models have been developed to address different important aspects of MSW such as allocation of waste over disposal sites, routing of collection vehicles, waste estimation and prediction, rankings of disposal alternatives, and location of municipal solid waste manage* Corresponding author phone: +0175-2393043; fax: +01752393738; e-mail:
[email protected]. † Presently working as a software engineer at Infosys Technologies. 3732
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ment (MSWM) facilities such as transfer stations, processing plants, and disposal sites (5). But most of these models are designed using developed countries systematic MSWM. In developing countries, many explicit and implicit variables both inside and outside the system make these models less applicable. Therefore, a new computer model is proposed that takes these factors into consideration. Dalemo et al. (6) developed a computer-based model for calculation of substance flows, environmental impacts, and costs of waste management while Chang and Li (7) used a modeling-to-generate-alternatives (MGA) approach for preliminary design of MSWM systems for generating alternatives. The program was used to determine the least cost treatment and disposal systems for a given MSWM problem and to generate a set of alternatives that are widely different with respect to treatment processes that cost no more than a specified percent above the lowest cost design. Barata (8) applied an environmental input-output modeling approach in Portugal to give an analytical representation of the interdependencies between the economic activities and the quantity of waste generated, the main sources of waste generation, the significance of hazardous substances present in the waste generated, and the overall dependence on landfill consumption of individual industries. In Campania, Italy, a decisional model for integrated management of MSW was applied by Antonio et al. (9) using an optimization algorithm for the solution of the decisional model to spread the waste components among the envisaged plants with or without source-separated collection, while imposing four objectives for minimum material recovery. Themelis et al. (10) calculated energy recovery from New York City MSW and showed that of the 4.1 million t of MSW collected by the city annually, 16.6% are recycled, 12.4% are combusted in waste-to-energy (WTE) plants, and the remaining 71% are land-filled. Although many of the above models are available for use, they are too sophisticated and do not reflect the MSWM requirements of developing countries such as India. These models do not take into account MSW characteristics such as high organic content, poor performance of formal sector [municipal corporation of city] control and support, the high activity of scavengers and waste pickers, etc. A cost minimization model was developed for developing countries and was applied for the wastes generated in Bangalore city (India) (5), but it could not give concrete solutions. This was primarily due to the absence of reliable data on MSW both in terms of quality and quantity (11). The National Environmental Research Institute, Nagpur (NEERI) (12), carried out extensive characterization of solid waste for 43 cities during 19701994 and showed that the composition of the MSW is directly influenced by the food habits, culture, socioeconomic conditions, and climatic conditions. The majority of the MSW in India, according to the Central Pollution Control Board (CPCB), is composed of moisture (44%) and inert material (42%), including fine dirt, stones, and ash. Most of the recyclable material (e.g. paper, cardboard, plastics, and metal) is frequently recycled either by households or more often by rag pickers from the informal recycling sector. Unlike the formal system of waste management in industrialized counties, there is an active informal network in Indian cities. This network consists of waste pickers, itinerant waste buyers (IWB), waste dealers and wholesalers, and small recycling enterprises (13). The calorific value ranges between 1000 and 1500 kcal/kg. Taking clues from the model of Gerlagh et al. (5), a model is proposed whose prime objective is to minimize the overall system cost and to identify the low cost alternatives to manage 10.1021/es0492236 CCC: $30.25
2005 American Chemical Society Published on Web 04/15/2005
TABLE 1. Variables Used for Energy Recovery during MSW Treatment variable
TABLE 2. Detailed Physical and Chemical Analysis of MSW of Different Zones of Amritsara
comment
quantitya
dry waste (W in tons) energy recovery potential (ERP in kWh) power generation potential (PGP in kW) typical waste-to-wire conversion efficiencyb (WWE) net PGP (NPGP in kWh) output cost in USD
ERP/24 WWE × PGP NPGP × cost of power per unit
a Total MSW quantity excluding the moisture content. b Efficiency with which heat energy contained in fuel (waste) is converted into electrical energy. It is expressed as electricity produced as a percentage of the total energy content of the fuel consumed.
the generated waste effectively. The objective of our computer model is to calculate the energy recovery from MSW for the various disposal options such as biomethanation digester process, composting, incineration, and landfilling. The model concentrated on these technological options to suggest the most economically viable option. This was done by calculating the cost incurred and the amount of energy that can be recovered during MSW treatment by these disposal options. Data on the waste quality and quantity were collected from a typical Indian city (Amritsar) north of Delhi, and the model has been applied on it.
Materials and Methods Gerlagh et al. (5) applied a cost minimization model to Bangalore city that describes the activities of the waste management sector resulting from the demands in other parts of the economy for the processing of waste and for the production processes of output. These activities required the supply of production factors such as labor and capital. The cost minimization model attempts to minimize the difference between input cost and output cost:
minimum cost involved ) input cost - output cost min c )
∑p k∈K
k
r˜ k -
∑ ∑p˜
j′,h
y˜ j′,h
h∈H j∈J
where c is the overall cost; pk is the external price for input factors, intermediate inputs, and environmental resources; r˜ k is the input factors, intermediate inputs, and environmental resource vector; p˜ j′,h is the value of goods to external actors outside the waste sector, such as the value of recycled paper for printers; y˜ j′,h is the supply of waste-good pair (j′,h) by internal actors; k∈K ) {1, ..., K} is the factors and intermediate inputs (labor, capital, petrol, vehicles, and other inputs; j∈J ) {1, ..., J} is the internal actors (households, hospitals, waste pickers, composting plants, etc.); and h∈H ) {1, ..., H} is the waste-goods (biodegradable waste, disposed waste, recycled products). Tildes denote variables on an aggregate level. The model of Gerlagh et al. (5) was the starting point. We developed it further to account for (i) energy production and organic compost recovery from MSW and (ii) technology effectiveness for incineration, biomethanation digester, landfilling, and composting (see Table 1). The general equations used in our model are as follows: For example, recovery by heat recovery: If calorific value of MSW ) 1200 kcal/kg and WWE (14) ) 25%, then output cost ) 14.4 × W × cost of power per unit Recovery by gas recovery: If volatile solids (VS) ) 30%, digestion efficiency in terms of volatile solid destruction (15) ) 60%, bio-gas yield (16) ) 0.80 m3/kg of VS destroyed, calorific value of bio-gas (17) )
calorific value (kcal/kg) volatile matter (%) moisture content (%) density (kg/m3) quantity of MSW (ton/day) a
residential area
industrial area
mixed area
1898 38 54 450 329
1251 19 26 485 77
1398 27 53 425 82
Source: Municipal Corporation of Amritsar.
5000 kcal/m3, and WWE (18) ) 30%, then output cost ) 11.5 × W × cost of power per unit. Some fraction of the power generated during the treatment of the waste is consumed by the treatment plant itself, which decreases the output power sold to the market to a considerable extent. Otherwise, in general, 100 ton of MSW/d with the above-mentioned MSW characteristics can generate about 1-1.5 MW power. A rough assessment of the potential of recovery of energy from MSW through different treatment methods can be made from knowledge of its calorific value and organic fraction. In thermochemical conversion, all of the organic matter, biodegradable as well as nonbiodegradable, contribute to the energy output. In biochemical conversion, only the biodegradable fraction of the organic matter can contribute to the energy output. Our computer model for integrated MSW was developed in the computer C Language. The software developed on the basis of cost minimization model requires key inputs from the user such as moisture content, volatile contents, plastics content, and methane production per unit mass. The model requires specific input values for the decision variables, but sometimes in the absence of complete and accurate data, a default value is used. For example, MSW in India shows more or less the same characteristic (i.e., around 45% moisture content and 1000-1500 kcal/kg calorific value). These standard values are taken rather than creating artificial data. When better data are available, they can be incorporated into the model. For example, not every user can calculate methane production per unit mass of MSW. In this case, a default value is used.
Results and Discussions Amritsar with a population of around 1 million [as per 2001 census, India] and an area of 135 km2 generates 500 ton of MSW per day. It can be considered as a representative Indian city. Furthermore, extensive and accurate data were available from the Municipal Corporation of Amritsar, which divided the city into four zones. The first zone is a residential zone that includes purely residential areas without commercial or industrial activity, the second zone is an industrial area comprising small industries, the third zone was a mixed area comprising of commercial complexes, while the fourth zone is an open zone of agriculture area that does not contribute toward MSW. Amritsar generates around 500 ton of MSW per day with 45% moisture content, 30% volatile matter, and calorific value of 1500 kcal/kg (Table 2). A comparative analysis of all the technologies operating for 300 d/yr and treating 500 ton of MSW/d indicates that the Municipal Corporation of Amritsar will have to spend USD 6.6 per ton of MSW in case of incineration treatment technology while landfilling, composting, and biomethanation digester process could give an income of USD 0.13, USD 0.20, and USD 0.23 per ton of MSW, respectively (Table 3). This is mainly due to the sale of gas or organic compost formed during the treatment. Computer model was also applied to the different zones of Amritsar, which indicated that the selection of a single VOL. 39, NO. 10, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 3. Economic Viability of Treatment Technologies for Average MSW Characteristics biomethanation (A) general features capital cost (million USD) operating cost (USD per ton) (B) annual expenditure (million USD) operating cost interest on capital cost at 0.75% per yr (soft loan) cost of marketing at USD 1.1 per ton of organic compost subtotal (C) annual recovery (million USD) sale of power at USD 0.05/kWh sale of organic compost at USD 18 per ton subtotal (D) yearly profit/loss margin (million USD) (E) profit/loss margin (USD per ton of MSW)
FIGURE 1. Comparison of different treatment options for different zones of Amritsar.
TABLE 4. Energy Estimations of Timarpur Incineration Plant, Delhia quantity of MSW (ton per d) combustibles (%) moisture content (%) inert (%) net calorific value (kcal/kg) power generation estimationb (MW) net energy available estimation by application of computer model (mWh) net power available estimation by application of computer model (MW)
300 40.16 30 29.84 1462.5 3.775 85 3.54
a Source: www.mnes.nic.in/tender_notice/information.pdf. bBy M/s Volund Miljotecknik A/S, Brondby, Denmark.
facility for treatment/ disposal of waste in all the zones is not appropriate (Figure 1). Composting is best for industrial and mixed zones while the biomethanation digester process is preferable for residential zone due to the high volatile matter. Incineration would not be a preferred treatment option for any of the zones. Results for the landfilling technology for different zones of the city were not performed because the data were not available from Municipal Corporation of Amritsar. Validation of the model was accomplished by applying the model to a refuse incineration and power generation station at Timarpur, New Delhi, which was designed and built by a Danish operator M/s Volund Miljotecknik A/S, Brondby, Denmark. It was designed to incinerate 300 ton of MSW of Delhi per day (24 h) and generate 3.775 MW of electric power. The minimum waste quality (composition and calorific value) specified by the operator for the rated power output of 3.775 MW at the time of the supply of the plant was 3734
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incineration
composting
landfilling
8.2 7.3
6.67 9.6
1.1 3.8
0.16 1.24
1.330 0.065 0.020 1.415
1.45 0.05
0.57 0.008 0.04 0.62
0.189 0.001
1.150 0.300 1.450 0.035 0.23
0.5
1.5
0.5 -1.0 -6.62
0.65 0.65 0.03 0.20
0.19
0.213 0.02 0.13
calculated. The computer model was applied on the same given values, and the results are shown in Table 4. In case of the Timarpur plant, the model shows 300 ton of waste/d with net calorific value of 1462.5 kcal/kg can generate about 3.5 MW power. On the basis of preliminary calculations of the economic viability of various technological options, it appears that landfill gas technology, composting, or biomethanation digester plant technology can give a profit for MSW treatment whereas, incineration always incurs a loss. The model can be modified to local conditions, but the model accuracy is directly proportional to the set of detailed and precise data. This may be difficult in developing countries where the quality of the data varies significantly. But default value option solves the problem to a large extent. Furthermore, the software that has been used for modeling is comprehensive. To operate or modify the model, a basic level of understanding on C Language and economic principles is required.
Literature Cited (1) CPCB (Central Pollution Control Board). Manual on Municipal Solid Waste Management, 1st ed.; Prepared by The Expert Committee constituted by the Ministry of Urban Development, The Government of India: January 2000. (2) Singhal, S.; Pandey, S. Solid waste management in IndiasStatus and future directions. TERI Inf. Monit. Environ. Sci. 2001, 6 (1), 1-4. (3) Shekdar, A. V.; Bhide, A. D.; Tikekar, V. G.; Krishnaswamy, K. N. Indian urban solid waste management systems: Jaded systems in need of resource augmentation. Waste Manage. 1992, 12, 379-387. (4) Baud, I.; Schenk, H. Solid Waste Management: Modes, Assessments, Appraisals and Linkages in Bangalore; Manohar Publications: New Delhi, 1994. (5) Gerlagh, R.; Beukering, P. V.; Verma, M.; Yadav, P. P.; Pandey, P. Integrated modeling of solid waste in India. CREED Working Paper No. 26, 1999; pp 11-27. (6) Dalemo, M.; Frostell, B.; Jo¨nsson, H.; Mingarini, K.; Nybrant, T.; Sonesson, U.; Sundqvist, J. O.; Thyselius, L. ORWAREsA simulation model for organic waste handling systems. Part 1: Model description, Resour. Conserv. Recycl. 1997, 21, 17-37. (7) Chang, S. Y.; Li, Z. A computer model to generate solid waste disposal alternatives. J. Solid Waste Technol. Manage. 1997. 24 (1). (8) Barata, E. J. G. Solid waste generation and management in Portugal. Presented at the 7th Biennial Conference of the International Society for Ecological Economics, Environment and Development: Globalization & the Challenges for Local & International Governance, Sousse, Tunisia, March 6-9, 2002. (9) Antonio, G.; Fabbricino, M.; Pirozzi, F. Decisional model for integrated management of municipal solid waste. J. Solid Waste Technol. Manage. 2002, 28 (1). (10) Themelis, N. J.; Kim, Y. H.; Brady, M. H. Energy recovery from New York City municipal solid wastes. Waste Manage. Res. 2002, 20, 223-233.
(11) Hoornweg, D.; Thomas, L. What a Waste: Solid Waste Management in Asia; The World Bank Working Paper Series; May 1999; p 4. (12) Strategy paper on solid waste management in India. NEERI, 1996. (13) Furedy, C. Garbage: exploring non-conventional options in Asian cities. Environ. Urbanization 1992, 4 (2), 43-60. (14) http://www.defra.gov.uk/environment/waste/research/health/ pdf/health-report10.pdf. (15) http://www.cpcb.delhi.nic.in/slaughterhouse/slaughterhousech6.htm.
(16) http://www.cpcb.delhi.nic.in/slaughterhouse/slaughterhousech6.htm. (17) http://mnes.nic.in/u3.htm. (18) http://www.westbioenergy.org/swine.
Received for review May 26, 2004. Revised manuscript received March 14, 2005. Accepted March 15, 2005. ES0492236
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