Process-based analysis of waste management systems: A case study

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Waste Management 29 (2009) 2–11 www.elsevier.com/locate/wasman

Process-based analysis of waste management systems: A case study J. Villeneuve a, P. Michel a, D. Fournet b, C. Lafon b, Y. Me´nard a, P. Wavrer a, D. Guyonnet a,* a

BRGM, BP 6009, 3 Avenue C. Guillemin, 45060 Orle´ans Cedex, France b SYCTOM, 35 Bd Se´bastopol, 75001 Paris, France Accepted 14 December 2007 Available online 7 March 2008

Abstract This paper presents an analysis, using process simulation, of the waste management system applied in a collection basin located in the south of Paris (France). The study was conducted in close cooperation with the ‘‘SYCTOM of Paris agglomeration”, an operator in charge of managing 2.5 million tons/yr of municipal solid waste in the Paris area. The analysis includes a description of the current situation of waste management in this collection basin, the construction and calibration of a simulator that reproduces this situation, the simulation of scenarios that account for possible future changes in waste flows and treatment options and finally a comparison of scenario results. Results illustrate the interest of a process-based approach to waste management systems. Such an approach is complementary to life cycle analyses, which usually rely on more generic descriptions of waste treatment units. The detailed analysis of a waste management system using local data on waste streams and treatment units provides technical indicators of system efficiency expressed in terms of recycling rates, energy recovery, emission fluxes and costs. Such information can help reach a consensus with respect to the actual situation of waste management and provides decision-makers with quantitative arguments that can be brought into the public debate. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction As for many large European cities, the urban area of Paris is faced with the problem of an ever-increasing and not yet stabilizing production of household waste (the increase between 1995 and 2007 was on the order of 20%). The SYCTOM (inter-communal organisation for the treatment of municipal solid waste) of the Paris agglomeration is in charge of treating waste while complying with target objectives set by European policies in terms of recycling of waste (EN, 2006; OJC, 2004), treatment emission limits (OJC, 2000), energy recovery from waste (CEC, 2003) or limitation of the landfilling of waste (in particular organic matter; OJC, 1999). The SYCTOM is therefore particularly interested in identifying environmen-

*

Corresponding author. Tel.: +33 2 38 64 38 17; fax: +33 2 38 64 30 62. E-mail address: [email protected] (D. Guyonnet).

0956-053X/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2007.12.008

tally sound and economically advantageous ways of managing its waste while complying with European objectives. Methodologies for analyzing waste management systems or strategies include material flow analysis (MFA) (Brunner and Rechberger, 2003), cost-benefit analysis (CBA) (Farrow and Toman, 1999) and life cycle analysis (LCA) (Hunt, 1995). The LCA approach is recommended in several recent pieces of European legislation on waste (and in the future Waste Framework Directive) and several software tools have been developed as for example the WISARD software (see Clift et al., 2000), the SIMA PRO tool developed by Pre consultants and more recently the EASEWASTE tool (Kirkeby et al., 2006a,b) which represent an effort to design a flexible tool for comparing different waste management strategies. Life cycle analyses are generally based on an inventory of all flows of resources, energy, and emissions that compose each element of individual operations encountered in the system, including not only the operation of waste management processes

J. Villeneuve et al. / Waste Management 29 (2009) 2–11

but also for example the contributions of the primary processes related to the production of cement or metals present in the buildings or vehicles used for waste treatment. Methods based on LCA have sometimes been criticized for not being sufficiently transparent, as they may rely on default parameters that are not readily accessible to the user, or for being sometimes so wide in scope that they may be difficult to apply to specific waste management systems. Winkler (2004) compared six different LCA models applied to waste management and found large differences between model results, which in some cases led to contradictory conclusions regarding the respective environmental performances of the waste management processes. Winkler attributed these differences to the difficulties in modelling the extreme complexity of modern waste management systems and the inadequacy of the static linear modelling approach adopted by models tested in his analysis. The approach presented in this paper (called AWAST), which is complementary to life cycle analysis, has an objective of flexibility and adaptability to complex real-world situations of waste management. The main idea behind AWAST is that in real-world situations, similar processes may not have similar performances, depending on the quality of the feed material, the characteristics of the treatment units and other specific parameters that are typically omitted in global inventory approaches. Rather than to rely on averages, for example national statistics of waste production per capita or generic characteristics of waste treatment technologies, decisions need to rely on analyses that account for the specific characteristics of a given waste management system in terms of waste streams and treatment processes. In 2003, the SYCTOM initiated a project aimed at examining various options for improving waste management in the south-eastern area of Paris (the Ivry waste collection basin). The objective of the project presented in this paper was to analyze the overall management of waste in the south-east of Paris, taking into account all waste fluxes from input (collection) to output (secondary materials, landfill, etc.). The project was divided into three phases. During the first phase, data was collected and a simulator was developed that reproduced the situation of waste management in this area at the start of the project (reference year: 2003). This simulator provided the main indicators regarding material balances, energy consumption and production, environmental emissions and costs. Once this calibration was completed, the SYCTOM defined, during the second phase, waste management scenarios that combined four different strategic options with respect to waste collection in the Ivry basin. Each scenario was simulated up to the year 2015 and performance indicators were calculated. During the third phase, the results from the different scenarios were compared using a multiple-criteria approach, to help the SYCTOM make choices with respect to waste management options. This paper presents an outline of the methodology that was used, discusses the main results and suggests possible directions for future research.

3

2. Methodology 2.1. Overview of the simulator The AWAST simulator is an application, dedicated to waste management, of the process simulator USIM PAC (Brochot et al., 2002). The AWAST simulator was developed within a project of the 5th FRDP (Framework Research and Development Programme) in collaboration with eleven European partners. Its objective is to provide stakeholders involved in MSW management with a simulation software tool based on a description of processes involved and that accounts for environmental emissions, energy fluxes, economic aspects and that is amenable to the complexities of real-world waste management situations. The simulator offers several significant advantages. First of all, the flowsheet that is developed for a specific situation (see below) provides a global vision of complex waste management systems. Such a vision is very valuable for decision-makers and also for communication purposes. Also, the tool allows the simulation of the influence of waste composition and/or treatment process characteristics on the global performance of a waste management system. It allows the efficient simulation of scenarios, in order to examine the influence of changes brought to the system. Finally, it may be used to help determine whether a given technology can guarantee that objectives with respect to quality or quantities of certain products will be met. The simulator combines the following components (Villeneuve et al., 2005): – A flowsheet that visually describes the system in terms of material streams and treatment operations (Fig. 1). Materials are linked to treatment units by arrows that establish a physical connection that is automatically taken into account by the simulator. A modification of waste streams can easily be implemented by simply changing the links directly within the flowsheet. – A phase model that describes the characteristics of all the materials involved in the flowsheet (raw waste, products, reagents, water, etc.). Characteristics include composition (wood, paper, etc.), water content, heating capacity, chemical elements (C, N, S, Cl, F, P, etc.), and metal content (Fe, Al, Pb, Zn, Cd, Hg, etc.). Informing the phase model for each flow may require a large effort of data collection, but default information that is readily accessible to the user may also be used. – Mathematical models for each unit operation (collection, transport, biotreatment, thermal treatment, landfill) that summarize current knowledge with respect to efficiencies, environmental emissions, energy fluxes, etc. Again, specific information can be entered but default values may also be used. Each operation unit, depicted by an icon (see examples in Fig. 1), is connected to algorithms that model the unit’s effect on the waste stream and on emissions.

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J. Villeneuve et al. / Waste Management 29 (2009) 2–11

Fig. 1. Example flowsheet for a simple waste management system.

– A set of algorithms for data reconciliation (error minimization in mass balances), model calibration, environmental emissions and cost calculations, etc. Data reconciliation is a particularly important step in the analysis in this context because, due to incomplete data, measurement errors, sampling errors, etc., material balances between inputs and outputs are rarely equal to zero. The AWAST simulator applies specific algorithms to calculate mass balance estimators that are statistically consistent with the input data (Durance et al., 2004). 2.2. Brief summary of process models The waste process models included in AWAST are summarized in Table 1, along with the main features addressed by these models. Certain models may be very simple and addressed using default parameters, while others are much

more detailed. For example a default land use for waste sorting plants is taken as 2 m2/ton for small plants (<6000 tons/yr) and 1 m2/ton otherwise (see guidance documents on the website of the French Environmental Agency; ADEME). Net production costs for a sorting plant are calculated taking into account installed equipment costs, other direct costs (buildings, roads, etc.), indirect costs (studies, supervision of works), operating costs (maintenance, insurance, etc.), labour costs and revenues (see Michel et al., 2007). At the other end of the spectrum, the incineration process model is made up of several unit operations presented in Fig. 2 (boiler, furnace, stack, etc.). Specific algorithms serve to calculate flows from each unit operation. For the energy balance, the model can take into account energy recovered in one of three ways. Energy can be recovered from the furnace only, with mass being transferred during combustion from the waste feed to the bottom ash and the

Table 1 Process models included in AWAST and features for each process Process models

Energy + fuel consumption

Collection Transport Transfer station Sorting plant Incineration Composting Landfill

X X X X X X X

Energy production

X X

Emissions to water

Emissions to air

X X X

X X X X X X X

Land use

Costs

X X X X X

X X X X X X X

J. Villeneuve et al. / Waste Management 29 (2009) 2–11

5

Reagents Water

Flue gas to environment

Flue gas Steam water

Spray tower

ESP

Stack

Waste 1 Waste 2

Water to environment

Waste 3 Bulk Fe

Combustion air

Furnace Turbine-alternator group

Steam

Fly ash Steam to environment

Bottom ash

Reagent addition

Reagent addition

Scrap to environment

Bottom ash cooler

Neutralization Decantation

Screen

Mill Overband

Filter press

pH regulation Cake

Bottom ash stock Bottom ash to environment

Water stock

Cake to environment Fly ash to environment

Fig. 2. Flowsheet of the incineration process model depicted in Fig. 1.

flue gas (including fly ash). Combustion energy can also be converted to steam energy through a heat exchanger/boiler device. Finally, energy can be generated by the furnace, a heat exchanger/boiler and a turbine/alternator group where the steam energy is converted into mechanical energy through the turbine and then into electrical energy via the alternator, while the residual steam energy can also be used in a heating circuit. Credits or avoided impacts related to avoided fuels for instance, are not calculated by the model. For landfill emissions, the EMCON MGM model reviewed by Liberti et al. (1993) is used to estimate landfill gas production. Default regulatory limits are typically assumed regarding emissions in combustion gases. Unless specified otherwise, a default landfill gas capture rate of 80% is assumed. Landfill leachate generation is calculated using a hydrologic balance approach taking into account an absorptive capacity of the waste and a delay in leachate restitution from the waste as described in Guyonnet et al. (1998). Emissions to groundwater are calculated taking into account characteristics of landfill bottom barriers. 2.3. Calculation modes Application of the simulator to a specific case generally includes four main steps: (i) data collection and simulator construction, (ii) calibration, (iii) extrapolation via

scenarios and (iv) synthesis of results. The calibration procedure consists in comparing the results of the calculations with the data that has been collected with respect to output fluxes. Certain model parameters can be automatically adjusted during this inverse calibration procedure. In some cases it is not possible to obtain a calibration because there are gaps in material stream information. In this case it is necessary to turn to technical waste management staff in order to obtain the missing information. Such calibration is particularly useful to help constrain the model and avoid unrealistic projections during the scenario analysis step. Calculations can also be performed in a direct mode. In this case, the simulator initializes flows between processes then calls upon each process model sequentially to calculate outputs from the system. It then repeats the operation iteratively until a convergence criterion is met (i.e., when the sum of the squares of differences between outputs from two successive iterations is below a specified value). From this brief description of the AWAST simulator, it should be clear that the model is not an LCA model; on the one hand the model does not include such details as avoided impacts and on the other hand it goes into considerably more detail than most LCA models with respect to treatment processes, the objective being to gain predictive capability with respect to the influence of input variations on outputs.

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3. Application to the Ivry waste collection sub-basin

Table 3 Waste treatment technologies in the Ivry sub-basin in 2003

3.1. Simulation of the 2003 situation

Treatment units in the Ivry collection basin

Waste treated (%)

Sorting of recyclable materials Incineration with energy recovery Landfill Direct recycling

3.1 74.3 4.2 18.3

Total tonnage (ton)

931,715

The SYCTOM’s waste collection basin covers an area of approximately 5910 km2 and includes the city of Paris as well as 88 municipalities located around Paris. The collection basin is subdivided into four sub-basins: Issy, Ivry, Romainville and Saint-Ouen. These sub-basins receive the waste from Paris and surrounding municipalities and are equipped with several waste treatment systems. Waste collected within a given sub-basin is generally treated by equipment that belongs to this sub-basin, but there may also be transfers between sub-basins. There are imports and exports of waste, i.e., waste treated by SYCTOM equipment that is not generated within the SYCTOM’s basin and, conversely, waste generated within the SYCTOM’s basin that is treated outside the basin. This leads to complex material streams that must nevertheless be accounted for in order to avoid introducing biases in the calculations. The number of inhabitants estimated for the Ivry subbasin in 2003 is 1249,600. Table 2 summarizes waste treated within this sub-basin in 2003 while Table 3 presents the different treatment operations that are applied. As can be seen in this table, the majority of the waste is incinerated with energy recovery. Considering the huge amounts of waste generated in the SYCTOM collection basin, incineration offers the important practical advantage of considerably reducing the volumes of waste. A total of 760,750 tons of primary waste was treated in 2003 in the Ivry basin, generating 170,967 tons of secondary waste that also must be treated. Sorting refuse is incinerated, bottom ashes are sent to a ‘‘maturation” platform prior to reuse (typically in road works) and filter cake is sent to a hazardous waste landfill. Globally, an estimated 931,715 tons of waste were treated in the Ivry basin facilities in 2003. Data relative to waste fluxes provided by the SYCTOM were based on analyses performed according to the MODECOMTM methodology of waste characterization (ADEME, 1993). This is the French standardized method that determines the composition of waste produced by Table 2 Synthesis of waste treated within the Ivry sub-basin in 2003 Treated waste

Tons

RMSW (residual municipal solid waste) SC Multi (separate collection of multi-materialsa) SC Glass (glass from separate collection) DOW (waste collected in drop-off centres) VW (vegetable waste) Other Secondary waste (sorting refuse, bottom ash, fly ash, filter cake)

677,030 29,165 25,410 23,856 4093 1194 170,967

Total

931,715

a

Paper, cardboard, composite packaging, plastic (bottle of water, etc.), metal (cans, etc.), small waste electrical and electronic equipment collected within the same bin.

communities on the basis of a primary sample of 500 kg of domestic waste that is separated into 13 categories. The 13 categories are: putrescible waste, paper, cardboard, composites (e.g., packaging), textiles, health-care textiles, plastics, unclassified combustible waste (e.g., wood), unclassified incombustible waste (e.g., tiles), glass, metals, special wastes (e.g., paint) and fine elements (<20 mm). The implementation of a MODECOMTM campaign is subdivided into five main steps: a preliminary survey to collect information useful for the organisation of the campaign, the selection of adequate containers for the sampling, the sampling of the primary sample (500 kg), the separation of this primary sample into 13 categories, and the implementation of laboratory analyses (e.g., moisture content, chemical composition, heating value, etc.). Because it is very difficult to reproduce in journal format, the flowsheet of the Ivry waste collection sub-basin is not shown here. Such a flowsheet, which was constructed in close collaboration with the technical services of the SYCTOM, summarizes all known significant fluxes to and from waste treatment units within the waste collection sub-basin. It provides a global view of the waste management system which has been found to be very valuable for decision-makers and also for communication purposes. The results calculated by the simulator were confronted with measured data and presented to the technical services of the SYCTOM during the calibration process. A model was developed for waste transport during collection in order to estimate the number of kilometres covered and to calculate fuel consumption and atmospheric emissions. A distinction was made between kilometres covered with and without a waste load. Kilometres travelled for transporting the MSW and the waste from separate collection (glass, bulky waste) were designated as ‘‘collection”, while kilometres travelled by drop-off centre vehicles, technical services, and for transporting transformed waste, were designated as ‘‘transport”. The waste transport model assumes that transport is optimized such that vehicle capacity is adapted to the collection itinerary. It accounts for kilometres covered during waste collection as well as those required to travel to and from the treatment units and the garage. Results of the calibrated AWAST simulation of the Ivry sub-basin situation in 2003, expressed in terms of percent treated waste, are summarized in Fig. 3. System performance can be evaluated using different indicators. Considering the products of sorting operations, waste sent directly

J. Villeneuve et al. / Waste Management 29 (2009) 2–11

% treated waste per treatment mode

7

Exports 72 480 t

Bulky waste 41 600 t

18%

COLLECTION

Metal Glass

Imports

Recycling 26 700 t

1 300 t 25 400 t

Garden waste Civic Amenity 390 t Municipalities 4 090 t

177 510 t

Organic recycling 960 t Newspapers+card. 20 470 t Tetrapack 40 t Al + Fe pack. 430 t Glass 45 t Plast. 1 750 t

0% 3%

Multi materials 30 690 t

Sorting 29 160 t

Refuse 6420 t Metal scrap 19 990 t Residual MSW 530 000 t

Incineration 692 310 t

74% Civ Amen Inert 14 740 t Civ Amen Others 3 280 t

Maturation.

Recycled bottom ash 142 220 t

Filter cake 16 680 t

4%

Landfill 38 930 t

Fig. 3. Summary of waste streams in the Ivry sub-basin in 2003.

to recyclers (glass from separate collection, metal scrap from drop-off centres), green waste and incineration residues that are sent back to recyclers (metal, aluminium, bottom ash that can be reused in road works), yields 212,600 tons that are recycled from the initial 760,748 tons of waste treated in the Ivry sub-basin treatment units, i.e., a recycling rate of 27.9% with respect to the waste treated in 2003. Indicators defined by the French Agency for the Environment (ADEME) are the rate of collection for recycling and the rates of recycling, recovery (energetic) and disposal. Rates of recycling/recovery are summarized in Table 4 while Table 5 summarizes rates of collection, i.e., the proportion of waste collected separately with respect to the waste pool. Following treatment, residual waste amounts to approximately 250,000 tons (33% of the initial 760,748 tons of treated waste), 85% of which is recycled, the rest being landfilled. The energy balance is largely a function of transport and incineration. The overall energy

Table 4 Ivry sub-basin waste recycling/recovery rates in 2003 Recycling/recovery rates

% treated waste

Material recycling (1) Organic recycling (2) Incineration with energy recovery (3) Bottom ash utilisation (4) (Landfill)

9.1 0.1 66.9 18.7 5.1

Overall rate of recycling (%) (1 + 2)

9.2

Overall rate of recycling/recovery (%) (1 + 2 + 3 + 4)

94.9

Table 5 Ivry sub-basin rates of collection Material

Rate of collection for recycling (%)

Glass (separate collection) Paper Cardboard Plastic Metal

34.0 12.1 6.0 2.4 5.7

Table 6 Energy consumption and production in the Ivry sub-basin in 2003

Consumption Electricity (GW h) Fuel (GW h) Production Electricity (GW h) Heat (GW h)

Collection

Transport

10.95

2.20

Sorting 1.06

Incineration 42.78

181.38 855.00

balance is presented in Table 6. Operational costs are largely covered by electricity production alone. Environmental indicators can be expressed in terms of ‘‘equivalent inhabitants”, established with reference to national statistical data on annual consumption or emissions (ADEME and Eco-Emballages, 2001). These data yield the following ratios: 166 GJ/pers. yr 1 for energy consumption, 8.7 tons equiv. CO2/pers. yr 1 for greenhouse gas emissions and 1.9 kg equiv. H+/pers. yr 1 for air acidification. The calculated Ivry waste management system’s environmental emissions are expressed in Table 7 as quantities and population equivalents.

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Table 7 Summary of environmental emissions Indicator

Quantity

Population equivalents (persons)

Travel distance Fossil fuel energy consumption for transport and collection Gas emissions Contribution to greenhouse effect Air acidification

6  106 km 47.3 GJ

286

5.28 Mt/yr 753 kt equiv. CO2

86 505

166 ton equiv. H+

87 334

3.2. Simulation of scenarios of Ivry sub-basin evolution The projected Ivry sub-basin waste pool in 2015 was estimated in collaboration with SYCTOM technical services, based on the following hypotheses: – no evolution of the basin’s geographical limits: they are assumed to be identical to those of 2003, – a population increase based on statistical data, – an evolution of waste production per inhabitant estimated as +2% (in 2015 compared to 2003) taking into account the SYCTOM’s waste prevention policies, and – an increase of sorting efficiency and therefore of the ratio between sorted waste and residual waste. Hypotheses with respect to waste production in 2015 are summarized in Table 8. Projected population is estimated to be on the order of 1301,500. In 2003, 655,662 tons of waste are generated within the Ivry basin but 177,500 tons are imported and 72,480 tons are exported. As a result, 760,748 tons of waste are treated in 2003 within the Ivry basin. In 2015, in addition to the 663,821 tons of waste generated, it is anticipated that 150,000 tons of imported waste will be treated in the Ivry basin facilities. No export of waste is foreseen. All of this waste (813,800 tons) will be treated in the Ivry facilities. Four strategic waste treatment options were defined by the SYCTOM (Table 9). These options relate to the relocation of the waste treatment units and to the treatment of imported waste. The Ivry waste imports will include Table 8 Hypotheses for waste production in 2015 2003 situation

2015 situation

Total (tons)

Total (tons)

% wrt total

% wrt total

MSW Separate collection (multi and single-materials) Bulky waste Vegetable waste Drop-off centres Glass

530,001 30,692

81.0 4.7

502,015 49,544

75.6 7.5

41,611 4093 23,855 25,410

6.3 0.6 3.6 3.8

48,209 4295 33,728 26,030

7.3 0.6 5.1 3.9

Total

655 662

100.0

663 821

100.0

40,000 tons of MSW from the municipalities of Issy and St-Ouen and 110,000 tons of RDF (Refuse-Derived Fuel) from Romainville. If these fluxes are not treated at Ivry, the 40,000 tons of MSW will be treated in an anaerobic digestion plant that will be constructed in the area and the RDF will be landfilled. Each of the four scenarios was therefore subdivided into sub-scenarios corresponding to the type of treatment technology for the Ivry sub-basin residual MSW (Table 9). Combining scenarios and sub-scenarios results in a total of 12 simulations noted S1.1 through S4.3. Scenario S2.3 was not considered because SYCTOM discarded it as irrelevant. For the comparison between scenarios, two types of indicators were used; ‘‘performance indicators”: total energy balance (in GJ), overall rate of recycling/recovery (%), overall rate of recycling (%) and ‘‘impact indicators”: greenhouse gas emissions (tons equiv. CO2), air acidification (kg equiv. H+), residual waste in hazardous waste landfills (tons), residual waste in non-hazardous waste landfills (tons), heavy metal emissions (Hg and Cd, g), dioxin and furane emissions (g), and travel distances (km). The indicator ‘‘total energy balance” accounts for energy consumption (with a negative sign) and production (positive sign) and is the sum of the following quantities: fuel consumption (converted to GJ) due to waste collection and transport, production minus consumption of electricity in waste treatment units (kWh converted to GJ) and finally production minus consumption of vapour kWh in the incineration and methanisation plants (in kWh converted to electric kWh and to GJ). This indicator does not have any intrinsic meaning since energy consumed and produced are not directly comparable; however, it provides a global indication of the amount of energy that can be transformed into mechanical energy. Comparisons between scenarios are presented in Tables 10 and 11 and graphically in Fig. 4. Heavy metal emissions are not presented in Fig. 4 as they display strictly the same trends as dioxin and furane emissions (being related to incineration). Favourable scenarios are indicated by high performance indicators (Fig. 4a–c) and by low impact indicators (Fig. 4d–i). As expected, incineration produces more energy but on the other hand increases impacts on air and quantities of waste sent to hazardous waste landfills. Methanisation improves the rate of recycling (organic matter) but generates more waste sent to non-hazardous waste landfills (pre-sorting refuse) (Fig. 4g), which in turn slightly increases road travel distance (Fig. 4i). The reduction of waste treatment capacity due to the cessation of waste imports results in the decrease of energy production, reduction of air impacts and increase of tons of waste sent to non-hazardous waste landfills (mainly due to the landfilling of imported RDF). The main influence of relocating the residual MSW treatment unit (scenarios 3 and 4) is the increase of travel distance (Fig. 4i). Greenhouse gas emissions do not seem much influenced by relocation (Fig. 4d), but are reduced when methanisation is used instead of incineration.

J. Villeneuve et al. / Waste Management 29 (2009) 2–11

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Table 9 Scenarios for the evolution of the Ivry sub-basin waste management system Scenario

Localisation of residual MSW treatment unit

Waste treatment capacity

Subscenario

Treatment technology

1

Remains at Ivry

Waste from the Ivry sub-basin + imports

2

Remains at Ivry

3

Relocation 12 km away

Reduced capacity (only waste from the Ivry subbasin) Waste from the Ivry sub-basin + imports

4

Relocation 75 km away + waste transfer centre at Ivry

Reduced capacity (only waste from the Ivry subbasin)

1.1 1.2 1.3 2.1 2.2 3.1 3.2 3.3 4.1 4.2 4.3

I S + M + Irdf S + M + Lrdf I S + M + Irdf I S + M + Irdf S + M + Lrdf I S + M + Irdf S + M + Lrdf

Notes: S = sorting, M = methanization, I = incineration, Irdf = incineration of the RDF, and Lrdf = landfilling of the RDF.

Table 10 Performance and impact indicators calculated for scenarios 1 and 2

Total energy balance (TJ) Overall rate of recycling/recovery (%) Overall rate of recycling (%) Greenhouse gas emissions (kt equiv. CO2) Air acidification (kg equiv. H+) Residual waste in Haz. Waste landfill (tons) Residual waste in Non-Haz. waste landfill (103 tons) Heavy metals (Hg + Cd) (kg) Dioxins/furanes (g) Distance travelled on road (103 km)

S1.1

S1.2

S1.3

S2.1

S2.2

2968 93.8% 16.1% 565 21,358 16,151 34 202 0.4 6539

1761 82.4% 53.0% 309 12,523 5510 138 102 0.2 6647

379 53.0% 53.0% 157 2491 0 383 0.696 0.0 7111

2170 78.7% 18.8% 475 15,780 12,692 161 145 0.3 6673

1092 68.1% 53.0% 244 7590 2846 257 52 0.1 6776

Table 11 Performance and impact indicators calculated for scenarios 3 and 4

Total energy balance (TJ) Overall rate of recycling/recovery (%) Overall rate of recycling (%) Greenhouse gas emissions (kt equiv. CO2) Air acidification (kg equiv. H+) Residual waste in Haz. waste landfill (tons) Residual waste in non-Haz. Waste landfill (103 tons) Heavy metals (Hg + Cd) (kg) Dioxins/furanes (g) Distance travelled on road (103 km)

S3.1

S3.2

S3.3

S4.1

S4.2

S4.3

1399 93.8 16.1 568 21,472 16,151 34 202 0.40 8283

1212 82.4 53.0 339 14,770 5510 138 102 0.2 8484

617 53.0 53.0 187 4720 0 383 0.765 0.0 8793

1051 78.7 18.8 481 16,069 12,692 161 145 0.3 8768

907 68.1 53.0 275 9836 2846 257 52 0.1 8531

590 53.0 53.0 188 4716 0 383 0.759 0.0 8739

4. Discussion and conclusions The performance and impact indicators calculated in the previous section may be used to compare the pros and cons of the different scenarios. It is also possible to introduce weights for the different indicators, in a multiple-criteria analysis framework. If the indicators are considered without any particular weights, then sub-scenario 2 (sorting of MSW followed by methanisation of residual waste and incineration of RDF) may appear to be more favourable than the others as it leads to a better balance between energy recovery (Fig. 4a) versus atmospheric emissions and quantities of waste to be landfilled.

With respect to relocation of residual MSW treatment units, analysis of scenarios 3 and 4 compared to 1 and 2 shows an increase of distances travelled on road (Fig. 4i). Relocating residual MSW treatment units increases the kilometres driven and the number of trucks circulating in the Parisian traffic. As an example, the time used by road vehicles for collection and transport of waste increases by 6.9% in scenarios S3 versus S1, while the number of trips made by trucks increases by 88% in scenarios S4 versus S1. This represents an important criterion in the Parisian context. The recycling and overall recycling/recovery rates (Fig. 4b and c) are directly linked to treatment options. Relocation of treatment plants has no effect on these indi-

J. Villeneuve et al. / Waste Management 29 (2009) 2–11

1

0.8 0.6 0.4

4 3 2 1 0

10000

S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

0

h

0.5 0.4 0.3 0.2 0.1 0.0

S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

20000

f

16000 12000 8000 4000 0 S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

20000

Haz waste landfill (tons)

e

Travel distance (106 km)

30000

Dioxins & Furanes (g)

g

S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

Non-Haz landfill (10 5 tons)

5

0.2 0.0

S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

d

Air acid. (kg eq. H+)

S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

7 6 5 4 3 2 1 0

0.4

S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

0

c

0.6

i

10 9 8 7 6 5

S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

2

b

1.0

Recycling rate

a

Energy recovery rate

3

S1.1 S1.2 S1.3 S2.1 S2.2 S3.1 S3.2 S3.3 S4.1 S4.2 S4.3

GHG (10 5 t eq CO2)

Energy balance (106 GJ)

10

Fig. 4. Performance and impact indicators calculated for the different scenarios (quantities per year).

cators, as shown in the comparison between scenarios S1.1 and S3.1. Unlike scenario 1, for which the production of steam is favoured considering the possibility for the SYCTOM to connect to the urban heating network, electricity production is favoured when relocating of facilities is foreseen (S3 and S4). Although relocation generates an increase in fuel consumption for collection and transport, the main factor influencing the energy balance is the type of energy produced (steam in Ivry, electricity for scenarios with relocation; Fig. 4a). It is reminded that in the global energy balance, steam is undervalued with respect to electricity in terms of transformation into mechanical energy. This explains the better balance observed for scenario 3.3 compared to scenario 1.3. Based in part on the analysis presented herein, the SYCTOM decided to privilege scenario 2.2 (no relocation, sorting + methanization + incineration of the RDF). The analyses provided SYCTOM with quantitative arguments to substantiate its choices. It also confirmed the utility of a process-based approach to waste management systems. An important aspect of the analyses was the close interaction with the technical staff of the SYCTOM, required in order to establish a realistic flowsheet of the waste management situation in the Ivry basin. This revealed many data gaps that had to be filled, which underlines the integrating capacity of the flowsheet approach.

With respect to classical life cycle analyses, the AWAST analyses differ in the sense that the objective is not to help identify the environmentally ‘‘ideal” waste management system. It is a complementary approach designed to help select the best options for a given situation, taking into account its specific technical constraints. The use of simulation provides decision-makers with quantitative arguments that can be brought into the public debate. It can help to reach a consensus with respect to the actual situation of waste management, to define meaningful scenarios as a compromise between political objectives and technical and economic constraints and also to reach an agreement regarding the choice of indicators for comparing management scenarios, taking into account the views of different stakeholders. Further developments of the AWAST simulator include the implementation of additional waste treatment processes (in particular mechanical–biological pre-treatment of waste) and also the interfacing between the simulator and databases of waste fluxes that certain operators maintain. Acknowledgements The development of the AWAST simulator was supported by the European Research 5th Framework

J. Villeneuve et al. / Waste Management 29 (2009) 2–11

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