METALLURGICAL PERFORMANCE OF WASTE MANAGEMENT SYSTEMS: A SIMULATION APPROACH TO COMPLEX MATERIAL BALANCES WITH AWAST

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1 J. Environ. Eng. Manage., 18(1), (2008) METALLURGICAL PERFORMANCE OF WASTE MANAGEMENT SYSTEMS: A SIMULATION APPROACH TO COMPLEX MATERIAL BALANCES WITH AWAST Jacques Villeneuve,* Philippe Wavrer and Pascale Michel Bureau de recherches géologiques et minières (BRGM) Orléans 45060, France Key Words: Simulation, waste management, material balance ABSTRACT The European drivers of waste management tend to limit the final disposal, thus favouring matter and energy recovery from the waste. Municipal solid wastes are made of mixtures of heterogeneous particles often collected in several fractions. In order to properly design the complex chain of technical means that collect, transport, split, transform, store the waste particles, the AWAST simulator proposes a process analysis approach used in mineral industry and metallurgy. It allows a precise definition of waste composition in terms of components (goods) and chemistry (substances). Each waste management operation is represented by models as close as possible to physical splitting or chemical transformation processes. Waste composition handled by models determines the relations between recovery of goods and distribution of substances. The simulator provides a link between material flow analysis and substance flow analysis that helps to better understand existing waste management systems and to evaluate optimised scenarios. INTRODUCTION The AWAST simulator [1] was issued from the AWAST 5th FP project ( It is based on process analysis techniques developed initially for the mineral industry as described by Brochot et al. [2] and Guillaneau et al. [3], and furthermore includes energetic and economic aspects of the entire management system: collection, transport, recycling, biological treatment, thermal treatment and landfill. The software is designed to adapt best practices and to build strategies for sustainable municipal solid waste (MSW) management, disposal and reuse in cities or rural areas. This tool, thanks to its flexibility and adaptability to the local context, enables cities and industrial operators to: (1) evaluate the present situation in terms of waste processing efficiency and cost, energetic balance, residual streams, etc; (2) accompany, control and re-orient the choices; (3) define and plan sustainable degrees of progress; and (4) improve the implementation of the concept of integrated MSW management. The simulator basically allows getting a representation of a given situation in terms of matter and energy "flows" circulating between "processes". By changing some key parameters of the system (either in input flows or in processes), it calculates the new material and energy balances. It supplies particularly the flows of matter output from the system to the environment. Additionally, the simulator supplies the costs associated with the operations and the "value" of the different flows, as explained by Villeneuve et al. [4]. The simulator does not integrate in itself the interpretation of its results. It remains an advanced calculator containing functions that can manipulate experimental data, calculate coherent material balances, sizes and settings of unit operations, simulate plant operation and display results in tables and graphs. The AWAST simulator combines the following elements [4]: The flowsheet describes the system in terms of successive unit operations and material streams. This flowsheet can reflect various scenarii so that they can be compared against given criteria. It takes into account numerous system features to describe the physical (sorting plant) as well as functional (sorting of glass) elements. The phase model describes the materials handled by the system (raw waste, products, reagents, water, etc.). The description of the phases is critical for analysing and optimising the system, as it imposes the type and quantity of data necessary to feed the simulator. For real applications, it has to be simple enough to face the general lack of data, but complete enough to be able to handle the available data so that operations performance, products and re- *Corresponding author j.villeneuve@brgm.fr

2 66 J. Environ. Eng. Manage., 18(1), (2008) Table 1. Waste generated in Holiwast case studies, according to municipality data (all blank cells worth zeros) Amount Katowice Turin Tollose t y -1 kg cap -1 y -1 t y -1 kg cap -1 y -1 t y -1 kg cap -1 y -1 Mixed waste 113, , , Paper 1, , Glass , Mixed secondary material , (plastics and metal) Batteries Road waste 3, , Biowaste 54, Plastics 7, WEEE 2, Metals 2, Hazardous Garden and park waste Market waste Sum 120, , , agents quality can be evaluated. Mathematical models for each unit operation (collection, transport, biological treatments, thermal treatments, landfills) formalises the current scientific knowledge about the unit operation, and its level of complexity depends on the data available and the targeted objectives (i.e. flowsheeting, unit operation sizing, or optimisation). The model parameters - dimensions, settings and calibration factors - are set to default values and can be calculated or validated from field data. The models can evolve as the knowledge on the processes improves without prejudice to the simulator structure. A set of algorithms for data reconciliation, model calibration, unit operation sizing, full material balance calculation, power consumption and costs calculation is interfaced with a set of data representation tools. As a result, the plant simulator constitutes a highly efficient communication vector between the different actors who play a part in the system operation and evolution. WASTE DESCRIPTION GOODS AND SUBSTANCES 1. Waste Flows The assessment of waste generation is a key element in waste management. It determines not only the design of the waste management system (organisational and technological choices, sizes of facilities), but also the evaluation of the performances in terms of functional units (tons per habitant, ratios expressed per ton or per inhabitant). In commonly encountered situations, MSW generation results from the quantity of the flows collected by municipal services. Municipal waste in Europe falls under the definition of the List of Wastes 2000/532/EC which includes (20 01) separately collected fractions, (20 02) garden and park waste, and (20 03) other municipal waste: mixed municipal waste, waste from markets, street-cleaning residues, septic tank sludge, waste from sewage cleaning, bulky waste, municipal wastes not otherwise specified. Table 1 shows such an assessment for the three case studies conducted during the FP6 Holiwast project [5]. 2. Components and Chemistry The data presented in Table 1 bring little help to waste management planning and evaluation: for instance, the questions concerning the improvement of selective collection need further analysis of the recovery potentials of mixed waste. The waste for which the definition is not precise (mainly mixed waste, bulky waste) often represents an important mass fraction of waste generated (as in Table 1). These wastes are commonly described according to the physical possibilities to distinguish different sizes and categories (i.e. putrescible, paper, cardboard, textiles, metals, plastics, etc.). The categories may be further separated in sub-categories (i.e. plastics may be categorised into polyethylene, polyethylene terephthalate, high density polyethylene, polyvynil chloride, etc.). This type of characterisation requires an experimental procedure which for practical reasons leads to sort physically only a limited part of the waste (a sample some hundred kilos). In some countries, the set up of characterisation procedures (as MODECOM in France) tends to limit the sampling errors. The FP5 SWA-tool proposes a framework method in the field of mixed waste [6]. Each category can be further analysed in terms of physical and chemical composition (heating value, substances, etc.). The analysis is done on only a limited part of the sorted categories (some grams or less). So far, all composition data are representative of the

3 Villeneuve et al.: Modelling Basic Operations in Waste Management with AWAST 67 Mixed waste Fig. 1. The waste composition is described by 2 matrices: components per size and chemistry per component. waste only with respect to sampling and analytical errors. Following these characterisations, the waste can be expressed as two matrices of components per size and chemical elements per component. The AWAST software allows a flexible definition of these characterisations: the definition of sizes and components can be adapted to available data (Fig. 1). In the AWAST project, it was proposed by Fehringer et al. [7] a framework description of categories and chemical elements in which most of the available national data in EU15 could fit. This work was conducted building on the precise definitions used in MFA (Material Flow Analysis) since the end of the eighties by Baccini and Brunner [8]. MFA defines a good as a tradable matter consisting of one or several substances. A substance is a chemical element or compound in its pure form. Following these definitions, the components deriving from physical analysis of waste will be considered as goods. The term material will be used to describe indifferently goods and substances. This led to the definition of waste matrices (see AWAST deliverable 25-28, annex 5), giving mean waste composition at European level for a number of different waste flows (mixed waste, selectively collected paper, glass, plastics, etc.). MODELS OF UNIT OPERATIONS 1. Modelling Concept There are mainly two applications of models, non exclusive from each other: mass balancing and design or optimisation of operations. These two applications require two types of models. The first type is called non predictive and relies essentially on the concept of transfer coefficient. The second type is called predictive as it aims at describing and interpreting what happens in real world, and tries to predict what would happen if some changes in operations occur. Only the first type of models is presented here as it is not directly process dependant and allows the calculations of coherent goods and substances flows. Non predictive models of unit operations handle the stream description presented above in two ways: separations according to physical characteristics of the waste (size, components) and transformations implying modifications of the physical nature of components (by chemical reactions). 2. Splitting Splitting affects particles. It represents operations based on their physical properties: size, density, shape, hardness, among others). These separations do not affect the chemical composition of components, but result in particles having the same physical properties. The global chemistry of the separated flows evolves according to the chemistry of components. As an example; let us take the following flow presented here in its size per component matrix (Table 2). The splitting is done by a trommel (according to size) following the split coefficients of Table 3 that assume that 100% of the coarse particles are recovered in the coarse flow, 50% of the medium-sized particles are recovered in the coarse flow, etc. These split coefficients do not correspond to reality as recovery in coarse depends on many factors (shape, density, entrainment). As a result of the operation, the following outputs are calculated. It can be seen in Table 4 how the

4 68 J. Environ. Eng. Manage., 18(1), (2008) Table 2. Input flow for splitting operation Size distribution Components Class (mm) Individual (%) Paper (%) Glass (%) Fe metal (%) Plastics (%) Biowaste (%) Other (%) > < Mean Table 3. Split coefficients (transfer function for a size separation) Size distribution class (mm) Components Paper (%) Glass (%) Fe metal (%) Plastics (%) Biowaste (%) Other (%) > < Table 4. Coarse (53,750 t y -1 ) and fine (46,250 t y -1 ) flows Size distribution Components for coarse flow Class (mm) Individual (%) Paper (%) Glass (%) Fe metal (%) Plastics (%) Biowaste (%) Other (%) Coarse flow > < Mean Fine flow > < Mean split affects the composition of the goods considered in the description of the flow. The second matrix description of chemical elements per component (Table 5) remains unchanged but the consequence of splitting on chemistry is illustrated in Table 6. From these figures it can be said, as an example, that: (1) plastic is the main carrier of cadmium, (2) 77% of plastics are recovered in the coarse stream, and (3) only 59% of cadmium is recovered in the coarse stream. 3. Transformation The transformation processes involve a change in the nature of particles (incineration, aerobic or anaerobic fermentation). These processes are represented by chemical reactions affecting the chemical elements of the different components (C org for example) and limited to a proportion of the component reacting. In composting, some of the paper particles are transformed by C org CO 2 and the remaining particles remain as paper with the chemical constitution of paper. The non-reacting chemicals of the reacting part of paper degrade as ashes. As an example, let us return to our feed flow (Table 2) and assume a composting operation where the non reacting part of components is defined as shown in Fig. 2. The reacting part of components may proceed through chemical reactions that are defined by the user. The reactions involve chemical elements whatever their belonging to components. They are defined by the user (Figs. 3 and 4). The reactions system can be further elaborated according to study objectives. The chemical elements that do not react constitute the ashes of the transformation process and can be distributed in other components. This can be of particular interest to simulate if the product fits with the legislative standards of composts, even if it imposes to define in the phase model the description that corresponds to the standard. As a result of the transformation process in our case, Table 7 shows that some paper and biowaste have disappeared. Part of these is transferred to gas and the remaining to others. These reactions produce a change in the global chemical composition of solids as shown in Table 8.

5 Villeneuve et al.: Modelling Basic Operations in Waste Management with AWAST 69 Table 5. Chemical composition of the input Components Paper (%) Glass (%) Fe metal (%) Plastics (%) Biowaste (%) Other (%) H (ppm) 43, ,888 15,483 30,925 C-fossil (ppm) , ,745 C-org (ppm) 312,961 4,455 1, ,932 82,623 N (ppm) 1, ,185 3,960 4,385 13,314 O (ppm) 279, ,205 92, ,300 S (ppm) 1, , ,729 Cl (ppm) 2, ,000 1,731 2,881 F (ppm) P (ppm) ,391 Fe (ppm) 992 2, , ,693 3,416 Al (ppm) 10,687 9, ,923 24,881 Pb (ppm) Zn (ppm) ,508 Cd (ppm) Hg (ppm) Cr (ppm) 19 4, As (ppm) Cu (ppm) ,195 H 2 O (%) Other (%) Table 6. Splitting of chemical elements due to splitting of components Components Input Coarse Fine Concentration in coarse H (ppm) 34,065 38,388 29, C-fossil (ppm) 144, , , C-org (ppm) 101, ,383 90, N (ppm) 7,396 6,405 8, O (ppm) 143, , , S (ppm) 1,833 1,764 1, Cl (ppm) 7,359 9,533 4, F (ppm) P (ppm) , Fe (ppm) 46,425 64,221 25, Al (ppm) 14,023 12,242 16, Pb (ppm) Zn (ppm) Cd (ppm) Hg (ppm) Cr (ppm) As (ppm) Cu (ppm) , H 2 O (%) Other (%) Fig. 2. Non-reacting part of waste components. Fig. 3. Chemical reactions settings.

6 70 J. Environ. Eng. Manage., 18(1), (2008) 25 t.h -1 Municipal solid waste Screens Collection DANO Overband 0.4 t.h -1 Metal scrap Bales 14 t.h -1 Double belt selector 0.5 t.h -1 Ballistic Fig. 4. Chemical reactions definitions. Table 7. Transformation results in a change of components grades Components Input Output Mass flowrate (t y -1 ) 100,000 81,585 Components (%) Paper Glass Fe metal Plastics Biowaste Others Table 8. Transformation results in a change of the global chemistry of solids Components Input Output Concentration factor Mass flowrate (t y -1 ) 100,000 81,585 H (ppm) 34,572 35, C-fossil (ppm) 117, , C-org (ppm) 124, , N (ppm) 4,429 5, O (ppm) 136, , S (ppm) 1,340 1, Cl (ppm) 8,337 10, F (ppm) P (ppm) Fe (ppm) 16,194 19, Al (ppm) 8,788 10, Pb (ppm) Zn (ppm) Cd (ppm) Hg (ppm) Cr (ppm) As (ppm) Cu (ppm) H 2 O (%) Other (%) In particular, the heavy metals originally contained in the biowaste and paper are transferred to the component others, leading to an accumulation in the compost. Of course, the approach may be refined by adding reactions (to account for example for the leaching or evaporation of metals during fermentation) in case experimental data allow it. Compost 9 t.h -1 Fig. 5. Flowsheet of the MBT plant including a fermentation process (transformation figures by the DANO equipment) and physical separations (splitting by size in the screens, by density in a double belt selector, by magnetic susceptibility in the overband). Collection type Collection efficiency (kg/inhab/y) NSOM Simulation results (in %) Films Heavies > 5 mm > 5 mm Total inerts Simulation results (in mg/kg dry) Hg Cd Ni Pb Cu Zn Raw MSW Collection Collection Collection In Grey: Value exceeding CERAFEL Standard Collection 1: Hazardous waste. Collection 2: Packagings, newspapers-magazines. Collection 3: Packagings, newspapers-magazines, hazardous. Fig. 6. Simulation results presenting the characteristics of compost after treatment of residual waste. EXAMPLES OF APPLICATIONS This paragraph presents two examples: influence of selective collection on the distribution of pollutants in waste treatment products and analysis of complex systems. 1. Influence of Selective Collection on the Distribution of Pollutants in Waste Treatment Products This example extracted from the study of Wavrer et al. [9] presents a real case study of a mechanicalbiological treatment (MBT) plant in France. Figure 5 presents the flowsheet of the plant which combines transformation and splitting operations. The aim of the study is to assess the effects of selective collections on compost quality. The flows concerned are hazardous waste, packaging, newpapers-magazines. These flows are described as shown in Fig. 1 by two matrices of components per size and chemistry per component. These flows were subtracted to the MSW using three combinations (collection 1 = only special waste, collection 2 = only packaging and newspapermagazines, collection 3 = three flows) and two efficiency hypothesis expressed in kg/year/inhabitant. The residual waste was treated and simulation results

7 Villeneuve et al.: Modelling Basic Operations in Waste Management with AWAST 71 SYCTOM Waste Treatment system To recycling Bulky waste sorting Civic amenity sites Dech. Paris Fe Dech. Paris DV Dech. Paris gravats Dech. Divers Dech. Fe Dech. DV Dech. gravats Dech. divers To other landfills FCR DV Incineration OE OM Separate collections Balayures Landfill Multi Verre Supplies To recycling Bottom ash treatment Sorting plant Wasre Export. To recycling Waste Import Fig. 7. Waste management system in Paris. This flowsheet includes scenarios. of the treatment were obtained (Fig. 6). In Fig. 6 the results both in terms of components (Non Synthetic Organic Matter, films, etc.) and in terms of chemical elements (Hg, Cd, etc.) linked together in all streams of the plant are shown. Note that the components used for the description of compost are not the same as those for the description of MSW. They correspond to the ones used in the French standard for compost quality (In Fig. 6, CERAFEL stands for Comité Economique Régional Agricole Fruits Et Légumes which is a dialogue group between farmers and agro-industry. They imposed local quality standards for compost stricter than the national standard). In addition to the transformation presented above, the composting model operates a transfer between components. 2. Analysis of Complex Systems This former example on a plant can be extended to the entire waste management system. Figure 7 represents the existing system in Paris (France) completed by possible diversification of treatment technologies. For such large systems, the matter balance calculation uses the same basic models of splitting and transformation, each plant being represented at a level of details equivalent to the former example. The incineration plant in the middle of Fig. 7 is described in a sub-flowsheet (Fig. 8) connected to the main flow sheet. CONCLUSIONS The AWAST simulator for waste management systems is based on concepts of mineral and metallurgical processing. Particularly the matter circulating in the flows of the flowsheet is described with mainly two matrices, the first reflecting the physical nature of particles (size and component) and the second providing the chemistry of components. Models are designed to handle both matrices according to the nature of the process by which particles are managed. Physical splitting affects the first matrix while transformation (i.e. fermentation, combustion, etc.) involves both matrices. Using these two basic models, virtually any system can be represented. By handling waste

8 72 J. Environ. Eng. Manage., 18(1), (2008) Imported waste Flue gas Waste Waste1 Grosses Fe Steam water 57 Combustion air Furnace Turbine-alternator group Bottom ash cooler Screen Bottom ash 13 Steam 14 8 Mill 28 3 ESP Fly ash Steam to environment Scrap to environment Overband 9 Spray tower Neutralization Reagent addition Decantation Filter press Bottom ash stock Bottom ash to environment Fig. 8. Sub flowsheet incineration plant (shown in part, the flowsheet includes the furnace combined with the energy production system boiler and turbine-alternator group, the treatment of bottom ashes to recover scrap iron, and the treatment of flue gas electro-static precipitator, spray tower). composition, the models determine the relations between recovery of goods (components) and distribution of substances (chemical elements). The simulator thus proposes a method to link MFA and substance flow analysis. This method helps to better understand existing waste management systems and to evaluate optimised scenarios. REFERENCES 1. AWAST, Aid in the Management and European Comparison of Municipal Waste Treatment Methods for a Global and Sustainable Approach, ( ). 2. Brochot, S., J. Villeneuve, J.C. Guilaneau, M.V. Durance and F. Bourgeois, USIM PAC 3: Design and optimization of mineral processing plants from crushing to refining. In A. Mular, D. Barratt and D. Halbe (Eds). Mineral Processing Plant Design, Mineral Processing Plant Design, Practice and Control. Society for Mining Metallurgy and Exploration, SME Inc., Littleton, CO, pp (2002). 3. Guillaneau, J.C., J. Villeneuve, M.V. Durance, S. Brochot, G. Fourniguet and H. Durand, From sampling to simulation: the BRGM range of software for process analysis. Minerals Engineering Annual Meeting, Santiago, Chile, July 29-August 1 (1997). 4. Villeneuve, J., P. Wavrer, P. Michel and Y. Menard, Comparative analysis of integrated waste management solutions: a case study. Tenth international Waste Management and Landfill Symposium, S. Margherita i Pula, Italy, October 3-7 (2005). 5. Holiwast, Holistic Assessment of Waste Treatment Technologies. ( ). 6. SWA-Tool, Development of a Methodological Tool to Enhance the Precision and the Comparability of Solid Waste Analysis Data, ( ).

9 Villeneuve et al.: Modelling Basic Operations in Waste Management with AWAST Fehringer, R., B. Brandt, P.H. Brunner, H. Daxbeck, S. Neumayer, R. Smutny, J. Villeneuve, P. Michel, M. Krannert, A. Schultheis and D. Steinbach, MFA-Manual: Guidelines for the Use of MFA for Municipal Solid Waste Management. AWAST deliverable D1-D2 (2002). 8. Baccini, P. and P.H. Brunner, Metabolism of the Anthroposphere. Springer Verlag, Berlin, Germany (1991). 9. Wavrer, P., H. Védrine, J. Villeneuve, B. Morvan and N. Noyon, Contribution of a modellingsimulation approach to analyse the performance of MSW sorting-composting plants. Proceedings Waste 2000 Conference, Strattford-Avon, UK, October 2-4, pp (2000) Discussions of this paper may appear in the discussion section of a future issue. All discussions should be submitted to the Editor-in-Chief within six months of publication. Manuscript Received: July 7, 2007 Revision Received: November 30, 2007 and Accepted: December 6, 2007