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Figure 9.3 An example of metallurgical processing of complex recylates and the destination within the furnace of some of the elements and compounds under various thermodynamic conditions.

metallurgical, and thermal separation performance when assessing resource cycles, performing design for recycling, and monitoring the progress and limits of recycling systems. Such a level of detail cannot easily be anticipated by the product designer, but can be predicted by first-principle system models which treat the rate, composition, and toxicity of recycling output streams as a function of design choices. These models, only recently under development, have the potential to simulate and optimize the recycling system of products, including the effect of (a) material composition of the product and particle composition and degree of liberation, (b) particle size classes, (c) separation efficiency of physical, metallurgical, and thermal processing as a function of recyclate quality (particle composition, degree of liberation, chemical composition of recyclates), and (d) recycling system architecture (arrangement and combination of recycling and final treatment processes/technology).

We illustrate the use of simulation models through an example of waste electrical and electronic equipment (WEEE), specifically, cathode ray tube (CRT) recycling. In this approach (Reuter et al. 2005), the model accepts a set of physical materials as selected by the designer. In this case:

Materials: Al, Cu, other metals, ferrous, stainless steel, PM, solder, glass PbO, glass BaO, phosphorous powder, ceramics, wood, PP, PVC, ABS, epoxy, others, and electronics.

Next, the model transforms them via different separation unit operations into their resulting compounds:

Compounds in materials: Al, Mg, Si, PbO, Fe2O3, Fe3O4, ZnO, Sb2O3, W, Cr, ZnS, Y2O3, Eu, Ag, Au, Pt, Pd, Rh, Pb, Cu, Bi, Ni, Co, As,Sb, Sn, Se, Te, In, Zn, Fe, S, Cd, Hg, Tl, F, Cl, Br, Al2O3, CaO, SiO2, MgO, Cr2O3, BaO, TiO2, Na2O,

These are then recovered as metals, plastics, and energy by the appropriate chemical transformation, which is dictated by thermodynamics within a suitable technology and economic framework.

The simulation/optimization models produce closed mass balances for each liberated and nonliberated material, and recycling/recovery rate predictions linked to product design choices. The model is fundamentally based on the conservation of mass, elements, compounds, particles, groups of materials, as well as on physics, thermodynamics, chemistry, and mass transfer between phases and not simply on total element and material flows upon which the more simplified approaches (e.g., LCA and MFA) rely. The more detailed approach, as applied here, from which the actual recovery of individual materials and energy can be calculated, is crucial for prediction and control of the actual distribution of toxic and contaminating substances into different individual particles and the various recycling streams and their destination after final (metallurgical or thermal) treatment. This provides an accurate and reliable basis for the control and assessment of toxicity and the related environmental/ eco-efficiency consequences; hence it is crucial for monitoring and quantifying progress in time.

The model structure, a portion of which is depicted in Figure 9.4, captures all the phenomena discussed above. For example, the separation efficiencies for the multi-material particles are determined as functions of the different materials present and have been determined for all physical separation processes and all classified multi-material particles.

From Figure 9.5 it is clearly evident that the maximum amount of the valuable metals copper and tin will be recovered if the PWBs are directly treated in the copper smelter. However, the recovery of steel and aluminium will then be low, as these metals will end up in the slag, a building material of low value. The model results corroborate recent fi eld data obtained by Chancerel and Rotter (2009). Figure 9.6 gives a typical example of the quality and toxicity calculations as captured by the model for both recyclate and output streams; in this case for the copper smelter for the list of materials and compounds listed previously. Because the model uses dismantling groups and the respective recyclate grades in terms of chemical compounds, it can be used to predict the qualities of metal, slag, and flue dust in terms of compounds (and hence therefore their toxicology). The model results show the effect of no shredding, medium, and high shredding on the recovery of valuable specialty and commodity metals from PWBs.

A potential, significant advantage of separation and process modeling is its ability to predict leakage for less abundant elements from the system for different plant confi gurations (including dismantling) and shredder settings, and hence suggests mitigating choices in design and processing to minimize losses. Such guidance is essential if sustainability measurements are to be used to influence the nonrenewable flow of material. Obviously the market-related economic value of the recyclates—the basic measure that determines whether

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