DESIGN-BASED DYNAMIC MODELLING OF E-WASTE RECYCLING SYSTEM PERFORMANCE
1
Antoinette van Schaik1* and Markus A. Reuter2 MARAS – Material Recycling and Sustainability -, The Hague, The Netherlands 2 Ausmelt Ltd., Melbourne, Australia *
Corresponding author:
[email protected]
Abstract E-waste covers a wide range of products as well as dismantled and/or sorted components originating from these. Being able to predict the flow of materials and recycling performance for different E-waste types requires a fundamental and flexible basis in which E-waste design properties are linked to liberation and separation performance of recycling. This paper discusses the authors’ design-determined liberation and dynamic models to predict and control E-waste recycling technologically, economically and environmentally. The uniqueness of this work lies in the modelling of product design characteristics in terms of design tables that define the mass and material connections derived from the design in real-time. The shredding process is modelled by defining shredder connection, and shredder liberation tables, based on heuristic derived from extensive data collection on design and shredder experiments. This facilitates the design-driven modelling of liberation. The heuristic modelling of liberation behaviour and prediction of particle composition and degree of liberation after shredding based on design-driven shredder modelling is a novel approach to link design to recyclate quality and recycling performance. This work is underpinned by extensive industrial data collection on product design of various WEEE categories, which was used to define and calibrate the models. The time dependent characteristic of design and recycling technology requires the adoption of dynamic modelling to predict recycling performance over time. The discussed dynamic and predictive framework provides a first principles basis for the calculation of the dispersion of harmful/valuable elements and environmental impact, preventing unnecessary spending of money on large scale trials and monitoring test to establish these results. At the same time legislation and policy can be tested if it stands the test of time or if new designs comply with environmental legislation in the future. Prediction of recycling performance, recyclate quality, toxicity and economics as a function of product design, shredding and (future) recycling system configurations will be demonstrated in various industrial examples, which includes an evaluation if shredding is good or not for precious and platinum group elements recovery during recycling. Also the so important balance between energy recovery and feedstock recycling is investigated. Keywords: design, liberation modelling, dynamics, system modelling, recycling, (recyclate) quality 1.
Introduction
The recycling of E-waste or Waste Electric and Electronic Equipment (WEEE) involves a wide spectrum products and materials, ranging from commodity metals and different plastic types (e.g. containing flame retardants), to precious metals as well as harmful minor elements. The so important question of to-shred-ornot-to shred as well as the balance between recycling and energy recovery are always questions to be considered. This makes the á priori prediction of recycling systems for E-waste indispensable from both an economic as well as environmental point of view. Crucial in all this is the control and prediction of recyclate quality as a function of (time-varying) product type and product design affecting the efficiency of the recycling processes applied (both physical and metallurgical/thermal) to recover the material and/or energy content of the product. Although methodologies such as MFA (Material Flow Analysis), LCA (Life Cycle Analysis), or
comparable methods claim to do this, these methods lack the fundamental knowledge required to predict recyclate quality and recycling performance and resulting material flows (toxicity, economic value, quantities, etc.) over time, hence requiring that expensive and time consuming tests are being carried out to collect the required detail of E-waste recycling as recently discussed by Chancerel et al. (2008) The application of LCA, MFA and comparable methodologies for the control, planning and prediction of E-waste recycling have been discussed by e.g. Kahhat et al. (2008), Nnoroma and Osibanjo (2008), Ruhrberg (2006), Den Boer et al. (2007), Park et al. (2006), Lu et al. (2006), Schmidt (2005) and Thomas and McDougall (2005). Generally these models cannot be linked to computer aided design to fundamentally predict recycling rates from product design. First principle models, based on the fundamental principles of recycling and liberation modelling are required to predict the behaviour of recycling systems in a sufficiently detailed and objective manner. The fundamental aspects to be addressed to characterize the recycling system of E-waste recycling are listed below and discussed in the next sections. Product design and liberation. The functional and aesthetic specifications of a product determine the selection of materials as well as the complexity of the material combinations and connections within this product (e.g. welded, glued, alloyed, layered, inserts, etc.). Liberation (disconnection) of the (complexly) connected materials during shredding or dismantling determines the quality of recycling streams, through creation of mono (pure) or multi-material (impure) particles. The composition and the degree of liberation of the particles or components after shredding or dismantling is directly determined by the design of the product as well as by the efficiency and intensiveness of shredding (or dismantling). In order to understand and predict the effect of design and shredding on liberation behaviour, the authors have gathered a large body of data on shredding and recycling experiments as well as by detailed and careful dismantling, destruction and analysis of WEEE products. These data have been used to identify characteristic design properties as well as to define liberation behaviour for different material connection types, which have been the basis for defining models for liberation, which will be presented in this paper. Physical separation. Physical sorting processes are governed by physics of separation and the properties of the different materials present in the shredded product and recyclates. The separation efficiency of the individual processes is largely affected by the composition or quality of the input of the process (i.e. the original product or the intermediate recycling stream), hence being a function of the actual composition the individual mono and multi-material particles created by design and liberation processes. This implies that the separation efficiency needs to be accounted for as a function of particle composition. Metallurgical, thermal and (non-)organics processing. The actual closure of the material and/or energy cycle of products is determined by the final treatment processes such as metallurgical and thermal processing. The separation and recovery in these types of processes into different phases (metal, slag, flue dust, off gas) is determined by thermodynamics as a function of temperature and by the chemical content and interaction between different elements/phases present in the recyclates obtained form dismantling and/or physical separation. The physical quality of recyclates is directly related to the chemical composition of the metal fraction (alloying composition, contaminations, oxides, etc.), the non-organic fraction and the chemical composition and heat content of the organic fractions. Dynamic Feedback Control Loop. The input to the recycling system changes over time as a function of changing product design, product composition (e.g. as a function of legislative restrictions such as the ban on the use of CFC coolants in refrigerators), life time and consumer use and disposal behaviour. In order to predict and control the performance of recycling systems over time, calculate time and input dependent recycling rates and toxicity, these dynamic principles have been incorporated in the developed models. In order to apply Design for Recycling (DfR) over time, the fundamental principles of recycling systems have to be dynamically fed back to the designer. The prediction and control of the recyclates quality and toxicity and hence the recycling/recovery rate requires fundamental modelling and understanding of the liberation behaviour from product design to recyclates and recovered/emitted products as a function of time. Although Georgiadis and Besiou (2008) examine the impact of ecological motivation and technological innovations on the long-term behaviour of a closed-loop supply chain with recycling activities based on
system dynamic modeling, this work does not address the required detail of recycling and recyclate quality prediction. Mathieux et al. (2008) discuss the multi-process modelling of recovery scenarios and the quantitative, multicriteria and multi-scenario assessment of the recoverability of a product. However the modelling of recovery processes is data driven, and not based on fundamental principles. Although high-temperature processing options such as metallurgy and thermal treatment have been assessed in the models, the discussed approach does not include thermodynamic and chemistry as a basis for modelling the performance of these processes. This makes these models fundamentally too weak to predict actual recycling output stream composition, toxicity, and recycling performance, rendering the results debatable. The same applies to the Qwerty method as discussed by Huisman et al. (2003), which is furthermore not capable of including the effect of liberation and quality. This makes that these types of models are not detailed and fundamental enough to evaluate the environmental consequences of recycling or to be applied as a basis for Design for Environment. The key fundamental contribution of the work presented in this paper lies in the modelling of design in terms of mass and material connection matrices (defined on the basis of collected design data), capturing the liberation decisive properties of design in a product design/construction suitable format (e.g. compatible with product definitions in Computer Aided Design (CAD) software). Heuristic rules to model liberation behaviour predicting the (i) composition of particles (created) after shredding and (ii) degree of liberation; have been defined with respect to this design definition and have been estimated from extensive data collection (dismantling and destruction tests and earlier work of the authors) on product design and liberation characteristics for different connection types. All the above aspects are incorporated into the simulation model developed under the dynamic simulation platform of Simulink/Matlab® (Mathworks, 2007), which will be discussed in this paper. By considering the whole E-waste recycling system and its fundamental parameters, the maximum achievable recycling/recovery rate as well as the toxicity and environmental impact of the output streams as a function of product design can be assessed. Being developed on a fundamental basis (applying physics and process thermodynamics and chemistry) these models can be applied to the wide range of E-waste products. This prevents that a large number of tests on a variety of E-waste types have to be carried out in order to collect data to capture the effect of its different and time-changing characteristics, toxicity, and recycling behaviour. It will be illustrated on various industrial examples that these models are perfectly suited to predict and control the performance, material product streams and toxicity of E-waste recycling. 2.
First principle dynamic E-waste recycling models
The theoretical aspects discussed above highlight the importance of capturing the degree of liberation, recyclate and output quality and the effect on physical, metallurgical and thermal separation performance when assessing the recycling performance of E-waste or WEEE. Figure 1 illustrates the wide range of products, components and materials involved in E-waste. It will be clear, that not only the design (composition and connections) of the various E-waste products will differ between the various products, but that this will also change continuously over time as a function of functional and aesthetic requirements and consumer behaviour. This figure reveals the complexity and interconnectedness of time dependent design (different designs) of these type of products, which the recycling models must be capable of dealing with, the level of sophistication and detail required to model design in terms of materials and connection in view of modelling liberation behaviour and recycling performance of this type of products, all as a function of design and process dynamics. 2.1
The (dynamic) recycling flow sheet
The recycling/recovery rate of products can be considered to be one of the metrics to express the performance of the recycling system. This is determined by the separation efficiency of each of the various treatment processes applied in the recycling system of that specific product (category), ranging from dismantling, shredding, physical separation processes to metallurgical and thermal treatment of the streams. The first stage to describe, evaluate and predict recycling system performance of WEEE products is the combination of these individual recycling processes, being represented by the flow sheet for the recycling of
a specific product. To this end extensive flow sheets have been developed for different WEEE products, in which the currently used recycling processes as well as alternative processes have been integrated (see Figure 3 that shows dismantling, shredding/stripping/sawing, physical separation and extractive metallurgy/thermal treatment/(non)organics processing, incineration and landfill).
Fig. 1. Examples of various E-waste types (products and components). Figure 2 illustrates the link between the combination and connection of the materials as defined in the product design and the influence of liberation and particle composition on the separation efficiency, the recyclate quality and therefore on the maximum achievable recycling/recovery rate.
Fig. 2. A schematic overview of the link between product design, liberation, separation efficiency, recyclate quality and quality (and hence toxicity) of final output streams. Different (time dependent) flow sheet options can be simulated in the models based on defined structural parameters. These structural parameters allow the construction of different combinations of recycling processes in the flow sheet (flow sheet architecture) by which various industrial and future plant scenarios can be simulated (e.g. the evaluation of CRT glass to sawing and separation or concrete production). Earlier optimisation models developed for car recycling (Reuter et al., 2005; Ignatenko et al., 2007) have also illustrated that the structural parameters could be used in defining optimal recycling solutions by applying optimisation modelling. These solutions can be a function of legislation, recycling rates, energy, and environmental, and/or economic parameters. The definition of the flow sheet makes it possible to
evaluate the efficiencies and toxicity consequences of different recycling alternatives for a specific WEEE product/component, e.g. dismantling versus post shredding technology scenarios, even in combination with thermal processing (e.g. in view of Annex II), while predicting and controlling the toxicity of the streams, the dispersion of toxic elements as well as their recovery. The fundamental principles of recycling and the basic unit operations as illustrated in Figure 3 for the example of CRT TV’s are general to any type of WEEE product. In addition, the fundamental parameters for the different unit operations are similar, being at first based on separation efficiencies of pure materials (physical and chemical). Usually the main differences are the dismantling requirements and materials, presence of toxic materials, etc. as would be evident from Figure 1. Although the presence of e.g. toxic or valuable elements in different types of appliances and hence the de-pollution requirements, toxic hazards and related environmental consequences of recycling requires a different focus. Since actual process efficiencies are calculated in the model as a function of product specific design (composition and liberation) and these fundamental separation characteristics permits these to be used to a large extent for any simulation model for WEEE products. This renders the construction of flow sheet and model development in Matlab/Simulink® adaptable to any type of WEEE product by ‘clicking’ together different product specific unit operations.
Fig. 3. The overall flow sheet for recycling of collected CRT TV’s, disassembled into the eight fractions (from metal to Printed Wire Boards (PWBs) to CRTs) in which each material is accounted for, recovered as metal, plastic, energy or reports to landfill/leaks from system (Colour code for unit operations: black: eight disassembled groups from CRT TV’s ; cyan: physical separation/sorting; magenta: structural system parameter/addition of streams; orange: conversion; red: metallurgy/energy/landfill etc.).
2.2
Product design and material liberation during physical processing
The design of the product determines the selection of materials to be applied in products as well as the complexity of the material combinations and interactions within this product (e.g. welded, glued, alloyed, layered, inserts, etc.). The liberation (disconnection) of the different materials, which have been complexly integrated and connected in the product design, determines the quality of recycling streams (Reuter and Van Schaik, 2008; Van Schaik and Reuter, 2007). During shredding and/or dismantling particles or components are created either consisting of one (pure particle) or more materials (impure particles). The modelling of the liberation behaviour is crucial in the prediction and control of recyclates and output stream qualities (composition); as well as the dispersion of minor/toxic elements over the various streams as a function of imperfect separation and design choices. The modelling of product design, the shredding process and the prediction of the degree of liberation and particle composition after shredding is discussed in the next section. The unique inclusion of design aspects into the liberation behaviour of shredding is a novel contribution and will be discussed in detail in the next section. Embedding this all into a dynamic framework provides a simulation approach that uniquely permits the evaluation of the recyclability of end-of-life products already during the design phase. 2.2.1
Theory on liberation behaviour
The actual liberation (or disconnection) of materials connected in design takes places during dismantling and shredding of the product. Therefore the prediction and control of the recyclates quality and toxicity and hence the recycling/recovery rate requires the modelling and understanding of the liberation behaviour from product to recyclates. The fundamental modelling of liberation and comminution not only applies to the recycling of consumer goods, but dates back to classical minerals processing. The modelling of liberation behaviour of mineral ores has been discussed by e.g. Andrews and Mika (1976), Heiskanen (1993), King (2001) and Gay (2004). These references indicate that even in the field of classical minerals processing difficulties still exist to model the liberation of multi-component materials (which is the case for WEEE products), despite of the long period in which theory has been developed in this field. From the comparison of the modelling of liberation of ores with that of consumer products (Van Schaik et al., 2005) it was concluded that the breakage and liberation behaviour of consumer products differs fundamentally from that of mineral ores, indicating that a different approach is required to predict the liberation behaviour and hence the recyclate quality of consumer products. It was established through various industrial tests and observations, that breakage is non-random and cannot be defined by classical minerals processing theory. In earlier work (Van Schaik et al., 2004) we described the design and un-liberated particles after shredding (or summation of all multi-material particles) as being composed of fixed combinations of all elements present in the product. The degree of liberation was modelled by defining these matrices for different low to high (as well as fully) liberated classes (materials liberation/connection classes). The liberation process was modelled in this work by defining mass balances over the in- and output of the shredding process to predict the output of the shredder as a distribution over the higher liberated and fully liberated pre-defined classes. Due to the complexity of liberation behaviour, shredding matrices were not defined at this stage. In order to improve the modelling of liberation, and incorporate new findings on liberation behaviour from extensive experimental work (Van Schaik et al., 2005), we developed fuzzy models to predict liberation behaviour (Van Schaik and Reuter, 2007) for the recycling of end-of-life passenger vehicles using rules to capture the heuristics of non-random liberation processes, which are linked directly to connection types in the design of the product. The modelling of design and liberation behaviour in these fuzzy models are based on classification of the materials in the input to defined input classes (fixed amounts of materials in a product) and pre-defined liberation classes for the description of binary, ternary, quaternary (i.e. all multi-material) particles individually, hence making a significant step ahead in modelling of liberation. However the definition of fixed material connections and prediction of liberation on this basis makes that these models are not as flexible and general as desired; hence require improvement in order to more accurately and flexibly model design and liberation as a function of design. In this paper we discuss an innovative step in modelling of both design and liberation based on design characteristics, as well as discusses the data collection that underpins this. This will allow the modelling/description of design based on actual material connections and connected masses and the
corresponding modelling of liberation, predicting the mass and composition of non-liberated as well as liberated particles after shredding as a function of design and process characteristics. 2.2.2
Data collection on liberation behaviour
Product design is decisive in the liberation of design connected materials, the composition of non-liberated particles after shredding and the degree to which this liberation takes place. The latter is also affected by shredder intensity and efficiency. This implies that detailed data is required on the composition and connections in products. Such data does not readily exist in industry, although this is and should be available from CAD software as discussed by Van Schaik and Reuter (2007). To characterize and model product design in terms of material composition, material connections, and material ratios in the connected ‘clusters’, data collection on products characteristics have been carried out by the performance of dismantling experiments by the authors. In order to understand and predict the effect of design on liberation and recyclate quality the authors have carried out destruction tests on the different material clusters, to derive (heuristic) liberation rules from this. Figure 4 depicts material clusters of copper, showing these different types of connections/joints and also the liberated/dismantled fractions. The type of data collected by the authors for the depicted recyclates in Figure 4 as well as for other E-waste products is discussed below. These have been gathered through performing dismantling and destruction tests as well as shredding and recycling experiments or collecting data from plants by identifying particle characteristics after shredding (Reuter and Van Schaik, 2008; Van Schaik and Reuter, 2007). Product composition /material analysis. Careful dismantling and separation of the product into its various composing materials provides the required information to set up a material mass balance of the product. The material analysis is used to predict the amount of the different materials in the connected and unconnected particles as well as the mass flows through the recycling system. The analysis includes all materials (physical) as defined in the product and hence in the developed models. This type of analysis is also discussed by Reuter et al. (2005) in which statistical techniques such as data reconciliation were used to balance the recycling system that recycled 1153 cars in a first experiment of this size to calculate recycling rates within the statistical boundaries of sampling etc. This is however the stage where ‘normal’ data collection ends as can also be read from UNU (2007), although this review of the EU Directive on WEEE does not even capture statistics on data and presents incompatible and incomplete data sets. This provides a debatable basis for the evaluation of this directive. Material connections and connection types. Careful and accurate analyses of the connections between the different materials from industrial shredding as well as end-of-life products reveal the following information, data and insights as reflected by Figure 4: • • •
The type of connected materials based on all physical materials included in the model; Connections types for the different material connections, e.g. bolted, shape connection, glue, etc.; and Characteristics of the connection (connected surface, size of connection, etc.).
This information has been transformed into design descriptions and liberation heuristics that form the basis of the various design and shredding modelling matrices that will be discussed in the next section that describes link between design, liberation, recyclate quality (particle composition after shredding) and recycling. 2.2.3
Modelling design characteristics and shredding (liberation and particle composition)
In this section the three basic matrices are defined that (i) describe design in terms of materials and connections and related connected masses as well as link design to (ii) resulting material connections after shredding and (iii) material liberation by the modelling of shredding behaviour using these matrices.
Fig. 4. An example of the material analysis of copper and other materials from a coil by dismantling, different connection/joint types and their liberation after destruction (to simulate shredding of the materials) and dismantling of a Cathode Ray Tube Television (CRT TV). Modelling product design in terms of materials and material connection/liberation classes The modelling of liberation behaviour during shredding requires an adequate description of the product design; this being the input to the recycling system as an end-of-life product or still a conceptual design on the computer aided design (CAD) tool. This description has to capture the information on material composition as well as on connection of materials and the amounts of different materials connected in the different material clusters in the product/component as illustrated in the example of Figure 4. The data collected as discussed above, reveals product characteristics decisive for liberation behaviour and particle composition after shredding. The collected data provides information on the different ‘Clusters’ of materials (=groups of connected materials) in the total design, and the respective masses of the different materials ‘k’ (note that these are not elements) connected in the clusters, such as the mass of copper, plastics, ceramics (or ferrite), electronics etc in the material cluster of the copper coil as depicted in Figure 4. Other material clusters in a CRT TV are the CRT tube, plastic or wooden housing, Printed Wire Board, getter, etc. The clusters can be identified by e.g. components in a product; space separated parts in a product, structural design parts as defined in the design tree of CAD, etc. and can be defined from data collection as described above or from CAD software. In order to define the product/input suitable for modelling of liberation and subsequent processing, each of the individual material clusters has to be identified separately in the product and captured accordingly in the modelling/description of the product design. The product design characteristics and material clusters as derived from and identified by design data (either collected by dismantling or from CAD data) are in this model hence translated into a ‘Design table’ D l ,k , which describes the mass of the individual materials ‘k’ and combinations in the product as derived from the data collection on material composition and material connections in the product/component as described above (or derived from CAD software). Each material cluster is represented in this ‘Design table’ by separate ‘liberation or material connection classes’ for the un-liberated classes l = LC11, LC21 to LCM1, in which ‘M’ represents the number of different material clusters present in the design, roughly equivalent to the major components of a product. Material clusters consisting of one material are defined by the definition of liberated classes, for which l= LL1 to x . The liberation/connection classes ‘l’ contains the information on the masses of the materials ‘k’ connected in the unique combinations of connected physical materials representing a material cluster. A material ‘k’ can be present in different clusters; the mass of the material ‘k’ is hence spread over different clusters and hence allocated and defined accordingly in the ‘Design table’ to ensure closing of mass balances. The mass of the individual materials k as derived from the material analysis of the product and present in different clusters is hence distributed over the different LC11, LC21 to LCM1 classes defined in the ‘Design table’. Each design will be represented by a unique ‘Design table’.
The mass distributed over the different liberation/connection class l = LC11, LC21 to LCM1 can be defined as a distribution of the total product mass or can be normalised in order to generalise the ‘Design table’ and facilitate the modelling of comparable products with a different weight, but similar composition and connection characteristics. A typical representation a ‘Design table’ is illustrated by Table 1. Table 1. Example of a ‘Design table’ D l ,k defining the material composition and connections of a product/component (normalised) derived directly from the product design and its connections as depicted by Figure 4. Liberation/ connection class 'I' LC11 LC21 LC22
Materials 'k' Fe Al 0.000 0.000 0.158 0.000 0.000 0.000
SS 0.000 0.000 0.000
Cu 0.000 0.000 0.000
Cuwire 0.000 0.000 0.000
Plastics 0.008 0.001 0.000
PUR 0.000 0.065 0.000
Wood 0.029 0.000 0.000
Glass 0.000 0.000 0.000
Paper 0.000 0.007 0.000
Hg 0.000 0.000 0.000
CFC 0.000 0.005 0.000
Isobutane 0.000 0.000 0.000
Cyclopentane 0.000 0.000 0.000
Oil 0.000 0.000 0.000
PCB 0.000 0.000 0.000
LC23
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LC24
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LC25 LC31 LC32
0.000 0.045 0.000
0.000 0.045 0.000
0.000 0.000 0.000
0.000 0.002 0.000
0.000 0.000 0.000
0.000 0.103 0.000
0.000 0.065 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.005 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
LC33
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LC34
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LC35 LC41 LC42
0.000 0.088 0.000
0.000 0.002 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.054 0.000
0.000 0.009 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
0.000 0.000 0.000
LC43
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LC44 LC51 LC61 LC62
0.000 0.033 0.220 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.013 0.000
0.000 0.000 0.000 0.000
0.000 0.001 0.001 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000
0.000 0.000 0.006 0.000
0.000 0.000 0.000 0.000
LC63
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LC64
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL1
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL2
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL3
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL4
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL5
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL6
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL7
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL8
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL9
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL10
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL11
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL12
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL13
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.008
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL14
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL15
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
LL16
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Modelling of shredding - prediction of non-liberated material connection classes and liberated material classes during shredding of the Design table From the collected data on design as discussed above and data collected on shredded streams from industrial recycling plants/experiments it can be observed that the liberation behaviour of a product during shredding is characterised by two different key principles: (i)
Liberation of ‘design-defined material clusters’ into non-liberated particles. These particle can either contain the material combinations as initially defined in the design (containing equal or different mass ratios between the various materials ‘k’) and/or can liberate into newly created non-liberated binary, ternary and/or multi-material particles (liberation/connection classes), which are sub-sets of the initial material combinations classes as defined in the ‘Design table’; and
(ii)
Liberation of materials into fully liberated (pure) particles.
The shredding process is hence modelled in this work based on these two principles, by the definition of two different ‘matrices’ (in fact shredding equations) by which the particle compositions and degree of liberation of the output stream of the shredding process can be calculated: (i)
‘Shedder Connection Table’ SC l,k – models the liberation of the individual materials ‘k’ for the different material clusters of the design into similar and newly created non-liberated particles (liberation/connection classes) and hence provides the basis to calculate the masses of the different
materials present in the different material connection/liberation classes (original and new) after shredding. (ii)
l ,l
‘Shredder Liberation Table’ SLid y – models the distribution of the different material clusters of the design ld into liberated and un-liberated particles of the output ly and hence provides the basis to calculate the mass of the liberated materials ‘k’.
Since each material cluster can in theory remain connected during shredding (containing equal or different mass ratios between the various materials ‘k’) and/or liberate into each possible binary, tertiary or multimaterial combination of the initially connected materials and can liberate into their pure components, liberation/connection classes will be created in the model for each of the non-liberated (sub-)classes l = LC11 to a, LC2 1 to b to LCM1 to z, as well as fully liberated materials for which l= LL1 to x. The indices a to z represent the number of connection/liberation sub-classes which can be created from each of the original clusters and are determined by the amount of elements k present in one cluster. For example the connection of k= steel, copper and aluminium can liberate into the non-liberated particles: a= steel-copper-aluminium; b= steel-copper; c= steel-aluminium; and d= copper-aluminium. The number of liberated materials is defined by the number of materials ‘k’ present in the product, hence ‘x’=’k’. In order to facilitate calculation of liberation behaviour during shredding, these shredding-created liberation/connection classes and liberated classes are added to the ‘Design table’. Since these classes are not present before shredding, the masses of the different materials ‘k’ in these classes in the ‘Design table’ are zero (see Table 1). The degree to which the different material connections are liberated, and the new connection classes created are determined by the connection characteristics of the product/component. The re-distribution of the materials k in the non-liberated design connection classes l = LC11, LC21 to LCM1 over the non-liberated design defined and newly created sub-classes l = LC11 to a, LC21 to b to LCM1 to z is defined in the model by the ‘Shredder Connection table’ SC l,k . The degree to which the different materials in the initial connection l ,l y
classes are fully liberated or remain un-liberated is described by a ‘Shredder Liberation table’ SLid material connection class as defined in the ‘Design table’ D
l ,k
for each
(or intermediate recyclate stream entering a
l,k
next liberation process f i ). To guarantee the closure of material mass balances, the sum of the fractions reporting to the original and sub-classes as defined in the ‘Shredder Connection Table’ must be 1. The same applies to the sum of the materials reporting to the non-liberated and liberated classes as defined in the ‘Shredder Liberation Table’. Parameterisation of the ‘Shredder Connection Table’ and ‘Shredder Liberation Table’ The ‘Shredder Connection table’ and ‘Shredder Liberation matrices’ are design-specific – this is a key insight that this paper is offering. The values in these matrices have been estimated from heuristics rules, which are defined based on the ‘functional relationships’ between connection types and characteristics in the design as well as related liberation and connection behaviour during shredding as observed from various analyses of data from recycling plants and destruction tests as discussed above. The fractions of each individual material k present in the initial connection/liberation classes as defined by the ‘Design table’, reporting to the different sub-classes as defined in the ‘Shredder Connection table’ and the degree of liberation as defined by the ‘Shredder Liberation table’ are determined by: •
•
Connection types B=Bolting/riveting A=Adhesive/gluing W=Welding Connection characteristics Point connections (B, A, W, I) - Material of joint - Weight per joint (per bolt/rivet/etc) - Connected surface (length/width) - Number of joints
I=Insertion/shape capture S=Surface (painting/coating) F=Foaming/structural foaming Surface connections (A, W, S, F) - Material of surface connection - Weight of connection material - Surface of connection (length/width)
By detailed dismantling and destruction of the different products and material connections as discussed above, the authors have estimated the liberation behaviour in relation to different connection types and connection characteristics as listed here. It is determined how easily and well the materials can be liberated, which material connections do liberate and which materials will remain connected – hence an general estimate can be made for example how much of a material remains around a bolt or rivet after shredding. The loosely bonded materials (due to e.g. shape connection), which could easily be separated by hand, will accordingly liberate easily during shredding. Materials which have e.g. been glued and can hardly be separated after destruction of the connection will liberate poorly during shredding. General liberation rules can be derived for these materials. The data on liberation behaviour of the various material connections and connection characteristics as observed from and simulated by the detailed and careful dismantling and destruction tests have been used to define heuristics by defining functional relationships between connection types and characteristics and liberation behaviour. This information has been combined with the heuristics on the prediction of shredding of connections as defined by the authors in earlier work (Van Schaik and Reuter, 2007) to predict the degree of liberation and material connections and combinations after shredding or size reduction. Table 2 illustrates some typical connection types as selected for functionality by the designer and their liberation behaviour if they pass through a shredder that breaks post-consumer goods apart. These heuristic rules and functional relationships provide the design dependent values to populate ‘Shredder Connection’ and ‘Shredder Liberation Tables’ in the model. Table 2. Characteristics of connection types related to their specific degree and non-randomness of liberation behaviour after a shredding operation (Van Schaik and Reuter, 2007). Connections types
Before shredding
After shredding
After shredding
Liberation behaviour
Bolting/riveting
• •
Gluing
• •
Coating Painting
/
• •
High liberation High randomness of liberation Medium liberation Medium randomness of liberation Low liberation Low randomness of liberation
Table 3 shows an example of a ‘Shredder Connection table’ SC l,k defining the values for the re-distribution of the materials k in the different material connection classes l over the non-liberated (sub-) classes during shredding. The values in the ‘Shredder Connection Table’ have been estimated based on the heuristics as estimated from the dismantling and destruction tests as discussed above. The values in Table 3 define that e.g. the liberation class l=LC21 consisting of a connection of k=Fe, Plastics, PUR, Paper and CFC (see Table 2) re-distributes to the non-liberated sub-classes according to the following distribution per material k as defined in Table 3: • • • • •
0.9 (=90%)*Fe from LC21 to LC22 and 0.1(=10%)*Fe from LC21 to LC23 0.2*Plastics from LC21to LC23 and 0.8*Plastics from LC21 to LC24 0.7*PUR from LC21to LC22 and 0.1*PUR from LC21 to LC24 and 0.2*PUR from LC21 to LC25 the same distribution applies to CFC and to Isobutane and Cyclopentane (when present) 1*Paper from LC21 to LC24
The values in Table 4 define for this example that the materials in the connection classes LC21-LC25 will fully liberate for 82% (0.82) to the liberated classes LL1 (liberated plastics), LL3 (liberated Fe), LL4 (liberated PUR), LL5 (liberated paper), LL6 ((liberated CFC), LL7 (liberated Isobutane) and LL8 (liberated
Cyclopentane) (if present) and that 18% of the material stays connected in the non-liberated classes LC21LC25 Table 3. Example of a ‘Shredder Connection Table’ SC l,k defining the values for the re-distribution of the materials ‘k’ in the different material connection classes l = LC11 to LCM1 over the non-liberated (sub-) classes during shredding based on the heuristics and approximate analysis of real shredded particles Liberation/ connection class 'I' LC11
Materials 'k' Fe Al 0 0
SS 0
Cu 0
Cuwire 0
Plastics 1
PUR 0
Wood 1
Glass 0
Paper 0
Hg 0
CFC 0
Isobutane Cyclopentane Oil 0 0 0
PCB 0
LC21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LC22
0.9
0
0
0
0
0
0.7
0
0
0
0
0.7
0.7
0.7
0
0
LC23
0.1
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
LC24
0
0
0
0
0
0
0.1
0
0
1
0
0.1
0.1
0.1
0
0
LC25
0
0
0
0
0
0.8
0.2
0
0
0
0
0.2
0.2
0.2
0
0
LC31
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LC32
0.9
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
LC33
0
0
0
0
0
0.85
1
0
1
0
0
1
1
1
0
0
LC34
0.1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
LC35
0
1
0
0
0
0.05
0
0
0
0
0
0
0
0
0
0
LC41
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LC42
0.9
0
0
0
0
0
0.5
0
0
0
0
0.5
0.5
0.5
0
0
LC43
0.1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LC44
0
0
0
0
0
1
0.5
0
0
0
0
0.5
0.5
0.5
0
0
LC51
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
LC61
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LC62
0.9
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
LC63
0
0
0
0.1
0
1
0
0
0
0
0
0
0
0
0
0
LC64
0.1
0
0
0.9
0
0
0
0
0
0
0
0
0
0
0
0
LL1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
LL2
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
LL3
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LL4
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
LL5
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
LL6
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
LL7
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
LL8
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
LL9
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LL10
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
LL11
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
LL12
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
LL13
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
LL14
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
LL15
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
LL16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
Since the degree of liberation is not only determined by the design, but also by the intensity and efficiency of shredding, the ‘Shredder Liberation table’ is both design and process specific, these tables are defined in the models for each different liberation process in the recycling flow sheet of the product. Table 4 gives an example of a ‘Shredder Liberation table’ (we have kept this general to show the principal; however, this table is populated by specific product type derived data as discussed above). It will therefore be obvious that the values in the shredder table will in practice differ for each material type and connection type, as well as connection characteristic. Important to note, a key insight is that shredding is a non-random process and is defined by design as has been discussed in Van Schaik et al. (2005).
l ,l
Table 4. Example of a ‘Shredder Liberation table’ SLid y defining the liberation of the different liberation/connection classes to non-liberated and fully liberated classes (pure materials), which is information derived from the heuristics of non-random liberation of particles as depicted by Table 2. Liberation/connection classes input 'l d'
Liberation/ connection classes output 'l y ' LC11
LC11-1
LC21-5
LC31-5
LC41-4
LC51-1
LC61-4
0.35
0
0
0
0
0
LC21
0
0.18
0
0
0
0
LC22
0
0.18
0
0
0
0
LC23
0
0.18
0
0
0
0
LC24
0
0.18
0
0
0
0
LC25
0
0.18
0
0
0
0
LC31
0
0
0.47
0.96
0
0
LC32
0
0
0.47
0
0
0
LC33
0
0
0.47
0
0
0
LC34
0
0
0.47
0
0
0
LC35
0
0
0.47
0
0
0
LC41
0
0
0
0.04
0
0
LC42
0
0
0
0.04
0
0
LC43
0
0
0
0.04
0
0
LC44
0
0
0
0.04
0
0
LC51
0
0
0
0
0.24
0
LC61
0
0
0
0
0
0.31
LC62
0
0
0
0
0
0.31
LC63
0
0
0
0
0
0.31
LC64
0
0
0
0
0
0.31
LL1
0.65
0.82
0.53
0.96
0.76
0.69
LL2
0.65
0
0
0
0
0
LL3
0
0.82
0.53
0.96
0.76
0.69
LL4
0
0.82
0.53
0.96
0
0
LL5
0
0.82
0
0
0
0
LL6
0
0.82
0.53
0
0
0
LL7
0
0.82
0.53
0.96
0
0
LL8
0
0.82
0.53
0.96
0
0
LL9
0
0
0.53
0.96
0
0
LL10
0
0
0.53
0
0
0
LL11
0
0
0.53
0
0
0.69
LL12
0
0
0.53
0
0
0
LL13
0
0
0.53
0
0
0
LL14
0
0
0
0
0
0.69
LL15
0
0
0
0
0
0
LL16
0
0
0
0
0
0
The mass balance over the shredder process i and hence the calculation/prediction of liberated materials and connected material masses in the non-liberated classes after shredding is described by the following set of equations (Eq. 1) LC11−a ,l y , k
= D LC11 ,k ⋅ SC LC11−a ,k ⋅ SLi
LC 21−b ,l y , k
= D LC 21 ,k ⋅ SC LC 21−b ,k ⋅ SLi
yi
yi
LC11−a ,l y LC 21−b ,l y
... ... to
(Eq.1)
LCM1− z ,l y , k
yi
= D LCM1 ,k ⋅ SC LCM1− z ,k ⋅ SLi
LCM1− z ,l y
where yi y = yi l ,k
LC 11−a ,l y , k
+ yi
LC 21−b ,l y , k
+ .... + yi
LCM1− z ,l y , k
Equation 1 implies that original material classes can be maintained as well as new connection classes are generated during shredding, the ratio between the materials in these connection classes could change, as well as liberated particles will be created. This equation summarizes the unique link that can be created between design and liberation of material after shredding. Since both the definition of the ‘Design Table’ D l ,k and the shredder model tables SC l ,k and
l ,l
SLid y are parameterized as a function of dynamic design characteristics, Equation 1 can capture the dynamics of time-varying design, related shredding performance and hence recyclate quality and recycling behaviour for different WEEE products and components. This first principles link between design and recycling facilitates the performance of Design for Recycling as a function of the dynamic characteristics of design and recycling technology. 2.3
Physical separation (as a function of material connection classes = particle composition)
Physical sorting processes are governed by physics of separation and hence the physical properties of the different materials present in the shredded product and consequently of the (intermediate) recycling streams. The separation efficiency of the individual mono- and multi-material particles is therefore determined by the actual composition of the particle and the ratio of the different materials as these affect their magnetic, density, electric, etc. properties that govern their physical separation. This implies that the separation efficiency needs to be accounted for as a function of the particle composition, rather than assuming that only pure particles are present in the streams as is the case in commonly applied LCA and MFA methodologies and similar bulk flow approaches (see Figure 5). These simpler approaches define separation efficiency only for the different individual materials which does not reflect industrial reality ignoring that recycling streams are a complex combination of materials, which cannot be separated by physical separation and hence drastically affect the quality of the streams. This argument holds even more for the minor toxic elements, which are often present in low concentrations and cannot be ignored for their toxic and harmful nature and obliged control and monitoring as imposed in Annex II of the WEEE directive. These toxic elements, present in different physical materials (e.g. flame retardants plastics) could be ‘dragged’ along with the (unliberated) physical particles to various recyclate streams (e.g. plastic with flame retardant connected to steel ending up in the steel stream and being processed in the steel convertor) where their destination and behaviour needs to be predicted in order to control the dispersion and recovery of toxic elements. The physical materials k that have been included in the model are Al, Cu, Cu wire, Mg, Ferrous, SS (stainless steel), PM (precious metals), Solder, Glass PbO, Glass BaO, Glass, P powder (fluorescent powder), Ceramics, Wood, Plastics (general), PP, PVC, ABS, PUR, Epoxy, CFC’s, Oil, Electronics, Paper, Hg switches, Isobutane, Cyclopentane and PCB’s (PolyChlorinated Biphenyls). The separation efficiencies for the different individual processes and pure materials have been derived from (confidential) plant data and have been calibrated against various literature sources as well as separation physics. The separation efficiencies for the multi-material particles are a function of the recovery factors of the different pure materials present and are calculated in the model for all physical separation processes in Figure 3 for all multi-material particles (material connection classes) according to a weighted average (Eq. 2):
∑R
k i, y
Ril, y =
⋅ Fi l ,k
k
∑F
l ,k
i
k
∑R
k i,x
Ril, x =
⋅ Fi l , k
k
∑F
l ,k
i
k
where Ril, y , Ril, x
recovery factor (separation efficiency) for liberation/connection class l for unit operation i to output streams y, x, etc.
k i, y
k i, x
R ,R
recovery factor of material/element k for unit operation i to stream y, x, etc.
Fi l ,k
input stream to unit operation i
(Eq.2)
in which
Rik, y + Rik, x + Rik, z = 1 and
(Eq.3)
Ril, y + Ril, x + Ril, z = 1 This results in a separation table for each process step in which the separation efficiencies (recovery factors) for each connection/liberation class l are defined distributing the input of the process to each of the output streams. These data are based on first principles and tested by the extensive experience and industrial data sources of the authors. Mass balances are defined for each unit operation i and j. The following equations apply to the physical separation processes for connection/liberation class l and element k between the different unit operations i within the flow sheet of Figure 3 and the structure of the network of processes determined by the structural (or scenario) parameter α, which allows simulation of different recycling flow sheet configurations (Eqs. 4 to 8). yil ,k − Ril,y ⋅ Fil ,k = 0
− R ⋅ Fi
(Eq. 4)
=0
(Eq. 5)
zil ,k − Ril,z ⋅ Fil ,k = 0
(Eq. 6)
xil ,k
l i,x
l ,k
in which n
Fil ,k = fi l ,k + ∑ α ij ⋅ y lj,k
(Eq. 7)
j =1
and n
0 ≤ α ij ≤ 1 and ∑ α ij = 1 (for each j)
(Eq. 8)
i =1
This description of the physical materials k and connection/liberation classes l allow that the composition (and therefore quality) of each of the streams in every stage in the recycling system is calculated in the model as a function of separation efficiency of pure materials for the degree of liberation and particle composition determined by product design and shredding efficiency/intensity.
Fig. 5. Schematic overview of fundamental principles of product design and liberation in relation to physical separation (efficiency is a function of particle composition after shredding determined by product design and material liberation during shredding).
The developed simulation model depicted by Figure 6 captures all these phenomena. For example the separation efficiency as described for example for the “Magnetic Separation” unit operation in Figure 6 (with associated Embedded Matlab Editor for the code underlying the simulation of the magnetic separator) is derived from the separation of pure materials/metals (defined by Eqs. 2 to 8) from various plant data the authors have measured over many years and have been calibrated against various literature sources as well as separation physics. The separation efficiencies for the multi-material particles are a function of the different materials present and have been determined for all physical separation processes in Figure 3 and all classified multi-material particles (material connection/liberation classes l).
Fig. 6. A Simulink dynamic model for the recycling of CRT TV’s, with the “Magnetic Separation” cyan coloured unit operation block highlighted with respect to its underlying separation model written in Matlab® code (shown is the Embedded Matlab Editor).
2.4
Metallurgical, thermal and (non-)organics processing
Whereas un-liberated materials cannot be further separated during physical processing, the separation and recovery in metallurgical and thermal processes into different phases (metal, slag, flue dust, off gas) is determined by thermodynamics as a function of temperature and by the chemical content and interaction between different elements/phases/compounds present in the recyclates as a function of pure and impure particles and their composition.
2.4.1
Chemical description of material streams (‘chemical composition of physical materials’)
The efficiency and recovery of materials in metallurgical and thermal processing is based on the process thermodynamics of these types of processes and on the chemical components building up the physical materials as defined for physical separation. This requires that the physical materials in the model are translated not only into its corresponding chemical elements, but also its phases. The physical quality or composition of recycling (intermediate) streams as indicated in Figures 4 and 5 is directly related to the chemical composition of the metal fraction (alloying composition, contaminations, oxides, etc.), the non-organic fraction and the chemical composition and heat content of the organic fractions (see Figure 7). A ‘Chemical Composition table’ CC k ,c has been defined in the model, which transposes each
physical material into its chemical elements/compounds. The transition of physical to chemical materials is described in the model based on Eq. 9.
∑∑ CC l
k ,c
⋅ yil ,k − yic = 0
(Eq. 9)
k
This crucial information characterizes the effect that design has on recycling efficiency and environmental performance of the system. It also determines primarily the smelting costs charged by the smelter as would be evident from the various material combinations of materials in the dismantling parts of CRT televisions as depicted by Figure 8 the smelter must separate economically into saleable products. The following compounds are included in the model - 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, Ta, SrO, [-C3H6-]n, [C2H3Cl-]n, [-C11H22N2O4-]n, Epoxy and Wood, which are recovered as metals, plastics and energy etc. by the appropriate chemical transformations dictated by thermodynamics within a suitable technology and economic framework.
Fig.7. The relation between physical composition of intermediate recycling streams and the corresponding chemical composition.
Fig. 8. The basic description of materials within a recycling system – the basis for the fundamental description of a recycling system: dismantling groups, their physical material grouping and their respective chemical description for a CRT television.
Figures 7 and 8 detail the fundamental link that is created between the physical description (i.e. the material combinations created by the designer and by physical separation) and the chemical description of post consumer goods in order to bridge the gap between design, physical separation and chemical metal and material recycling and energy recovery. Figure 8 shows that for each material in post-consumer products a corresponding chemical composition has been defined in order to describe and control the final treatment processes and actual recovery and distribution of the chemical elements and phases. The clear distinction between the physical and chemical description of material streams makes the description of the material flows in the model consistent, process specific, as simple as possible, and most important, only describes the toxic elements as free elements in the processes where these materials can be/are recovered, emitted, or captured/disposed and/or can be harmful. An example is the presence of flame retardants in plastics. Only for the high-temperature processing of these plastics, the identification of these flame retardants is important and makes sense. In the stage of physical separation of the plastics by means of density separation, automated sorting etc. it is not relevant to identify the flame retardants. These elements will remain bonded to the plastics. Figure 8 provides some insight into the complexity of consumer products and hence suggests the detailed processing that would be required to recover all of these materials so that they do not end as waste. In the end models have to be able to deal with this complexity to estimate where major elements go but more importantly what happens to minor and/or toxic elements. Hence any predictive model for minor elements should incorporate this knowledge to be any use to estimate depletion, toxicity, recycling and recovery rates etc.
2.4.2
The thermodynamics of recycling systems
The simulation models link design to the creation of (impure) recyclates, which when smelted, require virgin material to dilute impurities in for example metals to produce once again suitable alloys for use in metallurgical reactors. Figure 9 depicts an example of the metallurgical processing and recovery of materials and the quality (composition) of the various created output streams of the recycling system e.g. produced metal, valuable metal oxides of Sn, Pb and Zn, and energy as well as slag (benign building material originating from a molten mixture of oxides such as SiO2, CaO, Al2O3, MgO, FeOx, etc.). This figure represents the level of detail, which is to be captured by any type of model predicting material flows of product systems. Mass balances for the metallurgical and thermal processes are defined in the model based on recovery factors for each of these type of processes derived from process thermodynamics and extensive metallurgical industrial experience of the authors and their process/furnace models (Reuter and Van Schaik, 2008).
Fic − yic − y cprimary = 0
(Eq. 10)
yic − Fi c ⋅ Ric,y = 0 (to metal)
(Eq. 11)
x − Fi ⋅ R = 0 (to slag)
(Eq. 12)
z − Fi c ⋅ Ric,z = 0 (to dust) for which Ric,y + Ric,x + Ric,z = 1
(Eq. 13)
c i c i
c
c i ,x
(Eq. 14)
The relationship between in- and outputs of metallurgical reactors can be expressed in thermodynamics, the irreversible losses in terms of Exergy, which is also the measure of the increase in entropy of complex (ever smaller) shredded particles lost from the system due to them not being recoverable due to their designed material combinations. These aspects have been addressed by the authors through the quantification of the performance of recycling systems in terms of Exergy (Reuter and Van Schaik, 2008; Ignatenko et al., 2007,) building on work by Szargut (2005) and Ayres (1998).
Fig. 9. Schematic overview of smelting technology to smelt E-waste (efficiency determined by thermodynamics and chemistry and the chemical recyclate composition) with a feed of different recyclates containing a different mix of elements.
2.5
Dynamics of the recycling system
The above theory has been embedded into time dependent simulation models to predict and optimize product recycling systems; all as a function of the recycling system architecture (arrangement and combination of recycling and final treatment processes) and product design. Figure 10 shows an example of a dynamic model for the recycling of fridges/cooling equipment developed under the dynamic simulation platform of Simulink/Matlab® (Mathworks, 2007). Due to its fundamental nature, these models can be applied for the wide range of E-waste products as well as the dismantled and/or sorted components originating from these. Typically each block in the Simulink environment (Figures 3, 6 and 10) is defined in the Laplace (S) domain, either as a zero order process or a first order transfer processes (Matlab®, Simulink®). Addition blocks as well as gain blocks ensure that the mass balances are closed. Embedded Matlab code (Figure 6) does also perform additional calculations such as the separation efficiencies (Eq. 2-3), and transformations such as defined by Eq. 9.
Key to the simulation/optimisation models is that they produce closed mass balances for each liberated and un-liberated mineral (physical material) and (chemical) element as a function of design as the basis for 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 such as the more simplified approaches such as LCA and MFA rely on. Various figures in this paper and as required by the EU legislation for material and energy recovery for post-consumer goods, cars etc. The more detailed approach as applied here, from which the actual recovery of individual materials and energy can be calculated as required by EU, 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 a crucial for monitoring and quantifying progress in time.
Fig. 10. Example of a dynamic simulation model for fridge recycling, highlighting the relative % change (relative to 100%) in number of fridges being recycled with time and the effect on the aluminium recyclate quality over time.
3.
Application of the E-waste recycling models
The theoretical aspects discussed above highlight the importance of capturing the degree of liberation, recyclate and output quality and the effect on physical, metallurgical and thermal separation performance when assessing resource cycles, performing Design for Recycling (DfR) and monitoring the performance and toxicity of recycling systems from WEEE goods. In the next examples we will show the versatility and the type of results that can be achieved with this approach. The authors’ models can be applied as a fundamental technological framework for the prediction of mass flows, recyclate and output quality (inclusive of toxicity) as well recycling/recovery rates. The examples illustrate that the simulation models presented here are seminal; corroborating what has recently been tested by expensive test work in this regard (Chancerel et al. 2008). This underlines the validity of the approach as well as its predictive nature.
3.1
Prediction of mass flows, recyclate and output quality
Figure 11 gives examples of the quality and toxicity calculations as captured by the model for both recyclate and output streams. These quality predictions provide the basis for (i) evaluation of the marketability/economic value of the produced recyclates from dismantling and physical separation (e.g. smelter-charge for the streams that report to smelters and revenues in further processing (e.g. copper metallurgy)), (ii) a scientifically based estimate of the toxicity of each recyclate stream (based on the combination of materials in each particle and (conceptual) design) (iii) assessment of applicability of the slag and/or requirements for further treatment and (iv) assessment of toxicity of the output streams (e.g. leaching behavior and/or land fill costs).
Fig. 11. Copper recyclate material quality, chemical compound concentrations as well as outputs from a copper smelter (all in wt%). All these pie charts have a related economic value as they represent a scrap and product quality that is traded in the market (not all data given in right-hand pie charts due to confidentiality).
3.2
Prediction the impact of different operating modes of technology on the total recycling of minor and some commodity metals
Since all materials and streams are described separately in the model, the recovery of all elements and therefore the achieved recycling/recovery rate can be predicted (see Figure 12). This example illustrates the true predictive nature of the dynamic simulation models. Calibrated with plant data from various sources, this simulation shows what the effect is of no-shredding, medium and high shredding on the recovery of valuable minor metals as well as commodity metals from Printed Wire Boards (PWBs). Figure 12 shows that the maximum precious metals, copper and tin will be recovered if the PWBs are directly smelted in a copper smelter; however the recovery of steel and aluminium is then low as these report to the slag, a building material of low value. Whereas shredding has more positive effects for aluminium and steel recovery than for other valuable elements, it is clear that most material when shredded does end in energy recovery; the consequence being that most metals that are connected to the organic material are also then lost if not liberated. Note that this type of result cannot be predicted by MFA type models favoured by the Industrial Ecology field (Graedel and Allenby, 2003) as these are not rigorous enough to provide this depth of information and/or to predict the quality of recyclates (Figure 12). The discussed modelling approach can predict the leakage for minor elements from the system for different plant configurations (including dismantling) and shredder settings and hence suggest mitigating actions for example in the design or the processing to minimize losses and is hence essential if sustainability measurements are used to influence the nonrenewable flow of material. Obviously a market related economic value of the recyclates, the basic measure that does determine whether a recyclate can be recycled or not, is a key to determining losses from the system. These predictions can be applied to discuss or corroborate the work of Guo et al. (2008) reviewing the recycling options for non-ferrous metals from Printed Wire Boards.
Fig. 12. Metal recycling rates predicted by the recycling model for different metals for the recycling of disassembled Printed Wire Boards (PWBs) either being directly fed to a copper smelter or shredded with varying intensity.
3.3
Prediction of recyclability and energy recovery as a function of (distributed and timechanging) input of recycling systems
This example illustrates the application of the developed CRT TV model to predict the recycling of a typical CRT TV feed mix with standard deviations on the data (the data may also be distributed or change with time). Figure 13 shows that typically metal recovery is high; however, not all metal qualities are high nor is the energy recovered from the plastic fractions. A large portion of the Epoxy (from the PWBs) is recovered as energy because its purity is too low for further recycling, while the other organic materials are spread through the system due to poor liberation, hence representing a large loss of energy from the system into landfill and/or fractions that do not utilize this energy potential.
Fig. 13. The effect of a typical feed on metal recycling rate, energy recovery and product quality. The purity (yellow bar) of each of the recyclates determines their respective economic values as a function of valuable and deleterious minor elements.
4.
Conclusions
In this paper we have uniquely linked product design and liberation modelling. This is done by defining a Design table, which captures product design characteristics in terms of material masses and material connections. These design properties are decisive for the liberation behaviour of products during shredding. This is combined with the definition of a Shredder Connection Table and a Shredder Liberation Table, which allow novel design-driven and process specific heuristic modelling of liberation in order to predict the degree of liberation and particle creation and composition after shredding. The work is supported by extensive data collection by the authors on E-waste products to define and calibrate the developed heuristics and design tables. This provides the detailed material classes and particle compositions that can be used to simulate the performance of recycling systems for WEEE and other end-of-life goods. All this has been included in a dynamic modelling approach. The models predict the recycling performance for different E-waste systems, recovery of (precious) materials and leakage for minor elements for different (changing) plant configurations (including dismantling), shredder settings, as well changing designs, hence predicting recycling trends in the future. The modelling of the link between design, liberation behaviour and hence design determined recyclate quality is key to evaluate the recycling performance, material flows and toxicity of (conceptual) product designs. This approach has made it possible to link dynamic recycling prediction models to Computer Aided Design software. This allows the design engineer to perform Design for Recycling supported by the a-priori estimation of the recycling behaviour of different concept designs.
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