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60 Substance flow analysis of the recycling of small WEEE determine and express the uncertainty of any kind of measurements. Without the specification of the uncertainty, measurement results cannot be compared with each other or with reference values (DIN V ENV 13005). According to this guide, the uncertainty of a measurement results consists of several components, which can be classified into two types according to the method used to quantify them: A. Components calculated with statistical methods B. Components determined in another way. The standard deviation (square root of the variance) is calculated to quantify the components of type A. For components of type B, a variable is determined to approximate the standard deviation, the existence of which is assumed. In this thesis, the standard deviation of every component used to calculate a mass flow (mass, metal concentration, transfer coefficient) was either calculated or estimated. If the standard deviation could not be calculated, the radius (half the width) of the 95% confidence interval of the variable was estimated. Based on the assumption that the distribution of the uncertainty was normal, the standard deviation of components was assumed to be half of the radius of the 95% confidence interval. The variation coefficient, which is the standard deviation divided by the value of the variable, can also express the uncertainty. The uncertainties of the components were combined according to Gauss’s law of error propagation by summing the square of the standard deviations of the components (DIN V ENV 13005). The resulting uncertainty is also expressed as a standard deviation. 3.4. Data compilation The compilation of the data gathered through the methods presented in 3.3 requires the following steps: 1. A qualitative description of the system, defining the collection, treatment and reuse processes and modelling the material and substance flows from one process to the next one; 2. The integration of the quantitative data on the flows of sWEEE into the system model; 3. Data reconciliation to calculate observable unknown quantities and to eliminate discrepancy of redundant information (Reuter et al. 2005); 4. Calculation of the flows of precious metals based on the concentration of precious metals in the material flows. These four steps were realised by the STAN freeware (TU Vienna 2008), which helps to perform material flow analysis (Cencic & Rechberger 2008). After drawing the processes and material flows in the graphical interface of STAN, the data on the flows with their
3. Method 61 uncertainties were entered into the model. Data reconciliation was necessary to calculate the unknown flows from the available data and to eliminate discrepancy of redundant information (Reuter et al. 2005), i.e. to observe the law of conservation of mass (sum of the inputs equal to the sum of the outputs if the stocks remain constant). According to Reuter et al. (2005), data reconciliation aims at minimizing a sum of errors (difference between each measurement and its reconciled value) weighted by the measurement error, subject to a number of constraints (the mass balance equations). STAN uses Gauss’s method of least squares for data reconciliation (Brunner & Rechberger 2004). Based on the reconciled flows of sWEEE and on a concentration of gold and palladium in sWEEE that was assumed to be constant, STAN calculated the flows of precious metals associated with sWEEE. The resulting flows of gold and palladium are displayed as Sankey diagrams. 3.5. Selection and calculation of indicators “Indicators perform many functions. They can lead to better decisions and more effective actions by simplifying, clarifying and making aggregated information available to policy makers. They can help incorporate physical and social science knowledge into decisionmaking, and they can help measure and calibrate progress toward sustainable development goals. They can provide an early warning to prevent economic, social and environmental setbacks. They are also useful tools to communicate ideas, thoughts and values.” (United Nations 2007) The selection of indicators must fulfil scientific, practical and user-related requirements, such as adequacy to the social, economic and environmental context, reproducibility, traceability, transparency, comprehensibility and availability of the data needed to calculate the indicator (Abdul Rida 2008). In this thesis, the goal of the indicators is to synthesize the quantitative data on materials and substances that are collected, recovered, reused or discarded by the WEEE management system. Different ways to calculate collection rates were compared by Wen et al. (2009). The indicators selected to assess the results of the SFA are presented in Table 10. Table 10 Indicators selected to assess the flows of sWEEE and of precious metals quantified by the SFA and formula to calculate them Subsystem Indicator Formula Collection Collection rate Reuse Reuse rate M M M M C G RU G
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ITU-Schriftenreihe, 2010 Institut f
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Bibliografische Information Der Deu
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Acknowledgements Here I would like
Page 9 and 10: Kurzfassung Die Nachfrage nach Edel
Page 11 and 12: Composition of the doctoral thesis
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Page 17 and 18: Table 26 Quantity of sWEEE (includi
Page 19 and 20: List of abbreviations Ag Au BAT CE
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Page 51 and 52: 3. Method 51 Table 8 Groups of equi
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Page 69 and 70: 4. Data inventory 69 average weight
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Page 77 and 78: 4. Data inventory 77 Table 26 Quant
Page 79 and 80: 4. Data inventory 79 The variation
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7. Recommendations 111 WEEE contai
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7. Recommendations 113 After collec
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7. Recommendations 115 in the case
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7. Recommendations 117 In many case
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7. Recommendations 119 manufacturer
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7. Recommendations 121 the manufact
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7. Recommendations 123 Reuter et al
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8. Conclusion 125 All the equipment
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References 127 Binder 2007 BITKOM 2
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References 129 CIWMB 2004b Clift &
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References 131 Greenpeace 2005 Gree
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References 133 Janz & Bilitewski 20
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References 135 Melissen 2006 Mesker
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References 137 Rhein et al. 2008 Rh
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References 139 StEP 2009 Stevels 20
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References 141 Veldhuizen & Sippel
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References 143 Legislative texts Ba
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Appendices 145 Appendix 1 Equipment
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Appendices 147 Appendix 2 Average c
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Appendices 149 Appendix 4 Past and
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Appendices 151 Eq. group Equipment
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Appendices 153 2 Assumptions based
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Appendices 159 Appendix 8 Flows of
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Appendices 161 Appendix 10 Flows of
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