Simulation-based design for resource efficiency of metal production and recycling systems: Cases - copper production and recycling, e-waste (LED lamps) and nickel pig iron Markus A. Reuter, Antoinette van Schaik & Johannes Gediga
The International Journal of Life Cycle Assessment ISSN 0948-3349 Volume 20 Number 5 Int J Life Cycle Assess (2015) 20:671-693 DOI 10.1007/s11367-015-0860-4
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Author's personal copy Int J Life Cycle Assess (2015) 20:671–693 DOI 10.1007/s11367-015-0860-4
LCI METHODOLOGY AND DATABASES
Simulation-based design for resource efficiency of metal production and recycling systems: Cases - copper production and recycling, e-waste (LED lamps) and nickel pig iron Markus A. Reuter & Antoinette van Schaik & Johannes Gediga
Received: 22 October 2014 / Accepted: 3 February 2015 / Published online: 21 February 2015 # Springer-Verlag Berlin Heidelberg 2015
Abstract Purpose This paper illustrates how a product-centric approach to recycling, building on the extensive expertise, knowhow and tools of the mineral-centric classical minerals and metallurgical processing, should be core to Design for Resource Efficiency (DfRE). Methods Process simulation (HSC Sim 1974-2014, Outotec's design tool) and environmental software (GaBi 2014) are applied to quantify resource efficiency (RE) in a rigorous manner. These digitalisation tools are linked and will be used to show how the environmental performance of copper primary production, the processing of residues and the recycling of ewaste, e.g. light emitting diode (LED) lamps as well as the production of nickel pig iron can be evaluated. The paper also shows how technologies can be compared relative to a precise thermodynamic and techno-economic baseline. Results The results include simulation-based environmental indicators, exergy, recycling and recovery rates, as well as the qualities and quantities of the recyclates, losses and emissions of materials during production recycling. The complete
Responsible editor: Martin Baitz M. A. Reuter (*) Outotec Oyj, Puolikkotie 10, 02230, Espoo, Finland e-mail:
[email protected] M. A. Reuter Aalto University, Helsinki, Finland A. van Schaik MARAS BV, Rijsbes 46, 2498 AS The Hague, The Netherlands J. Gediga PE International AG, 70771 Leinfelden-Echterdingen, Germany
mass and energy balance simulation provides the mineralogical detail of all streams (both mineral and recyclate as well as offgas and dust) to define and improve environmental assessment, while at the same time revealing the aspects of LCA databases and their results that require improvement. Furthermore, this paper presents an approach for industry to implement life-cycle methods in practice. It shows that the DfRE is all about predicting stream grades and thus is equivalent to Design for Recyclate grade and quality (as this determines whether a recyclate or product stream has economic value and can be treated or processed further). DfRE also reveals especially the grade, composition, minerals etc. of the leakage streams, i.e. diffuse emissions, thus permitting a more precise evaluation of environmental impact. Conclusions The prediction of recyclate and stream compositions and grade makes the environmental analysis of systems more precise and will help to expand the detail that defines these flows on environmental databases. This is especially valuable for DfR, where the methodological rigour suggested in this paper is a very necessary addition and requirement for estimating the true environmental impact of product redesigns and the resource efficiency of processing technology and complete recycling systems. The methodology produces massand energy-consistent, economically viable best available technique (BAT) process blocks, the inclusion of which on environmental databases will be invaluable in benchmarking technology and systems in terms of estimating the achievable resource efficiency baseline. Keywords Copper production and scrap recycling . Design for Resource Efficiency . E-waste and WEEE . Greenprinting . LED lamp recycling . Nickel pig iron (NPI) production . Process metallurgy . Product-centric Design for Recycling (DfR) . System design . Ecodesign . LCA
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1 Introduction Ubiquitous metals and the functional materials derived from them play a pivotal role in imparting the special functionality we have become accustomed to in the products we manufacture and use daily. It is no surprise that the Europe 2020 strategy declares ‘Natural resources underpin the functioning of the European and global economy and our quality of life’, with the key objectives to achieve a resource efficiency (RE)-conscious, resilient society that champions a circular economy (EC 2013). The recycling of bulk waste is relatively easy and suits simplified circular economy discussions. In contrast, the challenge lies in the recycling of modern products, and the complex-designed ‘minerals’ characterised by the numerous specialty materials (metals, alloys, plastics etc.) at their functional core. Product-centric recycling (Reuter and Van Schaik 2012, 2015) provides the platform for optimising the recovery of materials and energy from these designer ‘minerals’ in System Integrated Metallurgical Processing (SIMP). In the United Nations Environmental Programme (UNEP) report on metal recycling (UNEP Reuter et al. 2013), it is shown that resource-efficient recycling requires a robust, interconnected hightech metallurgical infrastructure as a crucial enabler of the EU2020 vision. Companies such as Outotec (www.outotec. com), the third most sustainable corporation globally (http:// www.corporateknights.com 2014), have a long history of supplying such hightech mineral-centric metallurgical processing technology. For example, around 50 % of the world's copper is produced via Outotec technology, of which close to 70 % in China (the largest copper producer). Thus, platforms do exist for the recovery of metals from geological and designer minerals. Physics-based environmental policy must provide the wise foundation that cements these systems (i.e. internet-of-things) as the key pillars in an RE-aware society. The true challenge of RE is to link all the actors and stakeholders depicted in Fig. 1, through digitalisation techniques (e.g. simulation tools, process control, suitable databases that make data available to software and information technology (IT) tools, modelling, design, exergy, thermodynamics etc.), to pinpoint the innovations that will deliver the systemic performance that maximises RE. This will enable the rigorous estimation of the recovery and particularly of all the losses of materials from the primary processing of minerals, Waste Electric and Electronic (WEEE), E-waste or the End-of-Life (EoL) recycling system, plastics, the losses that diminish RE. This systemic simulation is fundamental to minimising the footprint of the system, thus enabling it by maximising its handprint (i.e. positive action through detailed analysis (Biemer et al. 2013)). The fundamental role of metallurgy as the basis of ‘closing’ the loop is clear from Fig. 1, depicting the true meaning of sustainable metallurgy. Metallurgical reactors are the true closers of the material cycle, and therefore
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understanding process metallurgy well, innovating its potential through deep understanding of the physics, thermodynamics and transport phenomena of unique new designer ‘minerals’ is the true challenge of RE, Design for Recycling (DfR) and Design for Resource Efficiency (DfRE). The EU2020 objectives will be steered towards economic reality through the wise support of the interconnection of SIMP with Original Equipment Manufacturers (OEMs), economics and thermodynamics- and economics-based policy. This paper will show how product design mineralogy can be linked to metallurgical processing to simulate the recycling system. In this way, the RE of the system can be quantified, its base operating line determined, from which improvements can be measured, optimised and certified. The simulation tools implicitly show what the mineralogical data format of the databases that should be in place to map the economic potential of the urban mine. Therefore, maximising the handprint and minimising the footprint in Fig. 1 through precise system and plant design defines DfRE. This produces the economic, environmental and other key performance indicators supplied by life-cycle assessment (LCA) tools. This greenprint of a designed system is the equivalent to the RE-optimised blueprint of the complete system. Classical process design and engineering is at the core of DfRE of products, plants and systems: the fundamental message of this paper (Reuter 1998; Ayres and Ayres 2002; Worrel and Reuter 2014). This paper will show that each of the simulation models denoted by Fig. 1a–c (in the simulation model boxes) can be linked together to capture the losses from the system and hence maximise resource efficiency within the limits of techno-economic constraints. This has already been discussed by Reuter (1998, 2011); the system-integrating simulation tools have now been created to facilitate this with the principle objective to de-silo the system and analyse and derive the maximum resource efficiency possible. This process of maximising the handprint and minimising the footprint will be simply called ‘greenprinting’ of the system. Entering the discussion from a life-cycle perspective, LCA and footprinting methods have been used for decades in various forms. However, the challenge that the LCA community is currently facing is that industry is not aware of the methodology, its assumptions and the possibilities it offers to generate changes (Rebitzer and Schäfer 2009). LCA is still far from being standard practice. Even though many organisations have implemented life cycle thinking, the application of LCA is often limited to singular efforts (Hunkeler and Rebitzer 2005). Klöpffer (2003) argued back in 2003 that ultimately what is needed are simple-to-use methods giving reliable results that are relevant for businesses and that could operationalise the principles of sustainability—instead of making the method diverge from the priorities of business decision-makers due to over-complexity. It is this gap, which this paper addresses, by providing a systematic approach,
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GEOLOGICAL MINE
URBAN MINE
Geological Minerals
Designer ”Minerals” and Functional Materials Market & Stocks
Collection & Dismantling Unaccounted
(A)
Losses & Theft
Physical
Losses (e.g. Poor collection)
Losses (e.g. complex mineralogy, Technology & system, economics)
Separation
(B)
Product Design (functional materials) Particle Properties Controls
Losses
Design for Resource Efficiency
(e.g. physics, complex linkages)
Multi-material
Stocks & Losses (e.g. Infrastructure, Sampling)
Recyclate Grades
Complex Linkages/Connections
Metal & Energy Recovery (Pyro- & hydrometallurgy, Refining)
Losses & Stocks
(C)
Thermodynamics Controls
(c)MAR
(e.g. Poorsampling, data integrity i.e. resolution / detail / statistics / ”mineralogy”)
Losses (e.g. Thermodynamics, system, technology, economics, metal price, feed morphology/analysis/complexity, dilution of alloying metalsetc.)
Fig. 1 Overview of the various stakeholders affecting RE, which have to be addressed in DfRE if it reaches the depth required to define the baseline and innovate the system architecture, technology and policy on a rigorous techno-economic basis (Biemer et al. 2013; Reuter and Van Schaik 2012, 2015)
reliable data gathering and a results format relevant for the metals and mining sector to ease the implementation of LCA in their decision-making. In summary, the true innovation of this paper is the comprehensive methodological description digitalisation of the whole system (i.e. the Internet of technologies/processes) using simulation tools from the primary minerals and metallurgical processing industry to capture the destination of all materials as a function of techno-economically precise design of the system as well as a detailed mineralogical granularity of recyclate data to match that from the minerals processing industry. Therefore, the target should be quantified sustainability through simulation-based DfRE (Reuter 2011), which produces a ‘greenprint’ for a resource-efficiently designed system. The paper suggests a rigorous methodology, implemented using the shown simulation tools, to greenprint systems with a rigorous set of LCA methodology based sustainability indicators.
2 Design for Resource Efficiency—greenprinting of system The objective of the societal challenge ‘Climate action, environment, resource efficiency and raw materials’ of the
Horizon 2020 Work Programme 2014–2015 (EC 2013) is ‘To achieve a resource- and water-efficient and climate change resilient economy and society, the protection and sustainable management of natural resources and ecosystems, and a sustainable supply and use of raw materials, in order to meet the needs of a growing global population within the sustainable limits of the planet's natural resources and eco-systems’. It is stated that A smart economy minimizes the production of waste and reuses waste as a resource and that ‘Resource constraints and environmental pressures will accelerate the transformation from a linear extraction-use-throwaway model of production and consumption to a circular one’. The need for the decoupling of economic growth from resource use has been clearly identified. One of the areas for investigation and improvement in the H2020 focuses on waste, i.e. resources, recycling, reuse and recovery of raw materials by addressing the whole production and consumption cycle, from waste prevention and the design of processes and products for recyclability to reuse and waste management. Figure 1 shows the various factors and actors/stakeholders that can affect the RE of metal processing and recycling. The interaction therefore of the primary and secondary recovery of metals drives not only the sustainable recovery of elements from minerals but also provides the recycling system
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architecture that recovers metals from complex products and therefore enables the maximum recovery of all elements from designer minerals. It is self-evident that ‘classical’ minerals processing and metallurgy play a key role in maximising RE and ensuring that metals are true enablers of sustainability. The theory and techniques of process metallurgy have been applied in various ways to quantify RE, which will be shown in this paper. The quantification of the flow of geological minerals through a complete metallurgical processing plant by means of simulation (Fig. 2) has evolved considerably over the years (Reuter 1998, 2011; Fagan et al. 2010). However, the quantification and simulation of the path of designer minerals (Fig. 2) through a complete recycling system has been simplified, sometimes to the extent that a reasonable analysis of RE can no longer be made. Figure 2 suggests that the reason for this is simply that recyclates are often so much more complex than geological minerals. This paper will show that a lot of development has happened in recent years towards the better understanding of the destination of all elements, materials, compounds etc. from designed copper minerals in products following a product centric approach (Reuter and Van Schaik 2012, 2015; Van Schaik and Reuter 2010, 2014). This enables a more accurate estimation of environmental impact and hotspots that demand innovation. Reuter and Van Schaik (2012) coined the word product centric to highlight the equivalence of a product ‘mineralogy’ to that of a geological mineral as shown by Fig. 2. Moreover, RE also covers the efficiency of monetary resources. This is a vital prerequisite for any industrial actor and
can be addressed through life cycle costing (LCC) (see, e.g. Klöpffer 2008; Swarr et al. 2011; Pagan 2009). LCC is widely accepted, especially among OEMs, as a way to provide reliability measures to customers (i.e. total cost of ownership). In the metals and mining sector, the question of economic efficiency is becoming increasingly important, as ore grades are declining and the production processes are becoming costlier, especially in terms of energy, chemicals and water costs or land use. However, LCC is seldom truly linked with the same system boundaries and same background assumptions as LCA, when and if such a method is used within the organisation. Setting up this common platform for life cycle based methods, and allowing the life cycle costing method to become a natural part of modelling, will make the approach so much more attractive for companies to implement. In this paper although LCC is not the focus, it is referred to where applicable.
Fig. 2 Simulation-based design and optimisation of the metallurgical processing of complex primary minerals (left) has evolved considerably over the years. Applying these techniques by linking product design to metallurgy to the more complex ‘designer’ minerals such as light emitting diodes (LEDs; right) is the true challenge of DfR and DfRE, de-siloing
the industry (Reuter and Van Schaik 2012, 2015; Van Schaik and Reuter 2010, 2014). The equivalence of geological and product mineralogy permits the harmonisation of simulation throughout Fig. 1—called product centric recycling
2.1 Fundamental limits of systems The principal aim of RE is to operate processes and especially systems to their fundamental limits, opportunity and innovation as depicted by Fig. 3. If the baseline for the system is known, innovation can be driven, and also RE can be improved as the position of systems relative to an absolute baseline is then known. This implies that one can then direct research to those areas which bring true innovation. Figure 3 thus depicts the ambition of the metallurgical industry to get closer to techno-economically feasible RE by quantifying all losses and therefore understanding better
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Fig. 3 DfRE establishes the limits of the system and driving innovation through systemsintegrated de-siloed metallurgical, material and product processing as depicted in Fig. 1—System Greenprint (Reuter and Van Schaik 2015)
which aspects have the most impact on the system transition to the ultimate operating point for the industry. Also, any possible innovation may lower the ultimate achievable operating point, which will increase RE even further. In this paper, we will discuss how a rigorous process tool (used in the daily business of technology suppliers) can be
used to evaluate typical metallurgical systems (e.g. copper smelting plants as shown in Fig. 4), and then use the result to analyse the environmental impact also, via for example GaBi software (GaBi 2013), which has been linked to HSC Sim, a process simulation tool (Outotec 2014), as shown in Fig. 5.
Fig. 4 A primary copper flow sheet for the flash smelting of copper concentrate depicting various technologies including flash converting. Note that under each icon, once again there is a complete flow sheet as
evidenced by the tabs at the bottom of the design pane, e.g. Dryer, Flash Smelting Furnace (FSF), Flash Converting Furnace (FCF), gasline etc.
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Fig. 5 The linking of the HSC Sim simulation and design tool (www.outotec.com) and PE International's (www.pe-international.com) GaBi software enables detailed environmental analysis and innovation (Reuter 1998; Worrel and Reuter 2014)
2.2 Methodology for quantifying the RE of a reactor, plant or system Figure 5 summarises the methodology. Precise simulation supplies data to environmental software, which carries out an environmental assessment. LCA software, as shown on the right in Fig. 5, generally relies on average process data for the various metals to assess their impact when used in product design and applications. This link makes it possible to develop the impact for a particular application.
2.3.2 Mapping After creating a simulation model as shown in Fig. 5, all streams can be collected and mapped as depicted by Fig. 6, following the various tasks as shown on the menu tab. The structure and data nametags are duplicated in HSC Sim as shown. The mapping also indicates if streams flow to the environment or the ‘technosphere’, for example. This selection also clearly has an effect on the final environmental impact—thus careful selection is required by knowledgeable individuals. Due to the large variety of stream types in a detailed flowsheet, this mapping has to be done as the environmental databases lack significant data as well as do not have the same names.
2.3 Steps of DfRE and thus ‘greenprinting’ of a system 2.3.3 Exporting of mapped data to environmental software The steps that are followed during DfRE and thus ‘greenprinting’ a system are discussed in this section.
2.3.1 Simulation Figures 4 and 5 form the basis of all the data and simulations that are used in the discussion of this section. These figures show a typical flow sheet for a flash smelting furnace and flash converter as well as sulphuric acid and metallurgical refining plants. This requires deep knowledge of processes but will as a result produce consistent and good baseline data.
The software mapping tool (Fig. 6) subsequently makes it possible to create GaBi processes from the HSC Sim process simulation models for complete plants or reactors. These processes can now be used to create any GaBi process that can be imported into GaBi and used for environmental analysis. Figures 6 and 7 depict some of the software steps using the LCA functionality within HSC Sim, including the following: &
The reference product is selected, which is the flow around which all other streams are normalised—in this case blister copper is the pivot. This creates a normalised
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Fig. 6 HSC Sim 8's mapping tool, which maps the simulator process flows to the GaBi database most equivalent data. On the left are the HSC Sim flows, and on the right the variables mapped to the GaBi database (numbers in 3rd column left are partially blanked due to confidentiality).
The right half of the figure reflects some of the GaBi database structure and search functionality that permits the linkage of HSC Sim data with the GaBi equivalents. (Note that the GaBi database is not part of HSC Sim)
data set in which all variables with the same GaBi database name have been collected (Fig. 7). This process shown by Fig. 4 is now exported to GaBi and can be used for example as a Best Available Technique (BAT) copper production process, with a fully consistent mass and energy balance. This is of interest, when for example Original Equipment Manufacturers (OEMs) are considering the selection of the environmentally cleanest possible materials for their products in environmental evaluations. This GaBi-compatible ‘process’ can now be used to create a plan (Fig. 8), which can then be used to make an environmental impact assessment (EIA), which includes for example the indicators shown and also endpoint indicators such as ReCiPe.
vary significantly around the average. It can, therefore, be very useful to evaluate the impact of specific processes at specific locations to assess their real impact as well as revealing the true potential for process improvement. This link provides some obvious and very useful benefits, which include:
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2.4 What innovation does this approach stimulate? As there are variations in any process flow sheet regarding their application of Best Available Techniques (BAT) in addition to processes being situated in different locations globally, the environmental impacts due to changing energy mixes, resource impacts, and transport, etc. for a specific plant can
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Maximising the strengths of simulation and environmental software platforms and their respective databases (thermochemical and environmental respectively) to evaluate systems. HSC Sim has databases of approximately 28,000 elements and compounds in solid, liquid, gaseous, aqueous and condensed phases as well as in excess of 13,300 geological minerals. It is self-evident that this level of detail is present in each liberated and unliberated material flow, in addition to molten and aqueous. This provides an extremely powerful basis to further bolster the depth of environmental databases that in many cases lack the detail of industrial flows in metallurgical and recycling systems. At the same time, this link will also reveal weaknesses, which would in turn provide the true points that have to be improved in the system to ensure that resource efficiency can be realised to its full techno-economic potential. Thus,
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Fig. 7 The normalised data of all the flows derived from Fig. 4 (from all levels of the many sheets of the complete model) and that are exported to GaBi (or Excel) so that they can be imported into GaBi as a process (bottom). (Numbers blanked due to confidentiality)
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it enables rigorous hotspot analysis while at the same time providing engineering-based analysis, so crucial to innovation. All streams are mapped in detail, including all compositional data, not only as elements but also as compounds as a function of a unique concentrate feed suite (e.g. LCA databases detail only a few slag types, while in fact each process has its unique slag) or recyclate and residue suite for secondary processing. This will help further drive environmental database development to capture unknown critical material combinations and their impacts. Flue dusts and other fugitive emissions can be quantified and/or characterised to understand their full impact. If not present on environmental databases, their impact can be analysed and added.
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Comprehensive closed mass and energy balances for each element are given for systems and processes. In many cases, average LCA databases do not provide consistency in this regard. HSC Sim can also provide exergy data. Therefore, combining exergy and LCA-type information can help significantly to drive metal production further to its technoeconomic limits. This innovation is driven on a thermodynamic and techno-economic basis. Harmonising LCA and the metallurgical software has enormous advantages to understand the limitations of RE better. The simulation component provides the total freedom to create any process whatsoever and impact it; clearly if the appropriate environmental data are present on the GaBi database, which can be mapped to the flows in the simulator.
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Fig. 8 A GaBi Plan that uses the GaBi Process (derived from Fig. 4) to estimate environmental impact, shown here using ReCiPe using EU-27 data where possible
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If the data are not fully known, these can be effectively interpolated from information in the system that is known. Thus from incomplete data, complete simulation-based data sets can be derived. Allocation issues decrease as all flows of elements, compounds; plastics and so on are estimated including an energy balance, permitting the evaluation of metal and energy recovery for a complete system (Fig. 1), processing plant (Fig. 4) or reactor. Simplified approximations of the recycling rate and energy recovery estimations are problematic for multi-material products. While from a material-centric point of view, these may be acceptable (EC 2012), for more complex products only simulation can provide an unambiguous answer, as will be shown in the LED recycling example. Allocation issues are more easily dealt with, as simulation will always map all input materials, elements, metals, alloys, minerals etc. to all outputs of the reactor, plant or complete recycling system. DfRE shows its true meaning namely it is the design for recyclate quality and further processing in metallurgical systems and provides the composition of diffuse emissions so that a decent environmental impact assessment can be made, especially on streams that have no economic value. As the recyclate quality is known, the amounts to dilute virgin pig iron or aluminium are also required to dilute impurities to acceptable levels in alloys. This also fills a gap that present LCA does not address well.
Due to the unique situation that exists at each industrial facility or system, the specific data in this paper are only partially presented, as the data are not representative and generally applicable for all situations. Thus, take care when using reported data that has not clearly provided the basis for their origin. Simulating BAT with HSC Sim and calculating the impact with GaBi must be done separately for different industrial facilities, because average values may lead to incorrect conclusions and ultimately do not show what really has to be improved at each facility and system. This is an ambitious goal to strive for i.e. to get the lowest impact for each facility with its unique technology and feed suite, which would obviously reduce the footprint of the complete industry. In the next section, this methodology will be used to derive various results, either for single reactors, plants or systems in which the combination of HSC Sim and GaBi are used for primary as well as secondary metal production, covering the complete flow sheet depicted in Fig. 1.
3 Greenprinting case studies Each example will highlight the strengths of this DfRE approach and discuss the developments that have to be made to improve the environmental impacting of systems in lieu of improving RE. At the end of each example, an innovation item is mentioned to also show how it improves LCA methodology and its practical implementation in the metals and mining sector.
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An environmental LCC, even though not carried out in the scope of this paper, would be the logical counterpart of these assessments, since many of the decisions made based on them (including policies) require information of their economic efficiency as well. This will bring the assessment one step closer to a full sustainability assessment, as envisioned by, e.g. Klöpffer (2008) and Finkbeiner et al. (2010), combining the environmental, economic and social perspectives of sustainability. The true value of this DfRE linkage is the augmenting of the databases of both tools with thermodynamics- and minerals-based detail, which is rather unique. This also provides the detail especially to quantify the environmental impact of diffuse emissions, the composition of which is estimated via simulation. In the end, resource efficiency is determined by understanding the losses from systems and how these can be mitigated. LCA and the used material flow analysis (MFA) (Brunner and Rechberger 2004) do not have the resolution and technological/thermodynamic basis to cascade information from LCA back to technology and ultimately its governing thermodynamics.
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consumption, tapping lances and similar consumables must also be included and can thereafter be incorporated in the impact assessment. Figures 11 and 12 show such an assessment, in which both simulated as well as other relevant data were used to present the environmental impact of a smelting line, starting from crushed rock to refined copper metal. The data in GaBi are estimated as approximately 1 % copper in ore (although this is not totally clear, and should be reported in the database for rigorous comparison). Figure 12 shows a comparison with the present reference data in the GaBi database. The simulation/modelling data is for 1 % Cu in ore and is compared with the data in the GaBi database. In this comparison we have highlighted the following points, which also allude to one of the present challenges in environmental assessment: & &
3.1 Primary copper production In this section, two examples will be shown, deriving from the discussion of the methodology in the previous section. The first will compare two types of converting processes for copper matte to blister copper and the second will consider some additional plant-specific production type data on top of process design.
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3.1.1 Comparison of copper flash and Peirce-Smith converting
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The flash-converting furnace in Fig. 4 can be replaced by Peirce-Smith converting. Figure 9 shows the Peirce-Smith furnace in such a flow sheet (i.e. flash smelting—PeirceSmith converting) and the detail of the white metal. Figure 10 then compares the results produced for the flow sheet including flash converting (Fig. 4) with that including Peirce-Smith converting. The midpoint analysis shows the effect of using Peirce-Smith converters, although both are relatively low values compared with the complete system as shown in Fig. 12. 3.1.2 Inclusion of additional plant-specific data in environmental analysis Considering the menu shown in Fig. 6 (top left), there is also the possibility to enter manual data, such as that for ancillary equipment, fugitive and other emissions that may not be fully captured by the simulations, such as gas burners for launders. Typical information such as refractory lining wear, electrode
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The left hand x-value (dot on axis) in the figures is that produced by the simulation and plant data and can be considered well defined (see Fig. 11 for data). Any mine- and ore-specific data, all of which have been averaged in the database, are assumed to for illustration purpose only to follow a normal distribution, as is the range of technologies that performs the processing. The unclarity concerning the boundary conditions of the data demands an assumption for comparison purposes that the data is distributed around the reported data in the GaBi database (which is obviously not necessarily normally distributed, but is selected for simplicity in order to highlight this important issue of also tightly defining the boundary conditions for all data in the LCA databases). This distribution highlights the importance of including important information such as ore grade, rock hardness (effecting crushing energy), open pit or underground mining, how much in-pit pre-processing, type of transport, conveying, electric vehicles, degree of automation, types of technologies etc. in the database to ensure that good comparisons can be made. The shading was produced by assuming a rather wide range of energy used at the 1 % ore grade level for mining and crushing, reaching a value of 35 % of the total.
When life-cycle methods, particularly LCA, are used to support decisions, uncertainty is an important issue to be incorporated into the assessment. In particular, if an assessment produces precise, quantitative results—such as for softwarebased applications—the results can at first seem very certain. Nevertheless, if only the results are viewed, it can often be overlooked that the actual process behind them is not very transparent. An approach, or results format, is required which manages uncertainty of all types and does so with transparency, fairness and competence. In fact, it has been highlighted many times before (see, e.g. Finnveden et al.
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Fig. 9 Shown is Peirce-Smith (PS) converting with the detail of the white metal given before the final blow to blister copper, illustrating the detail the result in Fig. 10 is based on.
2009; Huijbregts et al. 2001; Geisler et al. 2005; Lloyd and Ries 2007) that the results of any LCA often seem unambiguous, there is uncertainty and arbitrariness present—especially when using ready-made databases.
Fig. 10 A GaBi analysis for the production of blister copper for Flash Smelting and Flash Converting (FS-FC) and Flash Smelting and Peirce-Smith converting (FS-PC), respectively. Note, however, the values are relatively low for both processes in comparison to the wider system boundary including beneficiation/ mining as reflected by Fig. 11, showing that the concentrator has the major impact (This is an illustrative example only and should not be used for benchmark as energy mix, geographic location etc. will change the result in each category.)
3.1.3 Innovation potential This example clearly shows how this methodology of using simulation tools can be used to investigate which is the best
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Fig. 11 A GaBi Plan covering the processing of crushed rock (1 % Cu in crushed feed ore) via a concentrator, smelter (Flash Smelting and Flash Converting), refining as well as sulphuric acid production to 400,000 tpa refined copper. Included are the indicators for this section of the complete
flow sheet, noting that it is valid for a feed crushed ore feed grade of 1 % Cu (using EU-27 data if available—therefore do not generalise to all regions of the world)
solution on an engineering basis not only from a technoeconomic point of view but also from the environmental aspect. As both simulations are based on the same boundary conditions, feeds etc. an unbiased comparison can be made. It is also clear that the data in LCA databases need a more precise definition of boundary conditions to be able to compare simulation based impact data to data in environmental databases more accurately. In addition, this approach will also help to further develop environmental databases, as it is evident that numerous impact data are not available for detailed analysis, thus casting doubt on the general validity of such analyses.
recycling of different designs of LED bulbs and spot lamps. Recycling and recovery rates, as well as qualities, quantities, losses and fugitive emissions for all materials and elements in the LED lamps of this recycling setup have been predicted using the HSC Sim models. It is important to note that the particular focus of these trials was to produce a metal-rich fraction containing the driver and LED Printed Circuit Board Assemblies (PCBAs). Hence, the presented recycling performance does not necessarily represent the corporate or industrial average, however noticeable is the rather low recycling rates of the LEDs as shown by Fig. 13. The information produced by the simulation model for recyclate and element grades can be used to derive the environmental impacts of product designs and to quantify, evaluate and hence guide redesign for recycling (Krinke et al. 2009; Van Schaik and Reuter 2010, 2014). This analysis uniquely includes element and material recoveries for different operating conditions. The true meaning of DfRE (and DfR) is realised if all the materials are accounted for in the detail of the compounds and complex connections in all streams, whether they are valuable recyclates or diffuse emissions as shown in Fig. 13 (bottom right). All elements of the product design (through their compounds, alloys, materials etc.) are accounted for in either the recyclates or emissions. The detailed recyclates can then flow further to metallurgical operations and mass and energy balance models can be made, also incorporating any diluting metal, alloy, reagent etc. that
3.2 E-waste recycling and secondary copper production 3.2.1 Design for recycling of a light emitting diode lamp Reuter and Van Schaik (2015) have worked out a detailed Design for Recycling example following the simulation based approach discussed in this paper for Light Emitting Diode (LED) recycling. In this example five LED redesigns are linked via physical separation to metallurgical recovery as shown in Figs. 1 and 2 to the ultimate recycling rate of the LEDs' recovery rate of each element. The redesigns were performed to improve the recovery for example of aluminium and other bulk metals used, noting that loss of functional materials is inevitable due to product complexity. HSC simulation models have been defined and applied to simulate the
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Fig. 12 A comparison of Outotec copper technology (Beneficiation, Flash Smelting and Flash Converting, Refining, Sulphuric Acid— Fig. 11), which is the leftmost data point, to the mean in the GaBi environmental database. The shaded area represents mining and
crushing, for which the boundary conditions of the data are not well defined due to the unclear ore grade, mining and energy mix in environmental databases (would change for differing regions in the world)
impacts on the environmental footprint as shown in the next section. This depth of recyclate composition makes it possible to quantify the dilution with virgin material required to achieve the correct alloy compositions or to quantify the toxicology of diffuse streams, as their alloy and mineral composition is known through the simulation, being a function of recyclate quality (and product and process design). The result is a much more detailed evaluation of impacts and a true quantification of resource efficiency and thus true DfR.
technologies, it is also always important to compare innovations to the existing benchmarks. Figure 15 shows the simulation of a TSL furnace permitting the calculation of various options, feed types, operating modes etc. All the primary indicators shown in Fig. 4 can be calculated, and are summarised in the tables present in the simulation pane (covered due to confidentiality). These data can then be exported to GaBi and later an environmental assessment can be performed as shown in Fig. 16. Subsequently, an endpoint evaluation can be made as shown in Fig. 17, permitting a detailed analysis of different options. While LCA-type approaches provide a wealth of information (Figs. 16 and 17) on where things are problematic (socalled hotspots), they do not provide a solution: they provide neither information on how to innovate the system nor a techno-economic basis to improve the system. The linked simulation provides that wealth of insight into the process to translate hotspots to solutions. Therefore, it is always advisable, in addition to an environmental impact assessment of the complete system, to also use tools such as HSC Sim, which enable the use of thermodynamic-based approaches such as exergy for analysis and/or mass and energy transfer analysis
3.2.2 Metallurgical recovery—the ultimate recycling rate is calculated from the true ‘mineralogy’ of the recyclate composition Similarly to the evaluation of smelting for complete plants, individual technologies can be compared. It is quite useful to do this, especially if new technology is being developed. In this case, simulation is particularly worthwhile to check whether the developments make any techno-economic sense. Figure 14 shows various smelting technologies for the treatment of residues, e-waste, slags, scrap etc. It is not only important to understand the footprint of these
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Fig. 13 Simulation-based DfR and DfRE—the key to maximisation of resource efficiency and rigorous quantification of environmental impact. Shown is simulation based calculation of recycling rate of each element as
well as the recycling rate of the complete product (values on right of bottom right figure for the five product redesigns) product as well as the environmental impact for a product design—the basis of greenprinting
(Huda et al. 2012). Figure 18 shows such an exergy analysis, which can be made directly for the same simulated system shown in Fig. 15. It thus highlights where the improvements are to be made, namely where the most irreversible losses occur.
very much needed. It is also valuable in calculating the true environmental impact of product redesigns and the resource efficiency of processing technology and complete recycling systems, while being able to cascade that back to the processing system. The methodology produces mass and energy consistent as well as economically viable BAT process blocks, the inclusion of which in environmental databases will be invaluable in benchmarking technology in lieu of product design and systems for estimating the achievable resource efficiency baseline. The rather sparse available data in the environmental database with regard to the detail shown by Figs. 13, 14, 15, 16 and 17 does beg the question what DfR studies to date have analysed and how accurate the results are. For DfR to have any relevance and innovative potential in the future as well as to overcome its challenging issues, it should follow the path depicted by Fig. 13 which links Product Design (using CAD) to simulation that ultimately brings the designer into touch with metallurgy. This will help to harmonise and de-silo the activities in the material cycle depicted by Fig. 1 – thus simulation is the rigorous harmonising glue.
3.2.3 Innovation—a wider physics based analysis possible Seamless interaction between simulation results, thermodynamic (exergy) as well as environmental analysis is possible. This is required to drive innovation. While the LCA provides the hotspot analysis, the link to rigorous simulation and thermodynamics makes it possible to understand the hotspots and also subsequently drive the innovation that impacts RE the most. In addition, new green innovations and technologies can quickly be screened (or greenprinted) to check if they are viable. The prediction of recyclate and stream compositions makes the environmental analysis of systems more precise and will help to expand the detail defining these flows on environmental databases. This is especially valuable for DfR, where the methodological rigour suggested by Fig. 13 is an addition that is
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Fig. 14 Some figures of a selection of operational recycling smelting technologies (Top Submerged Lance (TSL) and Kaldo technology) mostly as an integral part of complex refining infrastructures, which recover metals from various phases. The environmental cleanliness of these systems is clear with extensive offgas cleaning systems to maintain operations within environmental legislative constraints
3.3 Environmental impact of nickel pig iron The high demand for non-ferrous metals in the Chinese market, the volatility of the market price of nickel and the lower recycling rate of stainless steel in the Chinese market has increased brought up nickel pig iron (NPI) production. Information about the
Fig. 15 An HSC Sim 8 simulation model for a Top Submerged Lance (TSL) e-waste and copper scrap smelting process, showing the link to GaBi that exports information in a form that can easily be used for
environmental impact of the production of ferronickel and nickel metal in the western world (including Russia) is available, as is information on the impact of nickel production as a share of highalloyed stainless steel. No data are available in relation to the environmental impact for the NPI produced in China that is used in the Chinese stainless steel industry.
environmental footprinting. Also shown is the detail of various streams as well as the Sankey for exergy
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Fig. 16 GaBi plan, Impact assessment of an e-waste smelter (Figs. 14 and 15) as well as the GaBi process (right) exported from HSC Sim (using E-27 data where possible). Note that these data should not be
generalised as they refer to a specific feed mix, which contains various copper containing materials, circuit boards, plastics, sludges etc. which impact all parameters
This example will elaborate on the LCA results for NPI production in China and show the environmental impact of nickel and ferronickel production per kilogram of nickel contained. Based on market analysis, Chinese NPI production
accounts for approximately 12 % of global production (Cartman 2012) and therefore its environmental impact is significant (Fig. 19). This figure also shows the importance of NPI production worldwide. The main production
Fig. 17 ReCiPe endpoint analysis from GaBi for the model depicted in Figs. 15 and 16 (mainly EU-27 data used) showing relatively low emissions. Note that the feedmix to the smelter which is usually a complex mixture of materials renders these results very specific and should not be generalised
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Fig. 18 Exergy analysis of the TSL furnace in Fig. 14 (could also be for a plant or system as shown by Fig. 4) using HSC Sim 8. In the example, the chemical, physical and total exergies (highlighted on right) are shown for a few streams of Fig. 15
technologies in terms of the amount of nickel contained in NPI produced are submerged arc furnaces (67 %) and blast furnaces (33 %), with a total production of 188 ktpa of nickel content in pig iron with a nickel content of 52 % above 10 %, 30 % at 4–8 % and 18 % at
