Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management

Journal of Cleaner Production xxx (2014) 1e14

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Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management Dimos Paraskevas a, *, Karel Kellens a, Wim Dewulf a, b, Joost R. Duflou a a b

KU Leuven, Department of Mechanical Engineering, Celestijnenlaan 300A, B-3001 Heverlee, Belgium Group T e Leuven Engineering College, KU Leuven Association, Belgium

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 August 2013 Received in revised form 23 July 2014 Accepted 15 September 2014 Available online xxx

The uncontrolled mixing of metals and their alloys during the different life cycle phases, combined with the melt purification constraints during remelting, pose great challenges during their end-of-life (EoL) treatment. In practice, open-loop recycling is typical and more common for metals than closed-loop recycling; especially in the case of aluminium, the industry operates in a cascade recycling approach. Associated with open-loop recycling are various types of material losses; loss of original functional quality, dissipation of scarce resources and the final need for dilution of the resulting metal impurities with primary materials. Thus, an environmental assessment tool is presented within this paper, aiming to support decision making related to the sustainable management of metal resources during secondary aluminium production. A material blending model aims at the minimization of the above mentioned losses in order to meet the product quality requirements. The goal of the study is threefold: i) to assess the environmental impact calculation of aluminium recycling, ii) to express, quantify and integrate dilution and quality losses into Life Cycle Assessment (LCA) studies, and iii) to determine the optimum material input for the recycling process from an environmental perspective. Different recycling options or strategies can be evaluated and compared based on avoided environmental impact. Case studies focusing on major post-consumer scrap streams are used to illustrate application areas and highlight the importance of altering and optimizing the raw material input. Finally, policy issues and opportunities for environmentally conscious metal management are discussed. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Life Cycle Assessment Cascade recycling Aluminium alloys Scrap sorting Material efficiency Industrial ecology

1. Introduction Metals, compared to other materials, have the highest potential for systematic recycling due to: i) their high economic value, ii) the large scrap volumes enabling economies of scale, as well as iii) their distinctive feature of excellent recyclability. Nevertheless, the contamination of the metal streams each time that re-circulates from residuals (alloying and foreign elements), especially those for which the removal from the melt is problematic (cf. Section 2.1), makes processing more difficult. For Aluminium (Al) more than 450 alloy designations/compositions have been registered by the Aluminium Association Inc. (Davis, 1998). While iron (Fe) mainly occurs as foreign/impurity element (also known as tramp element), typical alloying elements for aluminium are: silicon (Si), copper (Cu), Zinc (Zn), magnesium (Mg) and manganese (Mn). Two major categories can be defined with respect to the concentration of the alloying elements: i) high purity wrought alloys (alloy content up to 10 wt.%) and ii) cast * Corresponding author. Tel.: þ32 16 372801; fax: þ32 16 322986. E-mail address: [email protected] (D. Paraskevas).

alloys with much higher, especially for Si, tolerance limits (alloy content up to 20 wt.%). Due to the mixture and/or accumulation of the alloying elements during the different life cycle stages, these elements can no longer be considered as valuable elements, but rather as contaminants (Nakajima et al., 2011). Environmental considerations need to be integrated with many types of decisions. Ferretti et al. (2007) examined the aluminium supply chain, proposing a model to determine the supply mix, i.e. molten and solid alloy, incorporating both economic and environmental aspects. LCA is widely used in the aluminium industry (Rebitzer and Buxmann, 2005; Tan and Khoo, 2005; Liu and Müller, 2012) and provides a comprehensive methodology (ISO, 2006) that can be a valuable tool to identify the environmental burdens and benefits, of altering the raw materials (primary and waste material) in the recycling process from a holistic perspective. Major bottleneck in standard LCA is that quality degradation of metals during recycling cannot be properly described and quantified (Amini et al., 2007). Conventional LCA studies ignore the down-cycling aspect and account the metal inputs and the produced secondary metals as equivalent in terms of quality, assuming that they are recycled

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Please cite this article in press as: Paraskevas, D., et al., Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.09.102

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Nomenclature sc, Al, e Index for scrap, primary Al and alloying elements material batches respectively Msc, MAl, Me Available batch masses msc,mAl, me Selected mass from each batch in the pre-melt mixture ee ; esc ; eAl Mean or expected mass fraction values of alloying element e in each batch Mp Desired mass of the produced target alloy p emax ; emin Max. and Min. mass fraction tolerance limits of the p p target alloy p for element e SL, ML % scrap lost (SL) during collection, separation and processing (cleaning, pressing etc.) phase and % melting losses (ML) Ie, IAl Isc, Ipr Single point environmental impact per tonne of: alloying element e production (Ie), primary aluminium production (IAl), scrap collection, sorting and preparation phase (Isc). Ipr is the impact of the recycling process per mass of produced alloy excluding material inputs. (cf. Table A.1)

within closed alloy loops. However, instead of closed alloy loops most of the aluminium scrap is recycled in open loop cycles, where we cannot justify complete substitution of primary aluminium and alloying elements, since the inherent properties (chemical composition) of the metal input alter. Interrelationships between inherent properties and recycling maps for metal are provided in the work of Dubreuil et al. (2010). Thus, besides the amount of the metal input, also its composition must be taken into account in the environmental impact assessment of the recycling phase. Comprehensive reviews (Reuter et al., 2005; Gleich et al., 2006) discuss central issues related to the sustainable material use, recycling technologies and policies as well as the limits of metal recycling determined by thermodynamics, economics and process technology. One of the most critical and challenging steps in metal recycling operations is the logistic of material clustering based on the residuals concentration (Yellishetty et al., 2011). Johansson and Luttropp (2009), introduced the concept of Material Hygiene (MH). The definition of MH is ‘to act, in every step of the product lifecycle, towards greater amounts and increased purity of useful material from recycling, possible to use on the same quality level as before or degraded as little as possible’. In line with the MH concept, this study presents a decision support tool for the sustainable metal utilization during the metallurgical recycling phase, aiming at the minimization of material down-cycling and maximization of the scrap usage. 2. Aluminium recycling and its challenges 2.1. Melt refining and metallurgical limitations Recent studies based on thermodynamic analysis (Nakajima et al., 2011, 2012) indicate which elements can be removed and in how far impurity can be controlled during metallurgical recycling of various base metals, such as, aluminium, steel, copper and magnesium. These studies indicate that melt purification options are much more limited for aluminium compared to other base metals, like copper and steel. In particular, Nakajima et al. (2012) examined the removability of 45 elements (most of them occur as tramp elements) for the case of aluminium simulating the remelting

process under varying oxygen partial pressure and temperature conditions. Among the examined elements, only six can be removed either by evaporation in the gas phase or through the oxidation mechanism in the slag phase. Regarding the contamination by the typical alloying elements of Al, only Mg and Zn can be removed to an appreciable extent during remelting. Consequently, the residual elements remain in the metal phase during remelting and the purification of the melt from them is either technically very difficult or essentially impossible to achieve. Moreover, while a post electrolysis process can recover most of the elements that remain in the metal phase for copper, this is not the case for aluminium. Compared to the primary production of aluminium, consuming approximately 14 kWh/kg, the three layer electrolytic process for aluminium refining is more energy intensive with energy consumption between 17 and 18 kWh/kg (Gaustad et al., 2012). Fluxing is the most common and widely used melt purification treatment in the industry. In an experimental study, nearly half of the magnesium content was removed from the Al scrap after remelting with a salt flux treatment (Mashhadi et al., 2009). Furthermore, a flotation/de-gassing melt treatment, purging gases containing chemical reactive components such as chlorine gas, can also be an effective solution in removing Mg apart from hydrogen, Na, Ca and Li (Reuter et al., 2005). Zinc, a major alloying element in the 7XXX alloy series, can be recovered using distillation technologies (Ohtaki et al., 2000). Finally, other technologically advanced melt/chemical separation technologies, like fractional crystallization and unidirectional solidification are still in a research or early development stage (Gaustad et al., 2012). Therefore, from technical and economic point of view, these technologies are still questionable and currently not viable for scrap purification at large scales. 2.2. Quality constraint on subsequent metal uses Castro et al. (2004), Amini et al. (2007) and Nakamura et al. (2012) highlighted three types of losses during metal recycling: material, quality and dilution losses. Material losses include physical losses during scrap preparation/separating processes (e.g. from shredding) and melting losses such as oxidation losses as well as residues and slag waste that are landfilled. Quality losses occur when the quality (meaning composition) of the produced secondary metal does not match with the input material. Dilution losses occur when high purity metal is required to lower the contaminants (residuals) concentration to the desired limits of the target alloy. Amini et al. (2007) used an Exergetic Life Cycle Assessment approach to quantify these losses for the case of aluminium recycling; while Nakamura et al. (2012) focused on ferrous materials from the End-of-Life Vehicles (ELV). Quality and dilution losses can result in scrap under-utilization depending on the target alloy. Fig. 1 presents a Sankey diagram that visualizes the above mentioned losses during Al recycling, for the case of producing the 1 tonne of the 380.0 alloy by utilizing old scrap from the 6XXX (except 6061 and 6063) alloy series. The best case scenario of maximum scrap usage and minimum primary resource consumption is presented (example taken from the case studies section). Yet, despite these studies and the extensive Life Cycle Inventory (LCI) for aluminium, compiled from industry-wide generic data (Leroy, 2009; EAA, 2008; IAI, 2007), an environmental assessment tool for the sustainable metal use during recycling that integrates these losses is still missing. 2.3. Open and closed loop aluminium recycling circuits Nowadays, the refining limitations along with contamination challenges are addressed by the secondary Al industry either by dilution (also called sweetening) of the contaminants with primary

Please cite this article in press as: Paraskevas, D., et al., Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.09.102

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Fig. 1. Sankey diagram illustrating material, quality and dilution losses during recycling of old scrap from the 6XXX series (*except 6061 and 6063) to produce 1 tonne of 380.0 alloy.

Al, or by down-cycling to alloys with lower purity requirements. Therefore, the recycling of aluminium is performed in a cascade recycling chain (Fig. 2a). A graphical representation of the recycling pathways based on the scrap quality is shown in Fig. 2b. Unalloyed/ low alloyed Al (1XXX series), produced from primary Al, can be considered at the top of the chain as high purity products, but with relative few applications. Wrought alloys are in the middle of the cascade chain in terms of purity, while cast and die cast alloys, with much higher tolerance limits for residuals act as a sink in the cascade recycling chain absorbing the majority of the downgraded/ mixed scrap. For primary Al there is no restriction to produce any alloy by adding alloying elements. Three recycling options can be identified from environmental perspective: Preserving quality Meaning that the quality of the scrap streams is effectively controlled and recycling is performed in a compositionally closed recycling loop without any significant change in the chemical composition between the Input-Output (I/O) metal. Down-cycling/Cascade Accumulation of the residuals/impurities to Al alloys with lower purity requirements, by utilizing higher purity scrap streams to

produce lower purity products (e.g. transition from mixed wrought alloys to cast quality). Dilution of impurites When contaminants are diluted with higher purity metal inputs. Taking into account the limited scrap availability, the dilution agent, especially for wrought alloys, is primary Al. In closed alloy loops no dilution or quality losses occur. This strategy can more easily be implemented to high volume single alloy scrap streams where the demand for the specific alloys is also high. However, taking into account the limited scrap availability, and at the same time, the high demand for both wrought and cast alloys, dilution and cascade can effectively balance the demand and overcome the refining limitations. Nevertheless, one can wonder whether these latter strategies provide sustainable solutions in a long term perspective. 2.4. Cascade recycling and resulting challenges Liu and Müller (2012) studied the global aluminium flows for the year 2009. The authors reported that post-consumer scrap is mainly discharged from: i) ELV with 3.2 Mt, ii) electrical and cable scrap with 1.2 Mt, iii) packing-cans with 3.4 Mt, iv) buildings and

Fig. 2. a) Al cascade recycling chain and b) corresponding pathways (Paraskevas et al., 2013).

Please cite this article in press as: Paraskevas, D., et al., Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.09.102

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construction with 1.3 Mt, and v) consumer durables with 1.7 Mt. Due to the high demand for cast alloys, down-cycling mixed aluminium scrap to ‘recycle friendly’ cast alloys is a common strategy nowadays, but the future market for these products can be questioned. Cullen and Allwood (2013), estimate that every year, globally, 6.1 Mt of wrought scrap is downgraded into cast products. Wrought alloys represent two-third and cast alloys one-third of the global Al demand respectively (Cullen and Allwood, 2013; Liu et al., 2013). While cast alloys have limited applications and are mainly used in the transportation sector, wrought alloys have a much wider range of applications (e.g. in buildings). The European Aluminium Association reports that 73% of castings in Western Europe are used in the transport sector (EAA/OEA, 2006). High quality data from Japan also show that cast and die cast alloys are mainly used in the automobile sector, with their main application in engine manufacturing (Nakajima et al., 2011). In recent years, concern has risen from researchers regarding the sustainability of aluminium cascade recycling. Hatayama et al. (2012) indicate that the introduction of electric vehicles will result in a decrease of the demand for cast alloys, generating 6.1 Mt of scrap in 2030 which will not be recycled due to the high concentration of alloying elements. Modaresi and Muller (2012) used a dynamic material flow model at a global scale for the automotive system to forecast that the continuation of the above mentioned strategies will result in a non-recyclable casting scrap surplus by 2018 with an uncertainty margin of about 5 years. Minimizing material down-cycling and reducing primary Al needed for dilution during the recycling phase will help to mitigate these challenges and contribute to more efficient material use. From an economic perspective, the increased cost of primary Al in the case of dilution, as well as the reduced product value in the case of down-cycling form incentives towards more efficient quality preserving solutions. Since the impact of scrap usage is inversely proportional to the production cost, the overall production cost can be reduced by increasing the scrap utilization (Kirchain and Cosquer, 2007). 3. Model overview With the aim to develop a decision tool to support the sustainable utilization of metal resources during recycling, a parametric LCA tool, according to the ISO 14040 (2006) has been developed and is described within this paper. This tool aims to: i) to facilitate environmental impact calculation ii) express material, dilution and quality losses during Al recycling in LCA studies and iii) minimize down-cycling and primary material inputs by maximizing scrap utilization. Therefore the composition of the metal inputs as well as the desired target alloy specifications (e.g. Davis, 1998) need to be taken into account. The proposed model explicitly takes into account the material I/O interconnections, focusing on the contamination of the scrap streams by the residuals that pose great challenge in Al recycling. A systematic quantification of the environmental effects of different recycling strategies/options can be made based on this framework. The overall environmental impact is used as a metric to express and quantify material, quality and dilution losses.

Primary Al required for dilution of the final scrap mixture and necessary additions of all the typical alloying elements in order to adjust the desired alloy composition are included in the model. The case studies used for validation purposes in Section 4 focus on the major alloying elements for aluminium (Si, Fe, Cu and Mn) that remain in the metal phase during remelting (Nakajima et al., 2011). Other studies from Hatayama et al. (2007, 2009; 2012) also focus on these four problematic alloying elements for the case of Al. For these elements, the removal from the melt is very difficult or technically impossible, and dilution or ‘sweetening’ is required to reduce their concentration in the melt. Mg was excluded from the analysis since can easily be extracted from the melt (in the slag phase) at some appreciable extend applying the widely used salt fluxing and flotation/de-gassing refining technologies, as explained in Section 2.1. Therefore, dilution is not necessary for Mg and it is assumed that its concentration can be reduced to the target alloy compositional window. The environmental impact of the treatment of the liquid metal by fluxing and de-gassing with chloride gas is included. The Ecoinvent v2.2 (2012) LCI database is used as principal data source to model all processes and resources. The ReCiPe method (Goedkoop et al., 2009) was selected for the Life Cycle Impact Assessment (LCIA). Following this method the damage assessment was performed in two steps: the modelling of the actual damage € ran et al., 2009; Sleeswijk and the normalization and weighting (Go et al., 2008). The endpoint approach was chosen since at that level all the environmental burdens of the 18 midpoint categories are further converted and aggregated into the three endpoint impact categories: i) damage to human health (HH), ii) damage to ecosystem diversity (ED) and iii) damage to resource availability (RA). At the single point level the aggregated weighted damage of the three endpoint impact categories results are expressed in “ecopoints” (Pts). The hierarchist perspective and the European average weighting set (Europe ReCiPe H/A) were selected for this purpose. The results of the impact assessment according to Europe ReCiPe H/ A method for the material inputs and processes of the model along with their indicators are presented in Appendix Table A.1. The calculations were performed using the Simapro© software version 7.3.

3.2. Environmental impact calculation and impact contribution areas The abbreviations for the equations of 3.2 and 3.3 sections are explained at nomenclature. Fig. 4 presents a flowchart of the developed model to calculate the overall environmental impact of the secondary Al production as well as its interaction with the proposed material blending model. After mixing the available material batches, the concentration of each alloying element (e) in the final mixture should fall within the specification compositional window (minimum and maximum tolerance limits) depending on the target alloy specification. This requirement can be stated mathematically as:

emin p Mp 

X sc

3.1. Impact assessment model Fig. 3 represents the system boundaries of the LCA analysis covering all the life cycle stages from cradle to gate of the secondary Al production from old scrap. Since the recycling process aims at a specific alloy as output, the functional unit is set at 1 t (one metric ton) of the target aluminium alloy.

esc msc þ

X

ee me þ eAl mAl  emax Mp p

(1)

e

Within the recycling model for old scrap of EAA (2008), it is estimated that 1108 kg of Al scrap enters the scrap preparation phase (consisting of unit processes like shredding, sink and float, de-lacquering) accompanied with approximately 211 kg of foreign materials. Afterwards, 1055 kg of scrap exits the scrap preparation and enters the melting model to produce 1 tonne of ingot. Therefore, scrap losses (SL) of 4.79% during the scrap preparation phase

Please cite this article in press as: Paraskevas, D., et al., Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.09.102

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Fig. 3. System boundary and functional unit of the Al recycling process including melt refining.

Fig. 4. Flowchart of the proposed I/O model to determine the environmental impact of the production mix for the target alloy.

as well as metal losses (ML), mainly due to metal oxidation, of 5.21% during remelting (EAA, 2008) are also taken into account in the model. Including the above mentioned physical material losses and a composition uncertainty of the mean or the expected mass fraction values e of ±a% for the final material mixture, Equation (1) will be formed as:

emin p Mp 1  a%

 ð1  SL%Þð1  ML%Þ

þ ð1  ML%Þ

"

sc

X e

where:

X

esc msc #

ee me þ eAl mAl 

emax Mp p 1 þ a%

(2)

Mp ¼ð1  SL%Þð1  ML%Þ

X

msc

sc

þ ð1  ML%Þ

X

!

(3)

me þ mAl

e

In order to accommodate composition uncertainties of the input material, the compositional tolerance window of the target alloy for each element is narrowed. Consequently, the scrap usage in the final alloy is reduced, but on the other hand, the final product specifications are assured and possible costly composition readjustments are avoided. By mixing all the available scrap batches with primary Al and alloying elements, multiple potential solutions can be found. The environmental impact linked with the raw materials is taken into account as a function of the used volumes. The overall

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environmental impact per mass of produced alloy can be separated into five different impact contribution areas: i) the old scrap utilization impact, which includes the impact of sorting, collecting and preparing (scrap processing processes shredding, sink and float, delacquering etc.) old scrap, ii) the impact of the remelting, casting and alloying processes (mainly due to their energy requirements), excluding the material inputs and including the impact of melt refining, dross recycling and salt slag processing, iii) the impact of the added alloying elements, iv) the impact of the added primary Al required to dilute the scrap mixture contaminants, and v) the impact of the material losses. While the addition of alloying elements decreases the quality (in terms of purity) of the scrap mixture, the addition of primary Al decreases the concentration of contaminants leading to higher purities. Due to the limited scrap availability, especially for high purity scrap streams, and, as it is the practice nowadays, further dilution of the scrap mixture can only be made with primary Al. Therefore, dilution losses of the scrap mixture are expressed by the additional environmental impact originating from the primary Al addition and quality losses are reflected by the impact of the added alloying elements. The impact of the five impact contribution areas, including the material, quality and dilution losses normalized per mass of produced alloy, are quantified in Equations (4)e(8). The summation of the impact contributions provides the overall environmental impact per mass of produced target alloy (eq. (9)).

   P 1  SL% 1  ML% Isc sc msc Scrap utilization impact ¼ mass of produced alloy Mp

(4)

Recycling process impact ¼ Ipr mass of produced alloy

(5)

 P 1  ML% Quality losses impact e Ie me ¼ mass of produced alloy Mp

(6)

Dilution losses impact ð1  ML%ÞIAl mAl ¼ mass of produced alloy Mp

(7)

provide more opportunities for recycling. Mathematical programing techniques are also used to identify global recycling potentials for aluminium (Hatayama et al., 2009, 2012) and to optimize the recycling of ELV's (Reuter et al., 2005). In this section, a material blending model that aims to identify the optimum, from environmental perspective, material input for the recycling process is proposed. This batch mixture that serves as input for the recycling model (cf. Fig. 3) should meet the desired alloy specifications (cf. Equation (2)). Three main objectives can be identified: i) to maximize the scrap usage, ii) to minimize dilution losses and iii) to minimize quality losses. By minimizing Equation (9), the pre-melt batch mixture providing the lowest environmental impact per tonne of produced target alloy can be identified. Since, Isc has the lowest value compared to the rest of impact values (IAl, IFe, ICu, IMn, Isi), minimizing Equation (9) allows to determine a maximum scrap usage while minimizing primary material inputs. However, in the cases where primary Al is required for dilution, the minimum primary Al addition and the minimum alloying elements addition can be conflicting targets. Primary Al has higher environmental impact value than most of the alloying element (cf. Table A1). Therefore, minimizing eq. (9), these alloying elements are preferred and added instead of primary Al, until their concentration in the final alloy reach the upper tolerance limits of the desired alloy (as IAl has higher value than IFe, IMn, Isi). Thus, the minimization process tends to end up with solutions that needlessly downgrade the produced alloy, substituting dilution losses by quality losses, without any significant reduction in the overall environmental impact per mass of produced alloy. Downgrading can easily be done by the addition of alloying elements but the thermodynamics dictates that it is hard to remove some of them during remelting and dilution will be the only solution in order to decrease their concentration. For instance, if a given mass of scrap with 0.8% Mn is to be used to produce an alloy with a maximum tolerance of 0.4% Mn, the scrap mass needs to be diluted with primary Al of equal mass to achieve this requirement. Thus, in order to avoid unnecessary down-cycling in line with the MH concept, the objective of minimum addition of alloying elements should be prioritized. This prioritization is

  P P ðSL% þ ML%  SL%ML%ÞIsc sc msc þ ML% IAl mAl þ e me Ie Material losses impact ¼ mass of produced alloy Mp

 P  P  OverallEnvironmental Impact Isc sc msc e me Ie ¼ þ mass of produced alloy Mp Mp     IAl mAl þ Ipr þ Mp

(9)

3.3. Material blending model Mathematical programming models are used by many producers as a decision support tool in production optimisation and planning, raw material purchase and material blending problems. In particular for aluminium recycling, optimization models are used as a guidance tool for alloy production in order to increase scrap usage, decrease the total production cost and address composition uncertainties of the raw materials in the alloy production (Kirchain and Cosquer, 2007; Gaustad et al., 2007). Li et al. (2011) highlight the role of scrap sorting in production and economic flexibility during recycling, where generation of added value of the sorted waste streams could help to reduce material down-cycling and

(8)

strengthened by considering the EoL treatment of the produced alloy and the challenge of unrecyclable scrap surplus in the near future due to high alloy content (cf. section 2.4). Since the objectives of minimum quality and minimum dilution losses are conflicting, the optimization problem can be addressed as a multi-objective one. The Goal Programming (GP) approach, originally proposed by Charnes and Cooper (1961), has been selected to address this problem. GP is a well-known technique for such type of multi-objective optimization problems and attempts to attain specific goal values introduced by the designer for each objective. The optimum solution set can be defined as the one that minimizes the sum of the square of the deviations from the set goals. This formulation can be stated mathematically as:

Minimize :

X

 dþ j þ dj

2

(10)

j

With additional goal constrains:  fj ðXÞ þ dþ j  dj ¼ bj ;

j ¼ 1; n

(11)

Please cite this article in press as: Paraskevas, D., et al., Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.09.102

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dþ  0; j

j ¼ 1; n

d j  0;

j ¼ 1; n

me  0; mAl  0

where fj(X) is the jth objective function, bj is its goal set by the designer and slack/surplus variables dþ and d j are, respectively, the j underachievement and overachievement of the jth goal. By maximising the scrap mass fraction in the final target alloy mass, the primary material inputs (primary Al and alloying elements) are minimized. Thus, the maximum scrap utilization is set as first goal (sc). The best case scenario is when the scrap usage reaches 100%, totally avoiding primary resource consumption In that case, the scrap utilization impact (Equation (4)) will be equal to Isc representing the scrap usage (sc) objective goal value. In cases where primary Al and alloying elements are required, we cannot minimize both quality and dilution losses. Therefore, the target of minimum quality losses impact (equation (6)), should be prioritised as second goal (q), with the ideal goal value equal to zero. The goals, sc and q, are not conflicting and only one optimal solution set that minimize the objective function 12 under constrains IeVI exist. By achieving maximum scrap utilization and minimum addition of alloying elements, the mass fraction of primary Al will obtain a near-minimum value. Therefore the optimization problem with  þ  decision variables msc ; me ; mAl ; dþ q ; dq ; dsc ; dsc can be formulated as:

Minimize :



 dþ q þ dq

2

2   þ dþ sc þ dsc

(12)

Subject to: I. Quality losses impact (q) constraint:

 P 1  ML% e Ie me  þ dþ q  dq ¼ 0 Mp

(13)

II. Scrap utilization impact (sc) goal constraint:

ð1  SL%Þð1  ML%ÞIsc Mp

P

sc msc

 þ dþ sc  dsc ¼ Isc

(14)

III. Finished alloy composition constraints (Equation (2)):

emin p Mp 1  a%

 ð1  SL%Þð1  ML%Þ "

þ ð1  ML%Þ

7

X sc

X

esc msc #

ee me þ eAl mAl 

e

emax Mp p 1 þ a%

ð2Þ

dþ q 0 d q 0 dþ sc  0 d sc  0 VI. Mass Balance constraint:

Mp ¼ð1  SL%Þð1  ML%Þ

X

msc

sc

þ ð1  ML%Þ

X

me þ mAl

! ð3Þ

e

For the minimization of the objective function (equation (12)) under the constraints IeVI, a spreadsheet model was developed and the nonlinear generalized reduced gradient (GRG) solver, developed by FrontlineSolvers, was applied. This solution method uses the GRG method as implemented in Lasdon and Waren's (1981) GRG2 code. It includes ‘Multistart’ and “clustering” methods for global optimization. The method generates candidate starting points for the GRG Solver (selected values between the specified bounds for the variables), group them into “clusters”. The solver runs from a representative point in each cluster and continues with successively smaller clusters that are increasingly likely to capture each possible locally optimal solution. Moreover, the multistart method make use of a “topographic” search method to compute a “topography” of the search space, in an effort to find better clusters and start from an improved point in each cluster. The authors suggest the use of tight bounds on the variables, in order to better perform the multistart methods. The used solver parameters, an alternative approach of the problem as well as guidelines to improve the performance of the method and avoid ‘locally’ optimal are provided in the Appendix A. Following this approach, the optimum, from environmental perspective, pre-melt material mixture can be identified. Using the obtained batch volumes, the environmental impact per tonne of target alloy can be calculated based on Equation (9). Taking also into account the EoL treatment of the produced alloy, a solution with near minimum total environmental impact per tonne can be obtained assuring minimum down-cycling of the scrap input. 4. Case studies: selection and results

IV. Availability of batches:

msc  Msc me  Me mAl  MAl V. Non-negative constraints:

msc  0;

The case studies focus on major post-consumer scrap streams (cf. Section 2.4) where different scenarios are evaluated based on the related environmental performance of utilizing these scrap streams for the production of specific alloys. Two wrought and two cast quality alloys are assumed as target alloys: AA3104, a large volume alloy used in beverage cans body; AA6061, one of the most commonly used alloys of the 6XXX series with many applications, especially in buildings; and the 355.0 and 380.0 typically cast alloys used in vehicles. For the final mixture, a compositional uncertainty of ±10% (a ¼ 10%) in the mass fraction values for each element was considered. The chemical composition of the examined scrap streams, the primary Al used as dilution agent, as well as the target alloys composition tolerance limits are presented in Appendix in Table A.2.

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In the reported case studies, it was assumed as material input of the recycling model, only one scrap batch available at the time along with primary Al and alloying elements (all in sufficient quantities). The maximum scrap utilisation, the single point environmental impact, as well as quality and dilution losses were analysed to investigate the environmental performance of each scrap stream separately. Moreover, scrap mixing scenarios for each target alloy, assuming all scrap batches available in sufficient quantities at the time, were performed. Both results are compared based on the above mentioned indicators. All the case studies represent the scenario of maximum scrap utilization and minimum quality losses of the scrap input. The maximum scrap usage (%) that can be achieved from each scrap stream in order to produce the selected target alloys, is listed in Fig. 5. Aggregated values of the environmental impact caused during the production of one tonne of the selected target alloys, are presented in descending order in Fig. 6a,b. Furthermore, this figure also provides the share of each impact contribution area (Equations (4)e(8)) to the overall impact. All results are normalised based on the functional unit of one tonne secondary Al alloy. 4.1. Midpoint indicators In an impact assessment method, various environmental impact categories are formulated. As it is not pragmatic to exhaustively cover the full list of those impact categories, this study narrows down to important indicators; climate change, fossil depletion and metal depletion from the ReCiPe method. The climate change category, characterised by the equivalent greenhouse gases emissions, is today recognized as among the most significant environmental issues, illustrated by the broad attention spent to this problem in international forums. On the other hand, the metal and fossil depletion reflects the environmental concerns for resource consumption, which, in turn, fuels the implementation of recycling. That is, with increased recycling of aluminium, reductions in material and energy consumption are anticipated compared to the primary production. Fig. 7 presents the impact assessment results per tonne of produced alloy according Europe ReCiPe to midpoint

(H) method, for these 3 midpoint impact categories. The impact assessment results for all the 18 midpoint impact categories of the ReCiPe method are provided in Appendix B, Table B.1. 5. Discussion For the AA3013 target alloy, the container and packaging stream (consisting mainly of used beverage can- UBC scrap) reaches the ideal 100% scrap usage where no composition adjustment is required. For the 380.0 target alloy, engines and transmission scrap is the most appropriate input as a near optimum maximum scrap usage (99.3%) is reached. Since this scrap batch is among the other compositionally closest to the 380.0 specifications, it represents also the best input for this alloy among the examined batches. Therefore, for the AA3014 and 380.0 alloys no better input can be identified by blending the available scrap batches. This was verified also from the batch planning model assuming all scrap batches available and in sufficient quantities. 5.1. Focus on single old scrap flows  The engine and transmissions batch (cast alloys), having high alloy content, is not suitable input to produce the target wrought alloys since this stream has to be diluted to a high extend to reduce the residuals concentration. The low scrap utilization values (5.2% and 7.4%) are the reason why cast alloys practically cannot be recycled to anything else than casting quality. In contrast, when the production of 355.0 and 380.0 cast alloys, mainly used in vehicle castings, is targeted, scrap utilization for this scrap batch significantly increases. The scrap usage in the latter case reaches a near optimum of 99.3% since no dilution is required except for a minor addition of alloying elements (0.7%).  6XXX series as well as container and packaging streams contain higher concentrations of Mn compared to the other streams. Targeting the production of AA3014, higher scrap utilization rates can be achieved since the tolerance limits for Mn for this alloy are higher. Using the container and packaging scrap stream results in the optimum 100% scrap utilization. However, due to

Fig. 5. Maximum scrap utilization (%) of each material input for the examined target alloys.

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9

Fig. 6. Overall environmental impact (Pts/t) and the share of each contributions area per produced tonne of a) AA3014 and 380.0 alloys and b) AA6061 and 355.0 alloys.

the high Mn content, these streams need to be diluted to a high extend to obtain the other examined target alloys. A sorting priority to separate scrap streams that contain manganese in high concentrations is proposed in order to control Mn contamination of the other scrap streams. In particular, unless the 3XXX alloy series (having Mn as a major alloying element) is selectively separated, the resulting stream would be unsuitable for 95% of all aluminium applications (Reck and Graedel, 2012).  A rough separation between cast and wrought scrap in the EoL treatment of vehicles can be environmentally beneficial since the cast and wrought fractions can be utilized more efficiently in separate recycling loops. Hatayama et al. (2012) illustrate that scrap sorting of ELV significantly lowers the generation of unrecycled scrap and reduces the primary Al requirements by 15e25%.  Container and packaging scrap can be fully utilised in the AA3104 alloy (used for the can body) without any other primary material reaching the optimum 100% scrap utilization. Thus, this

stream can perfectly managed in a separate closed recycling loop for the same application. This strategy is already be followed and represents an ideal example of an appropriate closed-loop recycling strategy based on separation of the scrap streams according to their product application. In order to overcome technological and financial barriers for improved alloy sorting, a scrap management strategy that focuses on product systems or scrap sources could indeed be preferable. New scrap procedures that will expand the UBC closed-loop recycling example, focusing on separating Al scrap according to the source products (e.g. windows frames, vehicles engines, heat exchangers …), could be a solid basis for such system in the context of material efficiency (Allwood et al., 2011). For example, during the ELV treatment, the Al engines representing the biggest cast fraction in automobiles can be sorted separately and utilized as input for automotive castings. In this way, quality losses can be minimized more efficiently by substituting alloying elements additions with the scrap alloy content. Utilizing

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Fig. 7. Impact assessment results per tonne of produced alloy according to Europe ReCiPe midpoint (H) method.

scrap flows in closed product loops could provide a cost effective and an environmentally friendlier solution for urban mining. Scrap standards, focusing on sorting based on product applications can positively affect this approach (ISRI, 2013). 5.2. Focus on quality and dilution losses Dilution and quality losses of scrap mixtures are expressed by the environmental impact originating from the primary Al and alloy elements addition respectively. Fig. 6 presents the share of the different impact contribution causes (%) to the total environmental impact/tonne for the production of the examined target Al alloys. For the wrought alloys 3014 and 6061, all of the scrap batches need to be diluted to a high extend, significantly lowering the final scrap usage. Dilution losses are dominant while quality losses are almost negligible. Exception is the container and packaging scrap stream which can be fully utilized in a closed-loop for the production of the 3014 alloy. For the other scrap batches, dilution losses have an impact share of 86.2e91.2% for the 3014 target alloy and 88.2e91.4% for the 6061 target alloy. Since wrought alloys have strict and very low tolerance limits for alloying elements, their production is heavily depending on primary Al consumption. On the other hand, when targeting cast alloys with lower purity requirements (e.g. 380.0 and 355.0), quality losses become visible. When targeting the 380.0 alloy, the different scrap batches do not require dilution, except for the container and packing and the 6XXX scrap steams due to their high Mn content. The difference in maximum scrap usage shown in Fig. 5, ranging from 99.3% (engine and transmission batch) to 90% (vehicles wrought products) is due to the addition of alloying elements. Depending on the selected scrap stream, quality losses may become even the main impact contributor. For the 380.0 alloy, quality losses have an impact share range between 18.1% and 60.1%. Therefore, in case of cascade, quality losses can significantly influence the overall environmental impact of the produced alloy. For the production of the higher purity 355.0 cast alloy, quality losses also contribute significantly to the overall impact (cf. Fig. 6b). In all cases quality losses represent the lowest values that can be achieved for specific scenarios. This highlights the influence of material down-cycling during recycling. Since quality losses are also an important impact contributor; the effort should be to maximize the substitution of alloying elements addition with the scrap alloy content. Apart from that, it is important to minimize the accumulation of the residuals that pose great

challenges for aluminium recycling, each time the metal recirculates. 5.3. Focus on material blending Assuming that all scrap batches are available in sufficient quantities, improved solutions can be found for the 6061 and 355.0 target alloys by blending different scrap streams. The scrap mixture for both cases can be found in Appendix Table A.3. For the 6061 alloy, the maximum scrap utilization increased from 37.3% (highest value for single stream) to 42.9%, lowering the overall environmental impact of the produced alloy by 8.2%. This reduction was achieved by utilizing higher purity scrap inputs and thus reducing the primary Al addition. For the 355.0 target alloy, mixing of scrap batches results in a minor increase of 0.4% in maximum scrap utilization compared to the best single stream option using building scrap (96%). By mixing the scrap batches according to the proposed material blending model, the environmental impact has an increase from 89.7 Pts to 96.8 Pts per tonne of produced alloy compared with the single stream of building scrap. The difference is that in the scrap blending solution, more downgraded scrap input (including engines and transmission scrap) was used. By utilizing more downgraded scrap quality losses were minimized by the scrap alloy content. Only a small primary Al addition was required to achieve this. Quality losses in that case are substituted by dilution losses, since priority was given to the minimization of the first category of losses. This solution is preferable even if the impact per produced alloy mass is slightly higher, since more scrap and scrap with higher alloy content was finally utilised. Generally, as explained above, downgraded Al scrap has fewer opportunities to be recycled and the focus should be to increase its usage. 6. Conclusions and outlook The main objective of this paper is to highlight the role of quality degradation as well as dilution losses during metal recycling and integrate them into LCA studies. Therefore, this paper presents a parametric LCA tool to determine, from environmental point of view, the optimal metal inputs for the aluminium recycling process depending on the target alloy specifications. The focus of the proposed model is to address the challenges related to the contamination of the Al scrap streams by alloying and impurity elements in the final recycling phase of remelting. The

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environmental benefits of reducing quality and dilution losses are expressed in terms of avoided environmental impact that can be used as a metric to evaluate the efficient use of resources. In order to mitigate future challenges of un-recyclable scrap surpluses due to high alloy content and the anticipated saturation of the demand for low grade alloys, a material blending model is proposed. Addressing the problem of blending different secondary and primary material inputs during Al recycling as a multi-objective optimization problem, priority was given to the minimization of cascading scrap input and maximization of the scrap usage. In this way, down-cycling of input scrap can be limited as much as possible and a near minimum value of dilution losses, due to primary Al addition, can be achieved. Since the removal of most of the residuals for the case of aluminium is very difficult or problematic during metallurgical processing, it is crucial to control their concentration in the scrap streams before re-melting more efficiently. Comparison of the environmental impact caused by the recycling of aluminium scrap toward different alloys is important to develop a sustainable scrap management strategy as well as to identify compositional tighter recycling loops. It is demonstrated that significant environmental benefits can be achieved by reducing the quality deterioration impact (addition of alloying elements) and dilution losses (addition of primary Al) by proper selection/utilization of the available scrap batches to the producer. New scrap procedures, dismantling/ disassembly and scrap sorting techniques (e.g. colour ID, etching and spectrographic techniques) play an important role in improving the overall material and environmental efficiency by increasing the amounts but also the purity of the useful material for recycling. Opportunities in such direction can be found in the construction sector (Yamasue et al., 2013) as well as in the ELV recycling (Reuter et al., 2005; Hatayama et al., 2012; Gradin et al., 2013). In general, by utilizing higher purity scrap streams for the production of wrought alloys, higher primary Al substitution values can be achieved. In consequence, the overall environmental impact of the produced secondary alloy can significantly be reduced by decoupling wrought alloy production from primary Al to a higher extends. On the other hand, the higher the alloy content of a scrap stream, the higher the potential to efficiently substitute alloying elements addition with the scrap alloy content during cascade recycling (down-cycling). The closed alloy loop strategy represents the completely closed compositional recycling loop, effectively avoiding the need for dilution and alloying elements addition. The environmental impact per mass of produced alloy is minimal and can be mainly attributed to the energy requirements of the recycling process itself and the material losses. Finally, proper pre-melt scrap mixing based on the composition of the available scrap batches, can provide environmentally preferable solutions by increasing scrap utilization and reducing the need for primary material inputs.

11

Acknowledgements The authors acknowledge support of the research fund of KU Leuven through project GOA/15/012-SUMMA. Appendix A The DALYs concept is used as a metric of quantifying the damage to human health (HH). The loss of species during a year is used to quantify the level of anthropogenic disruption in ecosystem quality mentioned as ecosystem diversity (ED). Finally, the surplus cost to the society, expressed in dollars, due to degradation of resources is used as metric to quantify the damage to resource availability (RA). At the single point level the aggregated weighted damage of the three endpoint impact categories results are expressed in “ecopoints” (Pts), where one thousand points can be interpreted as the annual environmental load (damage) of one average European inhabitant. LCI data quality The Ecoinvent v2.2 (2012) datasets for the primary and secondary aluminium production as well as for the typical alloying element that were used are of good overall quality and the technology text refers to average technology used in Europe. For primary Al production, the infrastructure is roughly estimated but compared to other items in the LCI records its influence is negligible. The collection of the scrap and related infrastructure are roughly estimated. Infrastructure has minor importance to other items like collection (transportation). Complementarily in cases where data were found to be outdated (e.g. scrap preparation, secondary aluminium production from old scrap) a recent and comprehensive source (Leroy, 2009; EAA, 2008) was used to update them. For a typical 5XXX or 6XXX series scrap batch, 120 kg of chlorine gas is required to remove the Mg content from one tonne of aluminium (Utigard et al., 1998). Optimization part The GRG method can be viewed as a nonlinear extension of the Simplex method, which selects a basis, determines a search direction, and performs a line search on each major iteration e solving systems of nonlinear equations at each step to maintain feasibility. The authors strongly advise the use upper and lower bounds on all  þ  decision variablesðmsc ; me ; mAl ; dþ q ; dq ; dsc ; dsc Þ. If not the multistart methods can still be used, but because the random sample must be drawn from an “infinite” range of values, this is unlikely to effectively cover the possible starting points, unless the GRG Solver is run on a great many sub-problems, which will increase the computation time.

Table A1 Impact assessment results according to Europe ReCiPe H/A method of the materials and processes that were used in the model. Impact contributors

Damage to HH (DALY*E-4/t)

Damage to ED (species.yr*E-6/t)

Damage to RA/ton ($/t)

Single point impact (Pts/t)

Old scrap usage impact

5.4

2.7

825

22.4

Secondary Al from old scrap

6.1

3.3

2259

34.3

245.5 41.1 98.1 925.8 100.7

89.9 13.1 58.1 24.6 24.4

47278 8505 26231 11271 24672

1001.0 167.0 499.0 1959.8 417.0

Primary Al at plant Pig Iron at plant Silicon at plant Copper at regional storage Manganese at regional storage

Comments Collecting, sorting and preparing (cleaning, pressing, de-coating etc.) of post-consumer scrap including infrastructure Remelting, casting, alloying excluding material inputs. Infrastructure, dross recycling and salt slag processing are included. Primary Al production and transportation Typical alloying elements impact values

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The lower bounds for all the decision variables can be equal to 0. The maximum upper bounds for msc that can be used is the desired mass of the final alloy Mp (equation (3)) including the expected physical material losses, meaning Mp(1 þ SL%þML%). The upper bounds for the alloying elements decision variables, me, is suggested to be less that 0.3*Mp as the alloy content for aluminium alloys is well below 30%. By multiple optimization run tighter upper bound can be found, confirming also the same solution set. A population size of at least 10 times the number of decision variables is suggested. A population size of 300 (for 16 decision variables), constraint precision of 0.001 and convergence of 0.1 were used in the case studies. Furthermore, ‘locally’ optimal solution can be avoided by checking each alloying element concentrations (Ce) in the final alloy, whether fulfil the minimum alloying element addition goal. For each solution set, Ce, can be calculated from equation (2) as:

Ce ¼ ð1  SL%Þð1  ML%Þ 

" X

X

esc msc þ ð1  ML%Þ

sc

#

ee me þ eAl mAl

e

If an alloying element is added and its concentration in the final mix is above the minimum boundary for this element, means that

minimum value. This prioritization can be achieved with the use of weights with orders of magnitude difference (weight1 [weight2[weight3). The weak point is that the weights have to be selected in an arbitrary way. The authors instead selected the goal programming approach in order to avoid the arbitrary weights selection and in order to provide meaningful goals that are also not conflicting. Case Studies The investigated old scrap streams are discharged from: i) castings used in engines and transmissions from vehicles, ii) wrought products from automobiles assuming a rough separation between cast and wrought fractions, iii) electrical equipment, iv) container and packing (mainly from used beverage cans), v) buildings and construction, vi) consumer durables, and vii) by the 6XXX series scrap excluding the two main alloys of this series AA6061 and AA6063. Mean or expected composition values for these scrap streams are used based on published studies (Kirchain and Cosquer, 2007; Hatayama et al., 2007, 2012). According to the chemical composition indicated by the London Metal Exchange for high grade aluminium contracts (LME, 2010), Al99.7 is considered as equivalent to primary aluminium.

Table A2 The chemical composition (mean values) of the examined scrap streams, the primary Al used as dilution agent as well as the target alloys composition tolerance limits. Composition, wt % Scrap batches

Dilution agent Target alloys specifications

Al engines and trans Vehicles (wrought products) Electrical equipment Container & packaging Buildings Consumer durables 6XXX (except 6061/6063) Primary Al AA3014 AA6061 355.0 380.0

Fe

Si

Cu

Mn

Source

0.68 0.45 0.59 0.59 0.48 0.62 0.5 0.2 0.8 0.7 0.6 2.0

8.61 1.79 2.91 0.29 1.78 3.4 0.96 0.1 0.6 0.4e0.8 4.5e5.5 7.5e9.5

2.69 0.26 0.84 0.22 0.47 1.04 0.23 0 0.05e0.25 0.15e0.40 1.0e1.5 3.0e4.0

0.27 0.16 0.21 1.14 0.21 0.19 0.63 0 0.8e1.4 0.15 0.50 0.50

Kirchain and Cosquer, 2007 Hatayama et al., 2007 Hatayama et al., 2012 Hatayama et al., 2012 Hatayama et al., 2012 Hatayama et al., 2012 Hatayama et al., 2012 LME, 2010 Davis, 1998 Davis, 1998 Davis, 1998 Davis, 1998

the solution set is a local minimum. In that case the minimum alloying element goal is not achieved. An alternative approach that can be followed in constructing the model, is the use of weights in order to prioritize the different goals. For example:

minimize : weight1 * goal1 þ weight2 * goal2 þ weight3 * goal3 The objective, explained in the manuscript, is: i) maximize scrap usage and at the same time ii) minimize alloying elements addition. By achieving maximum scrap utilization and minimum addition of alloying elements, the fraction of primary Al will obtain a near-

Table A3 Optimal material mixture for the 6061 and 355.0 alloy production. Batch mixture

6061

355.0

Al engines and trans Vehicles (wrought products) Buildings 6XXX (except 6061/6063) Primary Al Si

e 3.47% 27.99% 11.45% 57.09% e

45.89% 50.36% e e 3.58% 0.14%

Appendix B. Midpoint indicators

Table B1 Impact assessment results per tonne of produced alloy according to Europe ReCiPe midpoint (H) method. Alloy

Impact Category

Unit

Al engines & transm.

Vehicles (wrought fraction)

Electrical equipment

Container & packaging

Buildings

Consumer durables

6XXX (except 6061/6063)

AA3104

Climate Change Metal depletion Fossil depletion Ozone depletion Human toxicity Photochemical oxidant formation Particulate matter formation Ionising radiation

kg kg kg kg kg kg kg kg

1.09Eþ04 2.10Eþ03 4.41Eþ02 1.03E-03 1.30Eþ03 3.10Eþ01 2.39Eþ01 1.01Eþ03

8.61Eþ03 1.94Eþ03 3.87Eþ02 8.72E-04 1.01Eþ03 2.42Eþ01 1.86Eþ01 7.91Eþ02

9.75Eþ03 2.01Eþ03 4.14Eþ02 9.50E-04 1.15Eþ03 2.75Eþ01 2.12Eþ01 9.02Eþ02

6.89Eþ02 3.93Eþ01 1.97Eþ02 3.28E-04 2.38Eþ01 5.61E-01 2.78E-01 1.49Eþ01

8.59Eþ03 1.92Eþ03 3.87Eþ02 8.70E-04 1.01Eþ03 2.41Eþ01 1.86Eþ01 7.89Eþ02

1.00Eþ04 2.04Eþ03 4.20Eþ02 9.68E-04 1.18Eþ03 2.83Eþ01 2.18Eþ01 9.27Eþ02

5.85Eþ03 1.30Eþ03 3.21Eþ02 6.83E-04 6.67Eþ02 1.59Eþ01 1.22Eþ01 5.21Eþ02

CO2 eq Fe eq oil eq CFC-11 eq 1,4-DB eq NMVOC PM10 eq U235 eq

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Table B1 (continued ) Alloy

Impact Category

Unit

Al engines & transm.

Vehicles (wrought fraction)

Electrical equipment

Container & packaging

Buildings

Consumer durables

6XXX (except 6061/6063)

Terrestrial acidification Freshwater eutrophication Marine eutrophication Terrestrial ecotoxicity Freshwater ecotoxicity Marine ecotoxicity Agricultural land occupation Urban land occupation Natural land transformation Water depletion

kg SO2 eq kg P eq kg N eq kg 1,4-DB eq kg 1,4-DB eq kg 1,4-DB eq m2a m2a m2 m3

4.91Eþ01 6.59E-01 9.58E-01 1.10Eþ00 3.48Eþ00 1.15Eþ01 1.04Eþ02 5.58Eþ01 2.07Eþ00 5.31Eþ01

3.82Eþ01 5.15E-01 7.49E-01 8.54E-01 2.71Eþ00 9.01Eþ00 8.52Eþ01 4.47Eþ01 1.62Eþ00 4.32Eþ01

4.36Eþ01 5.87E-01 8.53E-01 9.73E-01 3.09Eþ00 1.03Eþ01 9.47Eþ01 5.02Eþ01 1.84Eþ00 4.81Eþ01

6.17E-01 1.66E-02 1.94E-02 1.77E-02 4.67E-02 2.64E-01 1.93Eþ01 5.41Eþ00 6.87E-02 8.78Eþ00

3.81Eþ01 5.13E-01 7.46E-01 8.51E-01 2.70Eþ00 8.97Eþ00 8.51Eþ01 4.46Eþ01 1.62Eþ00 4.31Eþ01

4.48Eþ01 6.03E-01 8.76E-01 1.00Eþ00 3.18Eþ00 1.06Eþ01 9.68Eþ01 5.15Eþ01 1.89Eþ00 4.92Eþ01

2.51Eþ01 3.42E-01 4.95E-01 5.63E-01 1.78Eþ00 5.97Eþ00 6.23Eþ01 3.10Eþ01 1.08Eþ00 3.12Eþ01

Alloy

Impact category

Unit

Al engines & transm.

Vehicles (wrought fraction)

Electrical equipment

Container & packaging

Buildings

Consumer durables

6XXX (except 6061/6063)

Scrap blending

AA6061

Climate Change Metal depletion Fosil depletion Ozone depletion Human toxicity Photochemical oxidant formation Particulate matter formation Ionising radiation Terrestrial acidification Freshwater eutrophication Marine eutrophication Terrestrial ecotoxicity Freshwater ecotoxicity Marine ecotoxicity Agricultural land occupation Urban land occupation Natural land transformation Water depletion

kg CO2 eq kg Fe eq kg oil eq kg CFC-11 eq kg 1,4-DB eq kg NMVOC kg PM10 eq kg U235 eq kg SO2 eq kg P eq kg N eq kg 1,4-DB eq kg 1,4-DB eq kg 1,4-DB eq m2a m2a m2 m3

1.07Eþ04 4.97Eþ02 4.31Eþ02 1.02E-03 1.28Eþ03 3.04Eþ01 2.34Eþ01 9.96Eþ02 4.83Eþ01 6.49E-01 9.41E-01 1.08Eþ00 3.43Eþ00 1.14Eþ01 1.03Eþ02 5.43Eþ01 2.04Eþ00 5.22Eþ01

7.52Eþ03 3.75Eþ02 3.57Eþ02 7.97E-04 8.94Eþ02 2.09Eþ01 1.60Eþ01 6.81Eþ02 3.31Eþ01 4.62E-01 6.47E-01 7.43E-01 2.35Eþ00 8.03Eþ00 7.61Eþ01 3.87Eþ01 1.41Eþ00 3.83Eþ01

9.12Eþ03 4.24Eþ02 3.94Eþ02 9.07E-04 1.08Eþ03 2.56Eþ01 1.97Eþ01 8.38Eþ02 4.06Eþ01 5.47E-01 7.92E-01 9.08E-01 2.89Eþ00 9.59Eþ00 8.93Eþ01 4.64Eþ01 1.72Eþ00 4.52Eþ01

1.02Eþ04 5.22Eþ02 4.25Eþ02 9.82E-04 1.25Eþ03 2.89Eþ01 2.22Eþ01 9.43Eþ02 4.59Eþ01 6.46E-01 8.96E-01 1.03Eþ00 3.26Eþ00 1.12Eþ01 1.03Eþ02 5.20Eþ01 1.94Eþ00 4.99Eþ01

7.49Eþ03 3.49Eþ02 3.56Eþ02 7.95E-04 8.73Eþ02 2.08Eþ01 1.59Eþ01 6.79Eþ02 3.29Eþ01 4.44E-01 6.43E-01 7.36E-01 2.34Eþ00 7.78Eþ00 7.58Eþ01 3.85Eþ01 1.40Eþ00 3.81Eþ01

9.48Eþ03 4.40Eþ02 4.02Eþ02 9.32E-04 1.12Eþ03 2.67Eþ01 2.05Eþ01 8.73Eþ02 4.23Eþ01 5.69E-01 8.25E-01 9.46E-01 3.01Eþ00 9.98Eþ00 9.23Eþ01 4.81Eþ01 1.79Eþ00 4.67Eþ01

9.17Eþ03 4.66Eþ02 3.98Eþ02 9.10E-04 1.11Eþ03 2.58Eþ01 1.98Eþ01 8.42Eþ02 4.09Eþ01 5.75E-01 7.99E-01 9.19E-01 2.91Eþ00 9.98Eþ00 9.20Eþ01 4.68Eþ01 1.73Eþ00 4.54Eþ01

6.89Eþ03 3.22Eþ02 3.41Eþ02 7.54E-04 7.97Eþ02 1.90Eþ01 1.45Eþ01 6.19Eþ02 3.00Eþ01 4.06E-01 5.87E-01 6.72E-01 2.13Eþ00 7.12Eþ00 7.08Eþ01 3.55Eþ01 1.28Eþ00 3.55Eþ01

Alloy

Impact category

Unit

Al engines & transm.

Vehicles (wrought fraction)

Electrical equipment

Container & packaging

Buildings

Consumer durables

6XXX (except 6061/6063)

Scrap blending

355.0

Climate Change Metal depletion Fosil depletion Ozone depletion Human toxicity Photochemical oxidant formation Particulate matter formation Ionising radiation Terrestrial acidification Freshwater eutrophication Marine eutrophication Terrestrial ecotoxicity Freshwater ecotoxicity Marine ecotoxicity Agricultural land occupation Urban land occupation Natural land transformation Water depletion

kg CO2 eq kg Fe eq kg oil eq kg CFC-11 eq kg 1,4-DB eq kg NMVOC kg PM10 eq kg U235 eq kg SO2 eq kg P eq kg N eq kg 1,4-DB eq kg 1,4-DB eq kg 1,4-DB eq m2a m2a m2 m3

6.01Eþ03 2.81Eþ02 3.30Eþ02 6.93E-04 6.84Eþ02 1.64Eþ01 1.25Eþ01 5.32Eþ02 2.58Eþ01 3.50E-01 5.08E-01 5.80E-01 1.83Eþ00 6.12Eþ00 7.17Eþ01 3.13Eþ01 1.11Eþ00 3.17Eþ01

8.69Eþ02 3.43Eþ02 2.57Eþ02 3.40E-04 2.50Eþ02 1.56Eþ00 9.77E-01 2.51Eþ01 2.63Eþ00 2.13E-01 6.19E-02 8.51E-02 1.40E-01 2.96Eþ00 6.76Eþ01 8.09Eþ00 1.27E-01 9.85Eþ00

1.60Eþ03 2.00Eþ02 2.57Eþ02 3.90E-04 2.16Eþ02 3.51Eþ00 2.45Eþ00 9.81Eþ01 5.43Eþ00 1.48E-01 1.15E-01 1.34E-01 3.61E-01 2.26Eþ00 6.05Eþ01 1.07Eþ01 2.62E-01 1.28Eþ01

6.83Eþ03 6.67Eþ02 4.21Eþ02 7.49E-04 1.03Eþ03 1.94Eþ01 1.47Eþ01 6.03Eþ02 3.11Eþ01 6.20E-01 6.15E-01 7.24E-01 2.15Eþ00 9.97Eþ00 1.39Eþ02 3.75Eþ01 1.30Eþ00 3.56Eþ01

8.66Eþ02 2.71Eþ02 2.56Eþ02 3.40E-04 1.97Eþ02 1.49Eþ00 8.77E-01 2.47Eþ01 2.37Eþ00 1.68E-01 5.68E-02 7.38E-02 1.27E-01 2.34Eþ00 6.76Eþ01 7.75Eþ00 1.25E-01 9.73Eþ00

2.34Eþ03 1.95Eþ02 2.70Eþ02 4.41E-04 2.81Eþ02 5.66Eþ00 4.11Eþ00 1.71Eþ02 8.79Eþ00 1.71E-01 1.80E-01 2.06E-01 6.05E-01 2.75Eþ00 6.35Eþ01 1.41Eþ01 4.04E-01 1.59Eþ01

3.34Eþ03 4.81Eþ02 3.31Eþ02 5.09E-04 5.74Eþ02 8.98Eþ00 6.66Eþ00 2.64Eþ02 1.45Eþ01 3.85E-01 2.92E-01 3.51E-01 9.71E-01 5.90Eþ00 1.02Eþ02 2.04Eþ01 6.17E-01 2.05Eþ01

1.09Eþ03 5.71Eþ01 2.08Eþ02 3.55E-04 7.25Eþ01 1.75Eþ00 1.18Eþ00 5.32Eþ01 2.50Eþ00 4.13E-02 5.59E-02 5.97E-02 1.79E-01 6.97E-01 2.46Eþ01 7.35Eþ00 1.47E-01 1.05Eþ01

Alloy

Impact category

Unit

Al engines & transm.

Vehicles (wrought fraction)

Electrical equipment

Container & packaging

Buildings

Consumer durables

6XXX (except 6061/6063)

380.0

Climate Change Metal depletion Fosil depletion Ozone depletion Human toxicity Photochemical oxidant formation Particulate matter formation Ionising radiation Terrestrial acidification Freshwater eutrophication Marine eutrophication Terrestrial ecotoxicity Freshwater ecotoxicity Marine ecotoxicity

kg kg kg kg kg kg kg kg kg kg kg kg kg kg

7.00Eþ02 2.70Eþ02 2.00Eþ02 3.29E-04 1.93Eþ02 7.97E-01 6.03E-01 1.61Eþ01 1.49Eþ00 1.63E-01 3.60E-02 5.40E-02 8.65E-02 2.27Eþ00

1.08Eþ03 1.12Eþ03 3.28Eþ02 3.54E-04 8.28Eþ02 3.09Eþ00 2.36Eþ00 3.81Eþ01 6.50Eþ00 7.13E-01 1.40E-01 2.29E-01 3.17E-01 9.83Eþ00

1.02Eþ03 9.36Eþ02 3.07Eþ02 3.50E-04 6.89Eþ02 2.67Eþ00 2.01Eþ00 3.44Eþ01 5.51Eþ00 5.93E-01 1.20E-01 1.93E-01 2.71E-01 8.17Eþ00

6.42Eþ03 1.41Eþ03 4.76Eþ02 7.21E-04 1.52Eþ03 1.91Eþ01 1.47Eþ01 5.56Eþ02 3.20Eþ01 1.08Eþ00 6.35E-01 8.01E-01 2.12Eþ00 1.61Eþ01

1.08Eþ03 1.06Eþ03 3.27Eþ02 3.54E-04 7.79Eþ02 3.02Eþ00 2.27Eþ00 3.78Eþ01 6.24Eþ00 6.71E-01 1.35E-01 2.18E-01 3.05E-01 9.24Eþ00

9.91Eþ02 8.69Eþ02 2.98Eþ02 3.48E-04 6.39Eþ02 2.50Eþ00 1.88Eþ00 3.28Eþ01 5.13Eþ00 5.50E-01 1.12E-01 1.79E-01 2.54E-01 7.58Eþ00

2.93Eþ03 1.23Eþ03 3.85Eþ02 4.81E-04 1.07Eþ03 8.65Eþ00 6.61Eþ00 2.17Eþ02 1.54Eþ01 8.43E-01 3.12E-01 4.28E-01 9.39E-01 1.20Eþ01

CO2 eq Fe eq oil eq CFC-11 eq 1,4-DB eq NMVOC PM10 eq U235 eq SO2 eq P eq N eq 1,4-DB eq 1,4-DB eq 1,4-DB eq

(continued on next page)

Please cite this article in press as: Paraskevas, D., et al., Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.09.102

14

D. Paraskevas et al. / Journal of Cleaner Production xxx (2014) 1e14

Table B1 (continued ) Alloy

Impact category

Unit

Al engines & transm.

Vehicles (wrought fraction)

Electrical equipment

Container & packaging

Buildings

Consumer durables

6XXX (except 6061/6063)

Agricultural land occupation Urban land occupation Natural land transformation Water depletion

m2a m2a m2 m3

1.99Eþ01 6.51Eþ00 7.51E-02 9.16Eþ00

1.19Eþ02 1.31Eþ01 2.01E-01 1.17Eþ01

1.04Eþ02 1.18Eþ01 1.80E-01 1.12Eþ01

1.84Eþ02 3.95Eþ01 1.26Eþ00 3.48Eþ01

1.19Eþ02 1.28Eþ01 1.99E-01 1.16Eþ01

9.66Eþ01 1.13Eþ01 1.71E-01 1.10Eþ01

1.47Eþ02 2.23Eþ01 5.69E-01 1.97Eþ01

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Please cite this article in press as: Paraskevas, D., et al., Environmental modelling of aluminium recycling: a Life Cycle Assessment tool for sustainable metal management, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.09.102