Application of exergy-based approach for implementing design for reuse: The case of microwave oven

Accepted Manuscript Application of exergy-based approach for implementing design for reuse: The case of microwave oven Sérgio Tadeu de Almeida, Milton Borsato, Cássia Maria Lie Ugaya PII:

S0959-6526(17)32022-X

DOI:

10.1016/j.jclepro.2017.09.034

Reference:

JCLP 10535

To appear in:

Journal of Cleaner Production

Received Date: 13 June 2017 Revised Date:

9 August 2017

Accepted Date: 4 September 2017

Please cite this article as: Almeida SéTadeude, Borsato M, Lie Ugaya CáMaria, Application of exergybased approach for implementing design for reuse: The case of microwave oven, Journal of Cleaner Production (2017), doi: 10.1016/j.jclepro.2017.09.034. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Essential title page information ACCEPTED MANUSCRIPT • Title: An Exergy-based Approach for Implementing Design for Reuse • Author names and affiliations: Sérgio Tadeu de ALMEIDA, MSc

Cássia Maria Lie UGAYA, Ph.D.

Contact: Tel. (+55-41) 3279-4589 Fax. (+55-41) 3279-4589 Cel. (+55-41) 999-797-953 e-mail: [email protected]

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Affiliation and full postal address: Federal University of Technology - Parana (UTFPR) http://www.utfpr.edu.br Av. 7 de Setembro 3165 80230-901 Rebouças, Curitiba-PR, Brazil

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Milton BORSATO, Ph.D.

Contact: Tel. (+55-41) 3053-7043 Cel. (+55-41) 988-085-556 e-mail: [email protected]

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Affiliation and full postal address: Federal University of Technology - Parana (UTFPR) http://www.utfpr.edu.br Av. 7 de Setembro 3165 80230-901 Rebouças, Curitiba-PR, Brazil

Contact: Tel. (+55-41) 3310-4884 Fax. (+55-41) 3310-4445 e-mail: [email protected]

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Affiliation and full postal address: Federal University of Technology - Parana (UTFPR) http://www.utfpr.edu.br Av. 7 de Setembro 3165 80230-901 Rebouças, Curitiba-PR, Brazil

• Corresponding author:

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Sérgio Tadeu de ALMEIDA, MSc Affiliation and full postal address: Federal University of Technology - Parana (UTFPR) http://www.utfpr.edu.br Av. 7 de Setembro 3165 80230-901 Rebouças, Curitiba-PR, Brazil

Contact: Tel. (+55-41) 3053-7043 Cel. (+55-41) 988-085-556 e-mail: [email protected]

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Application of Exergy-based Approach for Implementing Design for Reuse: The Case of Microwave Oven Abstract

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Systematic reuse of parts is not often achievable in a sustainable way. The methods associated with the available disassembly technology rarely addresses non-destructive disassembly approach that favor reuse as an end-of-life (EoL) strategy. In this sense, this paper aims to propose a method able to identify best opportunities and thus, focus on the designers’ efforts during the early stages of product development, with major repercussions during the product EoL. To build the method, the Design Science Research Methodology (DSRM) is adopted. The method scope relies on material and energy flows measured in terms of exergy that ultimately depicts for energy efficiency, environmental impact, cost and technical efficiency. By means of a case study, findings challenge common sense by quantitatively showing how small subsystems with 3% of mass can hold nearly 200 times more embodied exergy than another with 50% of the mass and thus, may greatly affect product design and EoL results. Therefore, the adoption of the method may prove useful for establishing easy-to-use design practices that favor green engineering, circular economy and environmental policies. An exergy-based approach would unbiasedly drive Reuse EoL that facilitate Design for the Environment (DfE) as well as focus efforts on specific disassembly technologies.

Analytical Hierarchy Process After Shredder Residue Cumulative Exergy Extraction from the Natural Environment Cumulative Exergy Demand Design for the Environment Design Science Research Methodology Design Science Research Methodology End of Life Electronic waste Fuzzy Analytical Hierarchy Process Life Cycle Assessment Life Cycle Inventory Printed Circuit Board Reference Product Subdivision or components in a given product Second Law of Thermodynamics

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AHP ASR CEENE CExD DfE DSRM DSRM EoL e-waste FAHP LCA LCI PCB RP SbD SLT

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Keywords – Design for Reuse; End-of-Life strategy; Exergy; Energy Efficiency; Product Design

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1. Introduction

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The product waste has become a global challenge, given that it threatens health, the environment and economies, leading to an unsustainable future (Song et al., 2015). As a result, the environmental aspect of sustainability has received much attention in recent years while materials production and processing, particularly those related to energy, are rapidly becoming critical. In fact, recent projections estimate energy and material demand to double in the next 40 years (Allwood et al., 2011; Schandl et al., 2016). Regarding the economic aspect of sustainability, it became well accepted that the survival of companies relies on aggressive competitiveness (d'Aveni and Gunther, 1995; Snieška, 2015). However, it is equally recognized that the exclusive pursuance for economic advantages will lead to many problems (McGrath, 2013; Porter, 1979). Therefore, a major problem arises when most of the industrialized products succumb to a linear rather than a circular economy, thus configuring a weak sustainability (Ciegis et al., 2015; Pearce and Atkinson, 1993). Ultimately, such convergence of forces is increasing pressures brought to industrial regions to simultaneously achieve dramatic improvements in their environmental and economic performances (Fijał, 2007).

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In order to cope with the problems of waste escalation and material scarcity, several EoL strategies can be adopted. In this sense, the definition of a product reaching its EoL is the moment when a given it no longer satisfies the initial purchaser or first user. This allows for Reuse and Service in addition to Recycling as possible EoL strategies (Rose et al., 2002). According to Fukushige et al. (2012), promising approaches for sustainable development involve product lifecycle systems including EoL strategies, which will drastically reduce environmental loads, material consumption and waste generation, while increasing living and corporate profits.

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The most recognized EoL strategies are Reuse, Service, Remanufacture, Recycling with and without disassembly and Disposal (Rose et al., 2002). Since the term ‘EoL strategy’ was first adopted, there has been a consensus in identifying, defining, classifying and hierarchizing them, based on decreasing environmental impact, where Reuse is suggested, by far, as a priority (Ishii et al., 1994; Luttropp, 1997; Nilsson, 1998; Overby, 1979; Rose et al., 2002; Tani, 1999; Umeda et al., 2012). According to Kerr and Ryan (2001), sustainable production and consumption will only be possible with closed loop systems and, in this sense, there is no doubt that Reuse configures the smallest loop, thus being the most appropriate EoL. Even more, planning for reprocessing is an area of growing importance as Reuse and disassembly are now playing a key role of any design brief (Balkenende and Bakker, 2015; Duflou et al., 2008; Fukushige et al., 2012; Plant et al., 2010). In the context of product design, it is important to note that most products contain a wide variety of materials, combined in sets of high complexity, making their disassembly expensive. Instead, to recover materials, the recyclers usually apply shredding and a myriad of recycling processes (Bakar and Rahimifard, 2008; Favi et al., 2012). However, the efficiency of current recycling technology characterize those processes as industrial processes and thus, also consuming time, materials and energy, yet generating waste (Favi et al., 2016; Lee et al., 2014). That is why those products designed for disassembly and reuse can deliver much greater benefits than those not intended to (Go et al., 2015; Kerr and Ryan, 2001; Kutz, 2007). When designers are properly oriented to apply reuse EoL, they create the incorporation of components reclamation that will support environmental and economic industrial goals (Fitch and Cooper, 2005).

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However, polarized methods and the tradeoff contradictions between economic and ecological priorities seem to result in a trend to abandon non-destructive disassembly, resulting in a paradigm to apply Reuse EoL as a viable mean to achieve a closed loop economy (Duflou et al., 2008).

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In a consistent effort to understand lacks and barriers to the implementation of eco-design methods, Rossi et al. (2016) argue that current tools present limitations and weaknesses, both related to their structure and to their implementation, which can be summarized as follows. (1) The large number of tools with diffuse purposes and outputs, which make it very difficult to select the most suitable one for a company’s specific needs (Araujo, 2001); (2) The overformalization of methods and tools in comparison with the complexity of the product development processes, and the consequently divergence between the academic method and the real industrial and designer’s need (Blessing, 2003; Cross, 2008; Stempfle and Badke-Schaub, 2002; Tukker et al., 2001), which usually works without a life cycle perspective (Mathieux et al., 2002); (3) The need for specific knowledge regarding the tool usage itself and also the necessary skills to interpret the results (Ritzén, 2000); (4) The time-consuming efforts of performing these activities (van Hemel and Cramer, 2002); and (5) The scarcity of financial and personnel resources, which are usually limited, especially in the case of small and medium enterprises (Hillary, 2004).

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As a brief example of complexity, Böhringer and Jochem (2007) have demonstrated that the assessment of the triple bottom line elements of sustainability could be deployed in, at least, eleven indicators. Those indicators will demand more than a thousand variables to be evaluated and may lead to uncertain or wrong results when applied during the early design phase (Medyna et al., 2009a). Thereby, the traditional novel of sustainable indicators, even being indisputably relevant, do not seems to facilitate the assessment of product design for sustainability, as fast and unbiased as necessary (Deng et al., 2006; Kutz, 2007; Medyna et al., 2009a; Rossi et al., 2016).

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Thereby, the brief exposed background allows enquiring how could a new method converge principles and make Reuse EoL more feasible? Looking for an answer the exergy concept is found. It is based on the Second Law of Thermodynamics (SLT), which has been adopted, originally, in the analysis of the energy efficiency of devices or industrial processes in several industries (Borsato, 2017), even considering recycling processes where exergy is suggested as a more agile indicator (Almeida and Borsato, 2017). In addition, several researchers advocate the use of exergy due to its ability to indicate material depletion (Finnveden et al., 2016) as well as challenging undisputed truths taken for granted (Gutowski et al., 2009; Hamut et al., 2014; Ignatenko et al., 2007; Wall and Banhatti, 2013). For instance, manufacturing processes considered modern were revealed less energy efficient than traditional ones (Gutowski et al., 2009) while recycling policies were found out of the boundaries of the SLT (Ignatenko et al., 2007). Thus, exergy-based approaches stand out not only for revealing unknown energy and material inefficiencies and eventually accessing the environmental impact of product design but also for potentially serving as a relevant means when environmental policies are to be established (Medyna et al., 2009a). This paper aims to build, present and evaluate a new exergy-based method for energy efficiency assessment and material depletion aiming to overcome gaps on current attempts. A novel approach combines renewable and non-renewable exergy embodied in material and industrial processes, reliable databases and decision tools to assess the potential for Reuse EoL.

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By applying the method in a case study, the findings revealed faster and unbiased best choices for components to be reused. The results also suggest that with this approach applied during the early design stages of the product design, the Reuse EoL should become feasible and thus more practiced. The paper is organized as follows. In section 2, the DSRM methodology adopted to build the method is briefly described; in section 3 the method itself is built within justified choices; in section 4, the resulting method is presented; in section 5, the method is evaluated by means of a case study while results and implications are discussed; in section 6 discussions and limitations are presented; finally, in section 7, conclusions are presented. Additionally, an appendices section for the Analytical Hierarchy Process (AHP) decision tool is presented in section 8. 2. Methodological Aspects

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To build the proposed method, Design Science Research Methodology (DSRM) is adopted as suggested by Peffers et al. (2007). According to March and Smith (1995), while natural science attempts to understand and explain a phenomenon, DSRM is concerned with developing the ways to achieve objectives, so a given research may be prescriptive as it aims to improve the performance of industrial processes by developing artifacts to achieve those goals. Therefore, a suitable methodology to guide the construction of the method who is the main objective of the present work. Meanwhile, Kroll and Kruchten (2003) define an artifact as “a piece of information generated, altered or used in a process, which will be in this work, characterized as a method”. A DSRM-based process, here adopted to build a new and supposedly improved method, can be implemented within five activities as depicted in Figure 1.

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Figure 1 DSRM process model adapted from Peffers et al. (2007).

As presented in Figure 1, the DSRM activities will provide guidance while defining the problem and its relevance, robustness to the choices that will provide a better solution and finally, practical demonstration followed by a criteria-based evaluation to criticize and refine the new method as it is the main objective. The five DSRM activities will compose the core structure of the presented paper and can be summarized as follows: A1. Problem identification and motivation; A2. Definition of objectives for a solution;

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A3. Design and development; A4. Demonstration; and A5. Evaluation.

As the DSRM activity of motivation (A1) was previously presented inside of the introduction, then, each remaining DSRM’s activity will compose a section where it will be briefly explained, to next be developed in the context of the present research.

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2.1. Solution objectives (A2)

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In order to achieve the main objective of building an improved method, this objective must be deployed in the second level of specific objectives. According to the DSRM’s, it is necessary to infer the objectives of a solution from the problem definition and knowledge of what is possible and feasible. The objectives can be quantitative or qualitative, defining the terms in which a desirable solution would be better than current ones. Moreover, how a new method is expected to support solutions to problems not hitherto addressed (Peffers et al., 2007). Therefore, the established objectives for the new method are:

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O1. Support the environmental and economic aspects of the sustainable triple bottom line with fewer inputs; O2. Reduce time processing by using prepped data as inputs; O3. Reduce assumptions, uncertainties by using pre-validated and robust data inputs; O4. Process the data in a fast and accurate way; O5. The incorporate tools must be widely accepted; O6. Reduce the number of outputs from one to a maximum of four variables; O7. Provide unbiased results, free of interpretation; and O8. Clearly, indicate where the EoL Reuse should be applied. 3. Artifact Design and Development (A3)

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According to the DSRM, the artifact, here in the form of a method, is conceptually a designed object in which the research contribution is embedded. The activity 3 of DSRM includes determining the method’s desired functionality and its architecture delivering the proposed method (Peffers et al., 2007). In this sense, the exergy concept stands out as the core principle of the proposed method. Therefore, its coverage and validity need to be discussed. 3.1. Exergy as an interdisciplinary metric Only in the last years, the exergy concept has gained a more widespread interest in process analysis, typically employed to identify inefficiencies. However, exergy analysis today is implemented far beyond technical analysis; it is also employed in environmental and economic, analyses of industrial systems (Dewulf et al., 2008). By way of definition, exergy is described as the theoretical maximum useful work obtained by a system or a flow of matter or energy is put into the thermodynamic equilibrium with the environment which the system interacts with (Rant, 1956). However, being exergy a principle that can be deployed in different impacts, it is necessary to review concepts and highlight non-trivial relations with the environmental and

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economic pillars of sustainability (Almeida and Borsato, 2017). Next, concepts are described to justify its adoption into the proposed method. 3.1.1. The environmental pillar in exergy

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Several 20th-century scientists such as Lotka, Morovitz, and Prigogine have tried to make use of thermodynamics to explain the most probable behavior of complex natural systems at macroscopic scales. Such work deployed a series of studies reaching the ecosystem exergy concept, which constitutes an attractive proposition, having parallels with other ecosystem theories and showing as varied as promising applications such as land use impact assessment, sustainability evaluation of ecosystem management (Dewulf et al., 2008). This is why many authors suggest that to mitigate the environmental impact of energy resource use, best results are obtained when exergy is used, because it is a useful concept in improving sustainability (Dincer et al., 2014; Dincer and Rosen, 2012; Valero and Valero, 2013; Wall and Banhatti, 2013).

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3.1.2. On the other hand, waste heat emissions to the environment should also be a concern. Once neglected, they can cause the increase of environmental temperature, compromising marine life and the ecological balance of lakes and rivers (Ayres et al., 1998; Cornelissen and Hirs, 2002; Dewulf and Van Langenhove, 2002). Moreover, the environmental temperature increase tends to reduce exergy efficiencies of technical systems used to support human life on the daily bases (Esen et al., 2007). It is suggested that the exergy analysis of the natural processes that occur on Earth could form a basis for an ecologically correct planning, indicating the disturbance caused by changes in a large scale (Tribus and McIrvine, 1971). Moreover, the relations between exergy efficiency and environmental in terms of order destruction, resources degradation and exergetic waste emission suggests an exponential improvement if exergy is properly used (Rosen and Dincer, 1997).The economic pillar in exergy

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When energy loses quality, it is because exergy is being destroyed. Being exergy the useful part of the energy, it has economic value and must be managed (Dincer and Rosen, 2012). In other words, the exergy destruction implies economic waste translated into monetary value, thus, it is directly related to operation costs in industrial processes and the economic sustainability (Georgescu and Roegen, 1971). Other researchers suggest that to calculate the cost of replacing degraded materials during the life cycle in an expression measured in exergy units, thus called exergo-ecological cost. It is worth noticing that the higher the exergo-ecological cost for a product, process or service is, the more unsustainable it will be, also from the economic point of view (Go et al., 2015; Valero, 1998). With a holistic approach, Dewulf and Van Langenhove (2002) recognize that exergy can be considered a production factor proper and that the pro-capite exergy input into a societal system is indeed a measure of its “operational efficiency”. Similarly, the total input of exergy is an econometric measure of the same relevance as the Gross National Product (Wall, 1987, 2002; Wall et al., 1994). In a pragmatic case study, Zambrana et al. (2012) have presented an exergy analysis on a certain industrial process revealing that the real use of exergy was only 4%. Thus, by

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identifying the main instances of waste with the improved energy consumption became 30% lower. Therefore, by the brief literature here scrutinized it is possible to argue that the exergy concept is valid as the core of the method to be built. 3.2. Selection of tools for the proposed method

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Next, the tools selected to be part of the proposed method are described and justified, with respect to the previous objectives and according to the activity two of DSRM. 3.2.1. Tool for exergy calculation

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The literature provides a variety of methods aiming to calculate exergy. However, when looking for consensus, the Cumulative Exergy Demand (CExD) stands out. It was introduced by Bösch et al. (2007), in order to depict total exergy removal from nature to provide a product or a process by summing up all the exergy of the different resources required. CExD assesses the quality of energy demand and includes the exergy of the most relevant energy flows, according to the current technology, as well as of raw materials. Moreover, many authors defend it positively due to the fact that such method extends the classic exergy analysis beyond a single process while other authors highlight its contribution to evaluate goods and services through their exergy consumption (Hau and Bakshi, 2004; Morris, 1991; Szargut and Morris, 1987; Szargut et al., 1987). The CExD method is expressed by the following equation (1):

Portion A

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 = cumulative exergy demand per unit of product or process (MJ-eq)

Portion A of the equation is the exergy share from material flows, where: = mass of material resource i (kg) = exergy per kg of substance i (MJ-eq/kg)

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Portion B of the equation is the exergy share from energy flows, where:

! = amount of energy from energy carrier j (MJ) ",,,,, = exergy to energy ratio of energy carrier j (MJ-eq/MJ)

Exergy is stored in energy sources or material resources in different forms. In the equation, ch, k, p, n, r and t correspond to different sources of energy according to source and current technology available, where:

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#$ = Chemical exergy (applied on all material resources, for biomass, water and fossil fuels, i.e. all materials that are not reference species in the reference state); % = Kinetic exergy (applied on the kinetic energy in wind used to drive a wind generator); & = Potential exergy (applied on potential energy in water used to run a hydroelectric plant); ! = Nuclear exergy (applied on nuclear fuel consumed in fission reactions); " = Radiative exergy (applied on solar radiation impinging on solar panels); and ' = Thermal exergy (applied for geothermal, where heat is withdrawn without matter extraction);

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Therefore, the CExD method was selected since it contains the characteristics already defended in the CExC methodology, besides the main advantages highlighted by Bösch et al. (2007) listed as follows:

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It is a valuable indicator to assess energy and resource demand from the perspective of energetic quality; It is a comprehensive indicator due to the assessment of the quality of energy and the integration of non-energetic resources; It is simpler in the setting up as compared to the resource category of Eco-Indicator 99; and In contrast to Eco-Indicator 99 and CML'01, fewer assumptions are required.

3.2.2. Tool for exergy data inputs

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The calculation by the exergy method is challenging due to the diversity of sources and the respective data required (Bösch et al., 2007; Dewulf et al., 2007; Dewulf et al., 2008; Ignatenko et al., 2007). In a study centered on the analysis of recycling efficiency by means of exergy approach, Ignatenko et al. (2007) already alert to the unavailability of robust data and the limitations to which the results of a given research would face. Aiming to remediate this gap, several authors advocate the inclusion of data to calculate exergy in extensive, mature and already consolidated databases of Life Cycle Assessment (LCA) (Balkenende and Bakker, 2015; Bösch et al., 2007; Dewulf et al., 2007; Frischknecht et al., 2007; Hamut et al., 2014; Rose and Stevels, 2001). Along with this line, Bösch et al. (2007) carried out an extensive work in order to enable the use of CExD indicators by integrating the exergy factors in LCA inventory database (LCI), which was applied over the Ecoinvent (2016). In addition, Bösch et al. (2007) state that making the large body of LCI available to communities outside the LCA framework may be of great usefulness, as exergy data is currently not nearly as readily available as LCI data. Therefore, the choice of the Ecoinvent (2016) database is perfectly justified. 3.2.3. Tool for use and calculation of exergy data The wide range of data required and consequently, the high computational burden for the exergy approach commonly presses researchers to use support tools in the form of software. A non-exhaustive research highlights the following:

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FactSage 5.0 (2002); identifies in the works of Ignatenko et al. (2007) in the evaluation of resource efficiency in recycling systems and Reuter et al. (2006) for determining limits for the recycling of End-of-life vehicles; Aspen Plus® (2011); identifies in the works of Querol et al. (2011) applying exergy in the economic process analysis, Bram and De Ruyck (1997) in the thermodynamic design of an evaporator and Dincer et al. (2014) in the exergetic evaluation of hydrogen production system; Seminar on the conceptualization and application of exergy and Alves (2004) in the optimization of a power plant; and SimaPro (2016); identifies in the works of Hamut et al. (2014), in the exergy analysis of an electric hybrid vehicle, Medyna et al. (2009b) in the exergy approach in the initial phase of product design, Kara and Ibbotson (2011) incorporate energy from manufacturing supply chains and Paraskevas et al. (2015) in the evaluation of aluminum recycling.

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3.2.4. Tool for decision making

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However, it is important to note that the data embodied in such tools are not always representative of real industry situations and carry too many assumptions (Duflou et al., 2012). In this sense, SimaPro (2016) presents crucial advantages. It fills the data gap by incorporating the already validated database of Ecoinvent (2016) and includes CExD among several other LCA methods, As such, it plays a key role as it allows the link between the database and the CExD method. Thus, SimaPro proves to be the most promising tool for applying CExD in a quick and reliable way and therefore has been selected as part of the development method.

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Over the last twenty years, tools for decision support and trade-off analyses for sustainable product design have been poorly established (Bras, 1997). Many developments in this domain are concerned with the trade-off of sustainability, such as between environmental impact and financial costs/profits (Barbera and McConnell, 1990; Todd, 1994). The current scenario does not seem to be different since many researches are still investigating ways to overcome difficulties and balance sustainable trade-offs (Chang et al., 2014; Fiksel et al., 2014; Rossi et al., 2016; Valentinov, 2014).

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Regarding the source, the SLT is able to evaluate all energy conversion and material flows quantitatively (i.e. Joules) and qualitatively (i.e. "renewable" or "non-renewable" sources) (Dincer and Rosen, 2012; Szargut, 2005). This fact may result in difficult and not free-of-bias judgments over the exergy outcomes. On the other hand, multi-criteria decision-making is a branch of research that proposes decision methods able to cope with both, quantitative and qualitative criteria (Cavallaro, 2005; Wang et al., 2008; Yu et al., 2000). The Analytical Hierarchy Process (AHP), as proposed by Saaty (1980), provides a methodology for measuring the performance such data by decomposing a complex decision into a hierarchy. Its validity is based on thousands of real applications (Aragonés-Beltrán et al., 2010; Begić and Afgan, 2007; Szczypińska and Piotrowski, 2008; Tzeng et al., 2002). In classical AHP, there are two ways of synthesizing the priorities of the alternatives: the distributive and the ideal approaches. The ideal approach compares each performance score to a fixed benchmark such as the performance of the best alternative under that criterion. This mode should be used when the decision maker is concerned with how well each alternative performs relative to a fixed benchmark (Saaty and Vargas, 2012). In the distributive approach, the

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weights of the alternatives add up to 1, is adopted when there is dependence among the alternatives and when the objective is to choose an alternative that is better in relation to others (Saaty, 1994). Therefore, the distributive mode is the one adopted and more suitable for the purposes of this method.

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Several studies show that in AHP the greater the number of comparison criteria and the dependence between alternatives are, the greater the tendency to inconsistency in human judgment will be. Therefore, seven criteria would be recommended, but nine criteria could exceptionally be used, but not exceeded (Miller, 1956; Saaty and Ozdemir, 2003; Triantaphyllou and Mann, 1995).

Nonrenewable, fossil; Nonrenewable, nuclear; Renewable, kinetic; Renewable, solar; Renewable, potential; Nonrenewable, primary; Renewable, biomass; Renewable, water; Nonrenewable, metals; and Nonrenewable, minerals.

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On the other hand, the SimaPro (2016) software can output the impact category indicator into the resource categories as follows (PRé, 2014):

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According to Maes and Van Passel (2014), to perform a correct interpretation by exergy approach, it is necessary to review both, the total input and the renewable fraction in conjunction. The renewable fraction of input is an important indicator for the environmental sustainability of the process because this information gets lost when the total exergy input is determined by adding all resources and impacts together (Dewulf et al., 2000; Lems et al., 2003; Sewalt et al., 2001). Therefore, in respect to the AHP criteria limitations and the method objectives regarding simplicity and usability, the outputs have been reduced by grouping them in only three categories, as follows: renewable, not renewable and the total amount of exergy.

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The AHP fuzzy version (FAHP) was also considered in the present work, given its capacity to incorporate the imprecision of human thought (Salaheldin, 2009). However, FAHP was later discarded because, despite its popularity, some authors argue that the results are often the same (Rao, 2007; Salaheldin, 2009). Therefore, the method here proposed would not benefit from the adoption of FAHP due to the increment of complexity, thus hindering its practicality and agility. In the next section, AHP is described briefly and more pragmatically, in the form of steps, as proposed by Saaty and Vargas (2012). It's worth noting that the scientific literature presents plenty of AHP application examples in which Saaty (1990), Saaty and Vargas (2012), Triantaphyllou and Mann (1995) and Voogd (1982) stand out. 3.2.5. Tool for relevant selection Even with the AHP results properly ranking the subassemblies and overcoming the tradeoff difficulties, the lack of support for final and relevant selection to focus on still remains.

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Many researchers have observed the existence of the phenomenon of vital few and trivial many to their fields (Juran, 2005). Pareto (1896) observed this as applied to the distribution of wealth, and advanced the theory of a logarithmic law of income distribution to fit the phenomenon. Lorenz (1905) developed a form of cumulative curve to depict the distribution of wealth graphically. Juran (1951) was one of the first to identify the phenomenon of the vital few and trivial many as universal and applicable to many fields.

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Given the AHP results, a cut-off value for the most important subassemblies was established from the generalization Juran and Godfrey (1999) pp. 5.20-24 made for the Pareto (1896) postulate. Contextualizing this theorem to the proposed method, it means that if the method selects the minority of the most relevant subassemblies, they represent the majority of the exergy relevant and present in the end-of-life product.

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4. Artifact Demonstration (A4)

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This section presents the first result of the present paper, it means, the proposed method itself. However, as the main contribution is obviously the method's results, its instantiation will be performed by means of a simulated case study, according to the adopted DSRM procedures. Next, the steps for applying the method are detailed in order to effectively demonstrate how it works. 4.1. The RIPEx method

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The method has been named with the acronym "RIPEx", making allusions to the acronym R.I.P. (“Rest in Peace”) and in connotation to the term "ripe", referring to a fruit developed to the point of readiness for harvesting and eating. For last, the suffix "Ex" concerns exergy. Figure 2 presents a flowchart of the RIPEx method.

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Decompose into components or subfunctions


Disassembly and separate

Design for Disassembly

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Hazardous substances

Specific treatment

Subassembly 1

Subassembly 2

Subassembly 3

Subassembly 4

∑ Exergy 1

∑ Exergy 2

∑ Exergy 3

∑ Exergy 4

Disassembly for Reuse

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Figure 2. Flowchart of the RIPEx method.

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Next, the methods and the choices leading to the reference product (RP) to be used as a case study are presented in order to ensure it as a valid mean for demonstrating the RIPEx method in accordance with DSRM procedures.

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The composition of industrialized products is very heterogeneous, given the multitude of input materials (Mirabile et al., 2002). Therefore, to perform a proper evaluation of the proposed method, it is necessary that the RP, adopted as the object of analysis, be representative in terms of material diversity. It means that it must be average in size, mass and composition when compared to the overall industrialized products reaching the EoL. Furthermore, it shall be sufficiently complex in terms of subsystems, subassemblies, subdivisions or components (SbD), thus, allowing the analysis of different EoL strategies, such as Reuse. In order to validate the RP choice, a comparison assessment was made among the most prominent streams of products residues, identified in the literature as automotive and electronic waste, thereof well exemplified as white goods and electronic appliances (Balkenende and Bakker, 2015; Devoldere et al., 2009; Menikpura et al., 2014; Mirabile et al., 2002). Based on Mirabile et al. (2002), the average composition of an EoL motor vehicle was extracted, as well as its proposed SbD into classes of materials was adopted. Next, based on the work from Balkenende and Bakker (2015) on the treatment of electronic EoL products, the average composition of a liquid crystal display (LCD) monitor was retrieved. Then, an arithmetic mean was calculated and used as a parameter for RP selection.

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The RP identified as representative was a microwave oven of brand and model widely present in the market. The results of the analysis are summarized in terms of mass composition in Figure 4 and compared with other relevant waste streams in Figure 3.

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Figure 3. Comparative analysis of percentage composition by mass.

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The analyses revealed that the adopted RP contains the typical materials and reasonably surrounds the main sources of residues. It is worth noting that its percentage of electronic composition is actually higher. However, this characteristic is beneficial to the study, since it corroborates with the tendency to increase its participation, as a result of the widespread adoption of electronics in consumer products and the accelerated technological changes in the current dynamic environment (Menikpura et al., 2014). Moreover, regarding the European standards for electronic waste (WEEE), the microwave oven as the RP seems to be appropriate. According to the standard, in its annexes 1, 2 and 3, it is classified as a relevant flow of Category 1: "large appliances" (> 50 cm in any external dimension) and establishes, among all categories, the most demanding in terms of collection and treatment, i.e. 80% of its total mass should be prepared for reuse and/or recycling (Directive 2012/19/EU, 2012).

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ceramics and eletrics 1.505 kg coating 0.016 kg

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Figure 4. Mass composition of material classes in the RP microwave oven.

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As depicted in Figure 4, some categories of mass proposed by Mirabile et al. (2002) are, in the case of the microwave-oven, near to paltry. It is the case of coatings, elastomers and fabrics that are, together with the category of others, still lower than 2%. Furthermore, the category of others in the case study is mainly composed of minerals such as magnetic ferrites and composites like carbon fibers or the electronic Printed Circuit Board (PCB), the latter being usually a combination of soldermask, silkscreen, cooper and a polymeric substrate such as composite epoxy material. 4.3. Demonstration of the RIPEx method (case study)

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Next, in a case study using the selected RP, the RIPEx method is applied in the form of execution steps.

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4.3.1. Step 1: Product decomposition As the method aims to support the early stages of design, it is expected that information regarding components, materials, manufacturing processes and functional SbD to be known or sufficiently estimated. However, this assumption may not guarantee two important aspects, as follows: -

Consistent physical and functional product decomposition, in SbDs, in which Pahl et al. (2007) could be used as a useful reference to; and With the above, to promote markets for reuse as EoL strategy (Gehin et al., 2008; Kwak and Kim, 2015; Lee et al., 2012; Nnorom et al., 2009).

Therefore, product decomposition, which configures step one, plays a key role and must be carefully managed, to make the definition of SbDs for reuse feasible.

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Once the SbDs are defined, step one is concluded with the PR structure, including all components and respective subsystems depicted in material, mass and manufacturing processes registered in a specific field in SimaPro (2016) and, for each one of them, a respective dataset properly selected from the Ecoinvent (2016) database.

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Regarding the microwave oven, the analysis resulted in 348 components, 12.094 kg and 7 SbDs. Figure 5 illustrates the components, their complexity and the seven SbDs.

Figure 5. A microwave oven as a reference product (RP).

4.3.2. Step 2: Product depollution

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According to Fijał (2007), the assessment of hazards is frequently neglected in many methods. Therefore, it is included here at the very beginning.

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Regarding the operation, depollution is mainly performed manually. On the other hand, those SbDs which are not dismantled (i.e. for reuse) will be totally or partially shredded (Hischier et al., 2007). In this sense, post-shredder technologies have been developed, designed, and tested to treat shredder residue (ASR) material after depollution, dismantling, and shredding (Buekens and Zhou, 2014). Thus, depollution must be performed in advance. That is why most of the legislation, intended to manage important waste streams such as EoL vehicles and e-waste, impose depollution as described, thus it must not be neglected (Directive 2000/53/EC, 2012; Directive 2012/19/EU, 2012). 4.3.3. Step 3: Embodied exergy calculation Using SimaPro (2016), the RP was modeled and the exergy method CExD applied. As a result, the embodied exergy to each pre-established SbD was obtained, by retrieving information about the materials and the manufacturing processes. As defined previously, ten

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characterization factors were summed and then, if applicable, reduced to exergy of renewable or non-renewable sources. The first relevant results of the RIPEx method are then presented in Figure 6.

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Figure 6. Embodied exergy and mass.

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As depicted in Figure 6, the seven pre-established SbDs are distributed in descending order in terms of total embodied exergy. In each column, it is possible to observe four evaluation criteria: in light grey, the exergy portion from non-renewable sources; in dark grey, the portion of exergy from renewable sources; above each column, in black, the total exergy from renewable and non-renewable sources; and in the central column, pattern filled, mass composition (in kg) of the materials of each SbD, according to the scale to the right. Based on the results and given the large variations, it is possible to infer that decision-making is not trivial. If compared, for example, SbDs 5 and 1, it can be observed that, in terms of total exergy, SdD 5 is the fifth largest, even though it is 194 times lower than SbD 1. However, in terms of mass, SbD 5 is the largest holding about 50% of RP, even though SbD 1 is more than twenty times lower than SbD 5. Therefore, demonstrating that higher mass of a SbD does not means, necessarily, more exergy. Moreover, in terms of exergy from renewable sources, with the exception of SbD 4, the SbDs appear to follow a certain proportion of the non-renewable exergy, so the order suggested by the amount of total exergy would not change. Thereby, it is evidenced that variables mass and exergy alone, cannot provide a clear decision. At this point,

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the case study demonstrated that a tool for decision support, such as AHP, is necessary to allow decisions that consider the four variables here described in an unequivocal way. 4.3.4. Step 4: AHP-based decision-making During this section, the main findings revealed by AHP applied to the case study will be presented. It is worth noticing that operational details for each of the four AHP steps can be found in the appendices section.

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The first result of applying the AHP aiming Reuse EoL, consist in determine the weight of the variables or attributes, adopted as evaluation criteria. Also known as the eigenvector, it expresses the relative importance of each one of the judgment criteria, in relation to the others (Saaty, 1994). Therefore, Figure 7 presents the second relevant results of the RIPEx method.

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Figure 7. AHP weight of the four adopted criteria.

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As shown in 7, the total exergy (C4) is, based on the SLT, results as the most important criteria as it represents the total amount of destroyed exergy and cannot be recovered (Dincer and Rosen, 2012). Yet, it represents a waste of material, energy and economic value (Jawad et al., 2015; Wall and Banhatti, 2013). Next, the exergy coming from non-renewable sources (C1) raises as the second more important due to its inability of been renewed in any sense, thus representing finite resources (Valero and Valero, 2010). Then, the mass itself (C3) is found as it represents the materialization of waste and direct representation of material depletion (Valero and Valero, 2013). Moreover, being a physical waste, it will require future energy to be treated in recycling processes implying in additional exergy destruction (Ignatenko et al., 2007). For last, the exergy from renewable sources (C2), even being destroyed, is coming from renewable sources and thus can be replaced (Bösch et al., 2007). Once the four criteria are prioritized, next these weights are to be used to evaluate all the SbDs against each other based on their content of the four criteria. Therefore, the RIPEX method reaches its third important results by applying the embedded the AHP judgment tool. Thus, the quantitative prioritization of the SbDs is found as illustrated in the columns of Figure 8.

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Figure 8. AHP prioritization combined with Pareto principle.

As depicted in Figure 8, the seven SbDs are ordered in terms of priority, from highest to lowest, according to the AHP results. One of the main findings is the discrepancy, in terms of mass as well as exergy, previously found in Figure 6 was dispersed. Now with a single and comprehensible percentage scale, became clear the importance of each SbD while aiming Reuse EoL.

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However, it still not clear what are the SbDs to be selected for reuse. Therefore, adopting the generalization Juran and Godfrey (1999) pp. 5.20-24 made for the Pareto (1896) postulate, the principle of 80/20 is then applied as final criteria, and SbDs 4 and 5 are thus, selected for the Reuse EoL strategy and then characterizing RIPEx’s ultimate result.

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5. Method evaluation (A5)

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According to the DSRM, the method’s results are to be observed and measured as to how well it supports the solution to the problem yet under specific criteria (Peffers et al., 2007). The goal of introducing new tools should be to provide efficient and effective decision support in sustainably conscious product design for reuse. In order to meet this goal, decision support tools should ideally have seven characteristics (Hrinyak et al., 1996). Therefore, each one of the seven characteristics adopted from Hrinyak et al. (1996) is underlined to then, qualitatively evaluate the RIPEx method in its structure and results 1- Simple (i.e. easy to use). From a modeling perspective, due to de maturity of the incorporated tools, RIPEx is not very complex. However, for complex products with hundreds or thousands of components, some complexity of modeling is unavoidable while the other steps should consume the same effort. 2- Easily obtainable (i.e. available at a reasonable cost). The access to SimaPro (2016), which includes the exergy database of Ecoinvent (2016), with non-research purposes will demand a relatively low investment. Therefore, the initial cost of using RIPEx is not zero. However, since the product-modelling data can often be reused, the cost of using RIPEx quickly drops after subsequent applications. Regarding the other

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embedded tools, they are quite simple and there are plenty of examples in the literature. Furthermore, there are many free software suites available on the internet. Precisely definable (i.e. clear how information can be evaluated). From a design perspective, using RIPEx is straightforward. As design work reaches the moment when SbDs are asserted, needed information can be precisely collected for method application. Objective (i.e. two or more qualified observers should arrive at the same result). There is no space for ambiguity. It is likely that two separate teams will provide similar or identical evaluations of a new design. This is clearly a positive aspect of RIPEx. However, since the method does provide some flexibility by accommodating the SbDs, some variation in results can be expected. On the other hand, as RIPEx prescribes using the most accurate tools, data, and judgment constraints for a given situation, as the method is inherently assertive. Valid (i.e. should measure, indicate, or predict correctly, what it is intended to measure, indicate, or predict). The validity of RIPEx relies on exergy-driven assessments. However, the uncertainty of data for calculation depends on the maturity of the assessments derived from LCI. Robust (i.e. should be relatively insensitive to changes in the domain of application). As demonstrated during the case study, RIPEx is robust to pinpoint, from a range of design alternatives, those SbDs that are clearly more relevant in terms of exergy and its deployments. However, as the approach here proposed seems to be new, the full robustness of the method is yet to be tested for a wide variety of products, which is left for future research due to its too extensive content for a single work. Enhancement of understanding and prediction (i.e. good metrics, models, and decision support tools should foster insight and assist in predicting process and product parameters). The case study presented demonstrates how RIPEx enhances understanding and identification of relevant items and thus, focus on early design activities.

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6. Discussion and limitations

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Design plays a key role in determining economic and environmental benefits of products. Thus, products designed for disassembly and reuse can deliver much greater benefits (Kerr and Ryan, 2001). However, researchers and developers should improve tool characteristics and develop methods that meet company needs and expectations and prevail over barriers (Rossi et al., 2016). In order to compare the results from this paper to that from similar studies yet under relevant criteria, it was adopted those indicated by Rossi et al. (2016), from an extensive literature review of ecodesign methods and tools. In this sense, the proposed exergy-based method for implementing a design for reuse satisfy many of the eco-design methods improvements, pointed by current literature, such as: -

It can be integrated with traditional tools used inside companies and properly solve trade-off situations, allowing to integrate different Design for X tools; It is possible to integrate qualitative and quantitative results with an analyst module to quantify impact;

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It allows to model the product structure in compliance with ISO 14040 (2006) to facilitate product modeling and reuse of information if a successive LCA analysis is conducted; There is no need for experience to interpret results, nor to insert user arbitrary estimations; Possibility to perform a preliminary evaluation of product-related matters by choosing the simplification level according to the available data. From exergy, it links to economic aspects to allow companies to consider cost drivers (Schmidt, 2002); Simple to be used also by non-expert users following a short training on SimaPro (2016) tool (Rebitzer et al., 2004); and Most incorporated tools are free and ease to access (Santolaria et al., 2011).

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Additionally, the method allows identifying not only those SbDs suitable for Reuse EoL. Once determined their exergy composition and weight of importance, the SbDs themselves may become the main subject of study using exactly the same method, however with the goal of optimization aiming cost or environmental impact reductions. In this sense, the case study reveals that the components with the highest embedded exergy are electrolytic capacitors, in special those where tantalum material. Furthermore, together these capacitors hold, among the selected SbDs for Reuse, more than 80% of total exergy with only 0,067 kg of mass. Thus, these components are the main responsible for the weight attributed to the selected SbDs and may become a target of an investigation.

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On the other hand, it seems worthwhile to consider the main limitations of the complexitysustainability trade-off. The exergy-based environmental impact measures are not straightforward, due to both technical and theoretical limitations. Various different applications related to the exergy concept exist, but not all are appropriate within the framework of sustainability assessment (Sewalt et al., 2001). The exergy-based approach is limited to ecosystem services that concern material and energy flows. Other ecosystem services, such as noise reduction or social values, are immaterial, and cannot be objectively assessed either with energy, carbon or exergy-based metrics (Maes and Van Passel, 2014).

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Regarding the exchanges with ecosystems, exergy cost is an appropriate and objective measure (Valero, 2006). However, for the exchanges involving human preferences and choices, other solutions have to be pursued (Maes and Van Passel, 2014). Finally, in terms of the embedded exergy method, CExD has been improved to a new version called Cumulative Exergy Extraction from the Natural Environment (CEENE), in order to overcome some limitations and consider water, atmospheric resources, and land use. CEENE has also been developed to be compatible with the Ecoinvent (2016) life cycle database (Dewulf et al., 2007). However, the method is still not included in a software package that can facilitate its application, thus, cannot be considered now, even though it may represent an evolution of the RIPEx in a foreseeable future.

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7. Conclusion

The new approach here proposed attempts to simplify, integrate, depict and effectively assess energy and technical efficiency during early product design phases, with the potential to be included in traditional design processes.

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According to the evaluation conducted, the RIPEx method makes it feasible to consider Reuse EoL strategies during product design. The method combines LCA with exergy concepts in a systematic, yet adaptive manner that allows robust identification and support decision that properly overcomes trade off difficulties. In addition, it allows to clearly identify where a reuse EoL strategy should be conducted as well as the SbDs relative importance against each other. Consequently, designers can be driven to apply different Design for X tools (i.e. design for collection, disassembly & assembly, upgrade, reliability, modularity and maintainability) within the SbDs of a product structure, not only aiming for Reuse but also each technique to be used on different SbDs to reach the Reuse of the selected ones.

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Furthermore, the market for green products has been increasing in the last years. Environmental aspects are no longer a burden for companies but represent added value. When multiple SbDs with different life cycle implementations can be successfully created, economically competitive EoL treatments will be promoted and thus reduce the negative environmental impacts of discarded products. Finally, the method is aligned with the action plan suggested by the European Commission (2016) for Circular Economy, which includes actions on product design, reuse and repair of products, recycling levels, sustainable consumption, waste policy, raw materials usage, markets for secondary raw materials and specific sectorial metrics.

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Therefore, it is identified as a research opportunity, to extend the support tool for the other EoL strategies and its scenarios, thus helping not only designers but also recyclers to reduce the environmental impact of products through multiple EoL strategies. Yet and regarding the case study, as the tantalum capacitors were revealed as a key component with an important difference on the relation of mass and embedded exergy, they could be a subject of research aiming to reduce environmental impact or cost, thus driving material substitution or process improvements.

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Acknowledgements

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8. Appendices

This section the AHP is described briefly and more pragmatically, in the form of four steps adapted from Saaty and Vargas (2012).

Figure 9. AHP hierarchical levels distribution.

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AHP step 1: Determine the purpose of the decision and the weight of the variables or attributes, adopted as evaluation criteria. For such, the first step consists in to establish a hierarchical distribution in levels. Figure 9 illustrate the procedure.

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As shown in Figure 9, the first level corresponds to the objective of the decision, in other words, aims the Reuse EoL. The second level presents the selection criteria. In the case study, it means: (C1) exergy from non-renewable sources, (C2) exergy from renewable sources, (C3) material mass and (C4) total embodied exergy. The third level presents the seven SbDs identified in the RP of the case study, thus all possible alternatives.

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AHP step 2: Build the judgment matrix that will lead to the priority vector, also known as the eigenvector, which consists of a matrix A that expresses the relative importance of each one of the judgment criteria, in relation to the others Saaty and Vargas (2012).

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According to Saaty and Vargas (2012), the pairwise comparisons assume that the decision maker can compare any pairs of elements  ,  at the same level of hierarchy and provide a numerical value *  for the relation of their importance. If element  is preferred over  then * > 1. Correspondingly, given the property of reciprocity, *  1/*  , where *  > 0, for  1,2, … , ! and  = 1,2, … , !. Each set of comparisons for a level with ! elements requires !(! − 1)/2 comparisons, which are used to build the positive reciprocal matrix of pairs of comparisons 2  [*  ], according to Eq. (2).

1 * 2  5 76 ⋮ *6

*67 1 ⋮ *7

⋯ ⋯ ⋱ ⋯

*6 *7 ; ⋮ 1

(2)

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Therefore, to determine the relative importance of the different criteria, a peer comparison hierarchy is used to assign relative weights to the criteria, by means of their comparison in pairs. For this, a value of an importance scale is adopted, in order to express the relative importance of one criterion in relation to the other, according to Table 1 (Saaty, 1980; Saaty and Vargas, 2012).

Table 1 The fundamental scale. Definition Relative importance

9

Equal importance Moderate importance Strong importance Very strong or demonstrated importance Extreme importance

2,4,6 and 8 Adapted from Saaty and Vargas (2012).

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1 3 5 7

Two activities contribute equally to the objective. Experience and judgment slightly favor one activity over another. Experience and judgment strongly favor one activity over another. An activity is favored very strongly over another; its dominance demonstrated in practice. The evidence favoring one activity over another is of the highest possible order of affirmation. Intermediate values.

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(< )

Explanation

=> =? =@ =A => B B/C B B/C =? C B D B/D 5 ; =@ B B/D B B/E =A C D E B

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2 =

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Resuming to the case study, to build elements * of Eq. (2) and properly justify them, the question to be answered, using the values of Table 1 , is: “When looking for Reuse, how many times is criterion
more important, compared to criterion
 , and so on?”. Next, each column is summed, as shown in Eq. (3).

∑G 6H IJ/76 6K 6LH/6KM

(3)

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According to Saaty (1994), as the adopted mode of the AHP is distributive, each * must be divided by the result of the sum of its respective column in Eq. (3), thus obtaining the relative and normalized weight, where the sum of each column becomes equal to 1, as shown in Eq. (4)

2 

=> =? => B /BN OB/NEP =? C/BN OB/RS 5 =@ B/BN OB/OPO =A C /BN ND/RS

=@ =A B/BQ BQE/BODO D/BQ BQE/EOP ; B/BQ BQE/PPQ E/BQ BQE/BCN

∑G 6 6 6 6

(4)

Next, the calculation of the vector of priorities <  <6 , <7 , … . . , < )U can be derived from the matrix of pairs of comparisons by the traditional method proposed by Saaty (1980). According to Saaty and Vargas (2012), a simple way to get an approximation is to normalize the elements

31

in each column of the judgment matrix 2 in Eq. (4) and then calculate the mean on each row, as shown in Eq. (5).

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<=

0,0699 => ≅ ^. _% 1 /16 21/658 1/10 105/1232 7/16 21/94 3/10 105/528 0,2899 =? ≅ ?a. _% ; 5 ;5 1/16 21/282 1/10 105/880 0,0891 =@ ≅ b. a% 7 /16 63/94 5/10 105/176 0,5511 =A ≅ cc. >%

(5)

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Based on the AHP results in Eq. (5), regarding Reuse EoL, the four criteria are then prioritized, in terms of percentage of importance. These weights are to be used next to evaluate all the possible choices against each other, based on the four criteria. AHP step 3: Calculate the consistency of the answers.

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According to Saaty (1980), it is necessary to verify the consistency of the answers that led to the prioritization. The first indicator to be analyzed is the consistency index (CI), as shown in Eq. 6.
d  λfg − n/n − 1

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6

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In order to calculate CI, it is necessary to find the value called the maximum eigenvalue λfg . It is obtained from the sum of the products between each element of the priority vector < in Eq. (5) and the sum of the columns of the non-normalized reciprocal comparison matrix in Eq. (3). Using the numerical example of the case study, the maximum eigenvalue λfg can be found by approximation, as shown in Eq. (7) λfg  160.0699 

94 176 0.2899  100.0891  0.5511  4.231 21 105

(7)

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Next, to calculate CI, Eq. 6 is applied with the λfg found, according to Eq. (8).
d  4.231 − 4/4 − 1 = 0.077

(8)

According to Saaty (1980), if CI<0.1, that is, if the error is less than 10%, then there is enough consistency to proceed with the AHP calculations. If not, the judgments of the reciprocal comparison matrix shall be redone. The second indicator to be analyzed is the consistency ratio (CR). A measure of consistency can be estimated by comparing CI with the same calculated ratio of a reciprocal matrix of judgments, randomly generated and of the same order (Saaty and González, 1991). As the matrix here is of 4th order, the CR is then calculated by approximation according to Eq. 9 and numerically exemplified in Eq. 10. A consistency ratio of up to 0.10 is considered acceptable and the RI index, for matrices of order 1 to 15, is available according to Table 2.

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i =


i =

K.KLL

5 1.12

6 1.24

K.I


d id

(9)

= 0.0855 (< 10%, thus consistent).

Order 1 2 3 4 RI 0 0 0.58 0.9 Adapted from Saaty (1980).

7 1.32

8 1.41

9 1.45

RI PT

Table 2 – Random indexes.

(10)

10 1.49

11 1.51

12 1.48

13 1.56

14 1.57

15 1.59

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The results of the application of the AHP method in the case study, for the second level shown in Figure 9, are summarized in Table 3.

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Table 3 – Hierarchy of the selected criteria for the AHP aiming Reuse EoL. Criteria

Description

C1 C2 C3 C4

Exergy from renewable sources Exergy from non-renewable sources Mass Total exergy

Weight (s) 7.0% 29.0% 8.9% 55.1%

Hierarchy 4 2 3 1

λmax 4.231

Consistency CI CR 0.077

0.056

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AHP step 4: Calculate the weight of each possible alternative. Next, the AHP process in three steps is going be repeated. However, the objective will be each criterion in level 2. Thus, by comparing pairs all the seven SbDs alternatives on level 3, from the perspective of each one of the four criterion.

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Therefore, the comparison matrix of criterion C1 "exergy from non-renewable sources" is obtained according to Eq. (11).

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1 v1/3 u 1/9 u 2 = u1/9 u1/9 u1/9 t1/9

3 1 1/9 1/9 1/9 1/9 1/9

9 9 9 9 9 9 9 9 1 2 2 3 1/2 1 1 2 1/2 1 1 2 1/3 1/2 1/2 1 1/5 1/3 1/3 1/2

9 9y x 5 x 3x 3x 2x 1w

(11)

The AHP process is repeated and the results for each SbD under criterion C1 are presented in Table 4.

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Table 4 – SbDs under criteria C1 "exergy from non-renewable sources". Description

SbD 5 SbD 4 SbD 7 SbD 6 SbD 1 SbD 2 SbD 3

Inverter board Control panel board High voltage transformer Magnetron Mechanic parts Door components Control panel mechanicals

Weight (s) 45.4% 32.7% 7.3% 4.6% 4.6% 3.1% 2.3%

Position 1 2 3 4 4 6 7

Consistency λmax CI CR

7.509

0.08

0.063

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Alternative

The comparison matrix of criterion C2 "exergy from renewable sources" is obtained according to Eq. (12). 2 3 2 3 4 3 1 1 1 1 1 1 1 1 1 1/4 1/3 1/3 1/6 1/4 1/5

8 9 4 3 3 1 1

9 9y x 6 x 4x 5x 1x 1w

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1/2 1 1/3 1/4 1/3 1/9 1/9

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1 v 2 u 1/2 u 2 = u1/3 u1/2 u1/8 t1/9

(12)

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The AHP process is repeated and the results for each SbDs under criterion C2 are presented in Table 5.

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SbD 5 SbD 4 SbD 7 SbD 6 SbD 1 SbD 2 SbD 3

Inverter board Control panel board High voltage transformer Magnetron Mechanic parts Door components Control panel mechanicals

Weight (s) 24.3% 35.5% 12.4% 10.2% 11.6% 3.3% 2.8%

Position 2 1 3 5 4 6 7

Consistency λmax CI CR

7.084

0.01

0.01

RI PT

Alternative

The comparison matrix of criterion C3 “material mass" is obtained according to Eq. (13). 1/9 3 1 1 1 1/3 1/5

1 9 4 3 3 1 1

1 9y x 6 x 4x 5x 1x 1w

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1/2 1/8 1/6 1 2 4 1/2 1 1 1/4 1 1 1/3 1 1 1/9 1/4 1/3 1/9 1/6 1/4

(13)

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1 v2 u 8 u 2 = u2 u9 u1 t1

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The AHP process is repeated and the results for each SbD under criterion C3 are presented in Table 6. Table 6 – Hierarchy process of SbDs aiming the criteria C3 "material mass". Description

SbD 5 SbD 4 SbD 7 SbD 6 SbD 1 SbD 2 SbD 3

Inverter board Control panel board High voltage transformer Magnetron Mechanic parts Door components Control panel mechanicals

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Alternative

Weight s 5.4% 36.4% 19.4% 12.6% 18.4% 4.2% 3.5%

Position 5 1 2 4 3 6 7

Consistency λmax CI CR

7.628

0.10

0.078

The comparison matrix of criterion C4 “total exergy" is obtained according to Eq. (14). 1 v1/3 u 1/9 u 2  u1/9 u1/9 u1/9 t1/9

3 1 1/9 1/9 1/9 1/9 1/9

9 9 9 9 9 9 9 9 1 2 2 3 1/2 1 1 2 1/2 1 1 2 1/3 1/2 1/2 1 1/5 1/3 1/3 1/2

9 9y x 5 x 3x 3x 2x 1w

14)

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The AHP process is repeated and the results for each SbD under criterion C4 are presented in Table 7.

Table 7 – Hierarchy process of SbDs aiming the criteria C4 “total exergy ". Description

SbD 5 SbD 4 SbD 7 SbD 6 SbD 1 SbD 2 SbD 3

Inverter board Control panel board High voltage transformer Magnetron Mechanic parts Door components Control panel mechanicals

Weight (s) 45.4% 32.7% 7.3% 4.6% 4.6% 3.1% 2.3%

Position 1 2 3 4 4 6 7

Consistency λmax CI CR

RI PT

Alternative

7.509

0.08

0.063

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AHP step 5: At this point, it is necessary to compute the overall composite weight of each alternative. The overall weight is just a normalization of linear combination obtained from the multiplication between the weight of each SbD alternative and priority vector of <. Table 8 presents the procedure and the results for each SbD and its composite weight of importance. Table 8 – Overall composite weight of alternatives. Criteria

C1 C2 C3 C4

Priority SbD vector 5 (<) (<)

SbD 4 (<)

=

=

SbD 7 (<)

=

SbD 1 (<)

=

SbD 6 (<)

=

SbD 2 (<)

=

SbD 3 (<)

=

28.99% 0.454 0.1316 0.327 0.09469 0.073 0.02129 0.046 0.01336 0.046 0.01336 0.031 0.00903 0.023 0.00658 0.243 0.01696 0.355 0.02481 0.124 0.00868 0.116 0.00808 0.102 0.00712 0.033 0.00231 0.028 0.00194

8.91%

0.055 0.00489 0.348 0.03101 0.199 0.01777 0.189 0.01680 0.130 0.01155 0.043 0.00384 0.036 0.00323

TE D

6.99%

55.11% 0.454 0.25017 0.327 0.18001 0.073 0.04047 0.046 0.02539 0.046 0.02539 0.031 0.01716 0.023 0.01251 40.4%

33.1%

AC C

EP

Composite weight

8.8%

6.4%

5.7%

3.2%

2.4%

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Highlights An Exergy-based Approach for Implementing Design for Reuse Highlights

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Sustainability tradeoff contradictions trends to abandon non-destructive disassembly. An exergy-based approach unbiasedly drives Reuse EoL and Design for Environment. Small components may ride the most important factors for reuse and closed loop economy. The hierarchisation of components will drive design for sustainability within the product.

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