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Optimization-aided material and energy flow analysis for a low carbon industry
Journal of Cleaner Production 167 (2017) 1148e1154
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Optimization-aided material and energy flow analysis for a low carbon industry € er b, Frank Schulenburg b Hendrik Lambrecht a, *, Heidi Hottenroth a, Tobias Schro a b
Institute for Industrial Ecology (INEC), Pforzheim University, Tiefenbronner Str. 65, 75175 Pforzheim, Germany H.C. Starck GmbH, Im Schleeke 78e91, 38642 Goslar, Germany
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 June 2016 Received in revised form 11 January 2017 Accepted 6 August 2017 Available online 7 August 2017
Many industrial companies use material and energy flow analysis as a tool to improve the resource efficiency and in particular the climate impact of their production plants. As material flow models of industrial production systems are typically large and complex, optimal operating conditions or designs are hard to find without computational support. To overcome this shortcoming an integrated approach that combines material and energy flow analysis with mathematical optimization has recently been proposed. This paper presents a prototypic optimization tool for material flow based optimization via mathematical programming. A particularly interesting area of application arises from using the optimization tool already during the model building process. Such an optimization-aided material and energy flow analysis facilitates the systematic exploration of the decision-makers action space and thus supports the construction of valid optimization models. It furthermore helps to find adequate compromises, when there are conflicts between objectives like costs and greenhouse gas emissions. The optimization tool as well as these conceptual aspects are illustrated by applying it to a production plant from the technology metals sector. © 2017 Elsevier Ltd. All rights reserved.
Handling Editor: Yutao Wang Keywords: Material and energy flow analysis (MEFA) Operational research Mathematical programming Resource efficiency Trade-off greenhouse gas emissions-costs Tungsten production
1. Introduction e optimization-aided identification of greenhouse gas reduction potentials Latest since the Paris Agreement negotiated on the Conferences of the Parties (COPs) of the United Nations Framework Convention on Climate Change (UNFCCC) in Paris in December 2015 leading economies underlined the importance of a transition to a low carbon society as a major strategy for mitigating climate change (European Commission, 2011; Goldenberg, 2016). As producer of most goods and services, the industry controls an important part of all material and energy flows within the technosphere. Moreover, companies possess the technological expertise needed for an environmentally benign design of products and processes (Lifset and Graedel, 2002: 3). Low carbon industrial production is thus without doubt an important pillar of a low carbon economy. This is particularly true for the material and energy intensive chemical industry that is, moreover, a supplier to many other industries. Reducing greenhouse gas emissions of chemical production sites requires an integrated analysis and optimization of
* Corresponding author. E-mail address: [email protected] (H. Lambrecht). http://dx.doi.org/10.1016/j.jclepro.2017.08.053 0959-6526/© 2017 Elsevier Ltd. All rights reserved.
complex production systems. To meet this challenge, a softwarebased modeling and evaluation framework has been developed within the research project InReff (Integrated Resource Efficiency Analysis for Reducing Climate Impacts in the Chemical Industry) (Viere et al., 2014). As companies have to fulfill economic goals in the first place, the consideration of environmental impacts is, as a rule, related to economic performance measures such as the production output, costs or revenue. The core of an integrated resource efficiency analysis is a material and energy flow analysis (MEFA) of the considered production system that serves as the quantitative basis for calculating the key performance indicators of an improvement process. MEFA offers both an intuitive approach to analyzing production systems and provides a useful interface to methods and databases from life cycle assessment (LCA) needed for including supply chain information on indirect environmental impacts. It thus becomes possible to comprehensively address issues of resource efficiency and climate change without compromising the production performance. As industrial production sites tend to be complex, with interlinked processes and feedback loops of materials and utilities, sometimes even beyond the boundaries of single plants or companies (Herczeg et al., 2013), it is often difficult to manually identify
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optimal designs or operating conditions for a given production system. For those reasons an extension of MEFA by optimization methods from Operations Research (OR) that enable material flow analysts to perform automatic searches for optimal solutions has been suggested (Bode et al., 2012: 1513). A first step in this direction has been material flow based optimization (Lambrecht and Schmidt, 2010): First, the material flow network (MFN) of a production system is expanded to a material flow based optimization problem (MFBO) by adding an objective function and, if necessary, additional constraints. The MFBO is then solved using direct search heuristics in a simulation-based optimization framework (Fu, 2002; Kolda et al., 2003). A major drawback of this approach is that the MFN is only used to evaluate the objective function and the restrictions. It remains a black box to the optimization algorithms that conceals important information on its mathematical structure that could help to find better solutions with less computing time. This is why Lambrecht and Thißen (2015) have proposed another approach: Instead of embedding the MFN in a simulation-based optimization, the entire MFBO is transformed into an algebraic optimization problem (mathematical program) which can be solved with more powerful solvers of mathematical programming. This paper presents a prototypic tool for such a material flow based optimization via mathematical programming (Ch. 2). The focus is laid on first experiences from applying this tool in practice, i.e. to the tungsten production line at H.C.Starck GmbH in Goslar, Germany (Ch. 3). A particularly interesting area of application arises from using the optimization results (Ch. 4) already during the model building process. Such an optimization-aided MEFA facilitates the systematic exploration of the decision-makers action space and thus supports the construction of valid optimization models. The optimization results are furthermore used to analyze the trade-off between reducing production costs and greenhouse gas emissions, thereby supporting the production planers at H.C. Starck GmbH in adequately dealing with the multi-dimensional decision-situation and helping them to find options for a cost efficient reduction of GHG emissions. 2. Material flow based optimization via algebraic transformation In this chapter the basic concepts of material flow based optimization as used in the case study (Ch. 3) are briefly introduced. For a more thorough description of the conceptual foundations as well as technical and mathematical details we refer to Lambrecht and Schmidt (2010) and Lambrecht and Thißen (2015). 2.1. Material flow networks A specific technique for MEFA is the modeling of material flow € ller et al., 2001). Fig. 1 shows the MFN of an networks (MFNs) (Mo exemplary production plant that converts different raw materials plus energy into several useful products as well as some waste. The production system's structure is represented as a graph with two different node types: transitions (rectangular nodes) and places (circular nodes). Transitions represent single production units where materials are processed or transformed into other materials. Functional relationships between process inputs and outputs are specified in the transitions; either as coefficients of linear production functions, equations involving arbitrary mathematical terms or as subnets, i.e. material flow networks on a lower level that are embedded in the “global” network. Places may represent material stocks, e.g. a warehouse or a store. In most cases they merely connect processes and represent branching-points of material flows within the modeled system. They are also used to assess the material balance at the system boundary (P1-P5 and P9-P13 in
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Fig. 1. An exemplary material flow network (MFN) for a production facility.
Fig. 1). Transitions and places are linked by arrows that correspond to the material and energy flows. MFNs offer a visual and thus quite intuitive approach to system analysis. They have been implemented in the professional MEFA€uslein, software Umberto® (ifu Hamburg, 2015; Schmidt and Ha 1997). Umberto® provides useful interfaces to evaluation methods from cost accounting and life cycle assessment (LCA) that allow for a straight-forward calculation of meaningful environmental and economic performance indicators. As companies have to fulfill economic goals in the first place, the consideration of environmental impacts is often related to economic performance measures such as the production output or revenue. Table 1 shows that virtually any such resource efficiency indicator, e.g. the greenhouse gas emissions for a given product portfolio, can be calculated based on the material and energy flows at the system boundary (Lambrecht and Thißen, 2015). For those reasons MFN has been used in numerous industrial projects on resource efficiency improvement (e.g. Bode et al., 2012; Viere et al., 2010). As Umberto® in its current version NXT is a flexible platform for the integration with other planning tools such as flow sheet simulation, spread sheet calculation or heat integration it has been chosen as the conceptual starting point for integration of mathematical programming for optimization tasks.
2.2. Two complementary optimization approaches There are two different approaches to optimize a MFN model: Simulation-based optimization (SBO) or mathematical programming (MP). In a SBO context (Fig. 2) the model is used as it is to evaluate the objectives and restrictions of the related optimization problem (Driessen, 2006; Fu, 2002). The main advantage of SBO is that any kind of model is tractable. If a MFN contains processes that are specified using algorithmic sequences or embeds other software tools like a flow sheet simulator SBO is actually the only possible way for model optimization (Lambrecht and Schmidt, 2010). For this very reason, the SBO approach has been
Table 1 Typology of material flow based resource efficiency (RE) indicators. The first column indicates the goal when used as optimization objective.
max max min min min max min
RE Indicator
Example
benefit revenue effort costs environm. impact efficiency intensities
product output [kg] waste generation [kg] global warming potential [kg CO2 e] kg product/kg raw material specific energy requirement [MJ/t product]
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parameter varia on restric ons objec ve material flow model as „black box“ calcula on results
(heuris c) search algorithms Fig. 2. Simulation-based optimization.
considered in the above mentioned research project InReff as well, notwithstanding a number of severe drawbacks: As the MFN is a black box to the optimization algorithms that conceals any information on its mathematical structure, only search heuristics can be used that lead in many cases to long searches, high computational effort and suboptimal solutions. Moreover, solutions strongly depend on the configuration of specific search heuristics which is a difficult task for users who are not familiar with Operations Research (Schmidt et al., 2009).
the optimal value of the chosen objective as well as the corresponding values of the decision variables. The optimal solution may then be retransferred to the MFN (upper left) in order to visualize the optimal material and energy flows. The quick visualization of optimization results strongly simplifies their interpretation (causeanalysis) and communication within a company; either between different involved departments or from process engineers to the management. 2.4. Optimization-oriented material and energy flow analysis
2.3. Interface towards mathematical programming & prototypic tool An alternative to SBO is optimization via an “algebraic transformation” of the material flow model: the MFN is first automatically transformed to a mathematical program, i.e. a completely algebraic optimization model; in a subsequent step optimal solutions are searched using the more powerful and robust analytical solvers from mathematical programming. In this section we briefly describe a prototypical optimization module for the MEFA software Umberto NXT® that is based on this approach. For the conceptual and technical details the reader is referred respectively to Lambrecht and Thißen (2015) and Denz et al. (2014). In order to become a meaningful optimization model any MFN needs first to be completed by specifying the optimization objective, identifying decision variables and defining additional constraints. The optimization module that can be launched from within Umberto® NXT provides the necessary functionality (lower part of Fig. 3). A part from choosing single material flows such as a product output as optimization objective, there are a number of predefined objectives available that can easily be derived from the information contained in the MFN: i.e. revenue, costs, direct and indirect1 greenhouse gas emissions (carbon footprint), productivity and eco-efficiency (cf. Table 1). By switching to the “results” tab of the optimization module (upper right in Fig. 3) the LINGO solver (LINDO Systems, 2015) is invoked and returns the solution of the optimization problem, i.e.
1
Indirect means those emissions resulting from the production of e.g. raw materials, auxiliary materials or energy.
The close integration of MEFA and MP provided by the prototypic optimization module thus accelerates the feedback loop of model formulation, optimization, result analysis and communication leading to possibly necessary model adjustments. In this sense the optimization module becomes a tool for an optimization-aided material and energy flow analysis which supports a systematic exploration of the action space. By the term “action space” we understand the whole collection of options for a company to reduce environmental impacts or costs of its production activities or, in a broader sense, to improve its resource efficiency. In general, the action space is restricted by the used technology (as defined by the MFN), product quality requirements, resources availability, and plant capacities. There may also be non-technical restrictions like specifications from the sales department. The optimization-aided exploration of the action space helps to identify the relevant restrictions in practice with an iterative analysis that consists of the following steps: 1. System analysis and representation as a MFN 2. Algebraic transformation and optimization of the MFN 3. Critical review of solutions: which part of a solution is due to “real” resource efficiency potentials and which part is caused by model artefacts i.e. in particular missing restrictions? 4. If necessary: Adaptation and completion of the models constraints and/or structure to make it a better representation of the real situation; Consecutive iteration starting with step 2. Subsequent iterations of a material flow based optimization allow for a gradual exploration of those constraints that really matter in a specific case. The next chapter shows the first iteration
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Update material flow model
Transformation and solution with LINGO Solver [www.lindo.com]
Complete specification
Fig. 3. Algebraic reformulation of a MFN and optimization.
of an optimization-aided action space exploration in practice. 3. Case study: tungsten production at H.C. Starck In this chapter material flow based optimization via algebraic transformation is applied to the tungsten production at H.C. Starck. After a short overview of the company and its main activities the tungsten production line as well as the corresponding MFN is presented (3.1). The following section specifies the optimization problem (3.2). 3.1. Tungsten production at H.C. Starck in Goslar H.C. Starck's essentially produces two groups of materials: The high melting refractory metals tungsten, molybdenum, tantalum, niobium, rhenium and their compounds on the one hand and ceramic materials on the other hand. Refractory metals are of special significance for recycling. Today the recovery of tungsten, tantalum, molybdenum and other refractory metals belongs to the core business of H.C. Starck and is mainly based on recycling. The tungsten recovery is situated in Goslar, Germany. The tungsten chain consists of seven major production stages (Fig. 4). The material flow model also covers important ancillary processes such as steam generation, storage and distribution of steam condensate. Various starting materials (hard scraps, soft scraps, ores like scheelite or wolframite) are supplied after different pretreatment processes as sodium tungstate leach to the extraction/crystallization stage, where ammonium paratungstate (APT) and ammonium metatungstate (AMT) are produced. The latter is not obtained directly from sodium tungstate leach, but from WO3 meta glowed, which is provided by the downstream calcination stage. APT may be sold but also serves as a precursor for tungstic acid and tungsten oxide (WO3) produced in the next step. In the subsequent calcination step, WO3 with various grades is produced from both in-house produced and bought-in APT. WO3 recoiling from this stage is fed back into the pressure leach. Again, WO3 may be sold or used as a precursor for tungsten metal powder (TMP) production in the reduction step. To the reduction a mixture of WO3 from own production or WO3 purchased externally with TMP filter
Fig. 4. Material flow network (MFN) for H.C. Starcks tungsten production.
dust, which originates from the reduction is supplied. TMP leaves the process partially as a product; the greater part goes on in the carburization stage, where tungsten carbide (WC) is produced. The MFN calculates production costs and carbon emissions for a given production program (APT, AMT, WO3, TMP and WC) and
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feedstock mix (hard scraps, scheelite, wolframite, soft scraps, and WO3 purchase materials) of the tungsten production line. 3.2. Specification of the optimization problem The MFN of the tungsten production line has not been conceived as an optimization model in the first place. Its main purposes are (i) to provide a comprehensive quantitative basis for calculating the carbon footprint and (ii) by visualizing the material and energy flows to stimulate the search for improvement potentials. The MFN can be turned into an optimization model, however, by using it to answer the question: is it possible to produce the given product portfolio with lower costs and carbon footprint? To this end the optimization module described in section 2.3 has been applied to the MFN. The production program is fixed. The optimization is done with the aim of either minimizing costs or CO2 emissions. As described in section 2.4 model optimization leads to an iterative procedure, where the optimization model e the MFN itself plus complementary specifications within the optimization module e are refined progressively. The first optimization runs, not described in more detail here, indicated that obtaining meaningful results would require including particular prices, tungsten contents as well as the availabilities of the different feedstock scraps into the model (which were not needed in the initial, purely descriptive MFN). Hard scrap consists of eight and soft scrap of seven fractions which differ with respect to the above mentioned properties. Moreover, based on the first optimization results H.C. Starck decided to include the option of processing roasted soft scraps in the smelter instead of the pressure leach in the analysis. In order to facilitate the understanding of the effects of optimization on the production line, the results are presented in the following section in different ‘scenarios’, where a progressively increased number of model variables that have a fixed value for the status quo are treated as free decision variables (Table 2). In the first scenario, only the choice of pretreatment of soft scrap is variable. In the second scenario the quantities of the purchase materials (APT and WO3) is varied. The third scenario leaves the respective quantities of the scrap fractions used as an additional degree of freedom. Finally, in the fourth scenario the respective amounts of ores become variable. 4. Optimization results Model optimization shows that it is actually possible to produce the same product portfolio with the same production line at significantly lower costs (Fig. 5) or climate impact (Fig. 6). This is e at first sight e a surprising result in a project mainly focused on
Fig. 5. Results of cost minimization. For confidentiality reasons no numbers are shown.
Fig. 6. Results of emission minimization. For confidentiality reasons no numbers are shown.
technical measures. But it well reflects reality, where some of the modeled degrees of freedom (Table 2) are already used by the production management to react in particular on changes in market prices, e.g. to decide on in-house production versus purchase of APT (optimization scenario 2). But this potential has not been explored systematically in the past by the company. This section presents the optimization results obtained by solving the material flow based optimization model presented in the previous section. Note that all calculations are based on modified cost data for confidentiality reasons. Optimization results thus do not correspond exactly to the real tungsten production line.
Table 2 Decision variables in the different optimization scenarios.
4.1. Results for cost minimization optimization scenario 1 choice of pretreatment route for soft scrap share of soft scrap purchase material hard scraps soft scraps ores Legend
fixed variables
2 like 1 + share purchase material
3
4
like 2 + like 3 + choice scrap choice ores
free variables
Fig. 5 shows the effects of cost minimization (solid black line) for the different optimization scenarios described in the previous section. The other lines correspond to the values of the different decision variables (optimization results are actually point values, but lines are easier to distinguish visually). Scenario 1 shows that from a purely cost minimization perspective it is optimal to change the current practice for a pretreatment of the whole soft scrap flow in the smelter. If additionally the choice of purchase vs. in-house production for APT and WO3 is included, it becomes most costeffective to buy as much WO3 as possible (scenario 2). In scenario 3 the purchased amount of APT as well as the input of all soft scrap
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fractions rises to their respective maxima as defined by their market availability. This trend also applies to most of the hard scraps, except for the most expensive fraction, which is significantly reduced and thus leads to an overall decrease of the hard scrap input. As soon as the amount of ores becomes a decision variable (scenario 4), their use rises to a maximum at the expense of hard scraps which are further reduced. 4.2. Results for emission minimization For emission minimization (Fig. 6) the results are significantly different. In contrast to cost minimization, it is more favorable to pretreat the soft scraps in the pressure leach than in the smelter. Scenario 2 results in a reduction of WO3 in favor of APT. Involving the various types of scrap in the decision (scenario 3) shows that it is advantageous to buy as much of both purchase materials as possible, not to use soft scraps with low tungsten content and add a maximum amount of hard scraps. As for cost minimization the use of ores is raised to its maximum in scenario 4, whereas the use of soft scraps continues to fall. In the optimal state the same product portfolio as in the status quo is produced with emissions reduced by almost 16,000 tonnes CO2 equivalents which corresponds to 7% of the total (including indirect) greenhouse gas emissions of H.C. Starck's tungsten production. 4.3. Trade-off between costs and emissions Optimization results for cost minimization (Fig. 5) differ substantially from those for carbon footprint minimization (Fig. 6). In particular the results for scenario 4 indicate different pretreatment routes for soft scraps as well as different feedstock scrap mixes. Costs and carbon footprint thus are obviously conflicting objectives that cannot be minimized simultaneously. But how can the company handle this trade-off between costs and greenhouse gas emissions? A useful approach is to analyze the efficient or Pareto-frontier (Fig. 7), i.e. the subset of solutions in the objective value space where no objective may be further improved without degrading another (cf. e.g. Rardin, 2000: 378 f). The upper left point results from cost minimization without considering the carbon footprint. The lower right point, in contrast, from carbon footprint minimization without considering costs. The points in between are obtained by cost minimization with given constraints on the carbon footprint. The Pareto frontier indicates that the current state is not efficient: all solutions obtained by optimization have lower costs and most of them even lower emissions. Even in the case of emission minimization (lower right solution) costs can be reduced compared to the status quo. However, it is unlikely that the company will choose the solution that absolutely minimizes the carbon footprint. The more so as the convexity of the efficient frontier suggests that, starting from the emission minimum, further cost reductions only result in a quite modest emission increase. However, as costs approach the cost minimum the Pareto-curve becomes ever steeper. Hence, further cost reductions lead to ever higher environmental impacts. It is not a priori clear-cut which solution the company will finally opt for. But this kind of analysis obviously supports an informed decision of the company and helps to find a compromise that both reflects the companies economic interests and environmental goals. 5. Discussion of solutions and interpretation The solution for cost minimization shows that the optimum does not depend on a scrap's tungsten content but on its specific tungsten price. Conversely, the results for emission minimization
Fig. 7. Pareto curve for costs vs. CO2 emissions.
reveal a clear dependence on the tungsten content: hard scraps that have higher tungsten contents on average are clearly preferred over the soft scraps. The MFN represents, from the point of view of OR, essentially a “blending problem” for the feedstock mix: Different purchase prices, qualities (e.g. the tungsten contents of the scrap fractions), environmental burdens as well as different processing routes within the system for the different raw materials directly affect the optimization objectives. On the other hand, the system structure as modeled by the MFN model represents numerous technical restrictions making it difficult to manually identify the optimal feedstock mix. The problem is further complicated by the make-orbuy decisions concerning intermediate products (e.g. APT, WO3) which are interrelated in a complex way via the material flows in the system. The optimization results demonstrate that an increased number of degrees of freedom lead to stronger effects of optimization on the objective. This underlines the necessity to correctly representing the degrees of freedom of the real system in the MFN (cf. Lambrecht and Thißen, 2015: ch. 4). For an appropriate interpretation one should keep in mind that the optimization results are not, at this stage, a forecast of real production results. They rather indicate a direction for potential improvements and further analyses. As this direction has been considered promising by H.C. Starck, the model should, in a next step, be extended in order to explore this potential in more detail: the most relevant aspects of the real production system not yet considered are limits on plant availabilities and capacities, fixed process costs and idle costs that occur, whenever feedstock changes entail a temporary shutdown of individual production units. 6. Conclusions and outlook This paper illustrates how the close integration of MEFA and mathematical programming, as implemented in the presented prototypical optimization tool based on Umberto NXT®, facilitates resource efficiency analyses of plants thereby promoting low carbon industrial production. The tool's application to the tungsten production at H.C. Starck shows that optimization not only helps identifying optimal system configurations at the end of a system analysis and modeling endeavor. It can moreover be used in an early stage of the modeling process to systematically explore the decision maker's action space. For this purpose, a fast feedback of system representation, optimization, visualization and analysis of optimization results and, if necessary, a subsequent model refinement is crucial. The modeling results presented in this paper have
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contributed to a shift in H.C. Starck's perspective. A resource efficient tungsten production is not only a question of technically retrofitting isolated operation units. What is important is a more holistic view, where the feedstock mix and the product portfolio may also play an important role. The role of optimization thus is not necessarily to provide “ready to implement” optimal solutions for the companies. In many cases MFN are not detailed enough representations of the real production systems anyway. Optimization results can, however, be a valuable benchmark for potentially possible improvements. The exploration, via optimization, of a system's degrees of freedom that emerge from the holistic analysis of a production site via MEFA may lead to new ideas about how to reduce the system's carbon footprint that else would not have been considered. The usage of the optimization tool for such “limit-analyses”, almost like a “creativity technique”, as one of the industrial partners stated it, opens a new field of application for the tool that has not been anticipated at the beginning of the project. The practical application of the tool has also revealed requirements for further research and development. As the target group of the optimization tool are engineers and managers, i.e. non-experts in OR, in the first place, its applicability in practice strongly depends on making both the formulation of material flow based optimization models and the analysis of optimization results as easy and intuitive as possible. This requires a further integration of the “action-space-perspective” in the model building process. Constraints should e.g. be formulated in the MFN where they appear in the real system and not in a separate optimization module. Moreover, modelers should be guided towards a system representation that considers the relevant degrees of freedom for system optimization. On the other hand, the analysis of optimization results must be further facilitated. Examples are a visual comparison of different scenarios: Rather than showing absolute material and energy flows in a Sankey diagram, the visualization of differences between e.g. the status quo and an optimal solution could help to identify the most relevant improvement potentials. Finally, a better support is needed for the analysis of multiobjective problems. Where trade-offs between conflicting objectives like costs and carbon emissions make decision-making difficult, optimization-based analyses like the efficient frontier shown in the previous chapter help decision makers to find good compromises. Acknowledgements Funding of this research by the German Federal Ministry for Education and Research (BMBF) in the context of the project InReff (FKZ 01RC1111) within the program “Technologies for Sustainability and Climate Protection e Chemical Processes” is gratefully acknowledged. The authors would also like to thank all project
partners for many fruitful discussions on the issues covered by this article.
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Optimization-aided material and energy flow analysis for a low carbon industry € er b, Frank Schulenburg b Hendrik Lambrecht a, *, Heidi Hottenroth a, Tobias Schro a b
Institute for Industrial Ecology (INEC), Pforzheim University, Tiefenbronner Str. 65, 75175 Pforzheim, Germany H.C. Starck GmbH, Im Schleeke 78e91, 38642 Goslar, Germany
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 June 2016 Received in revised form 11 January 2017 Accepted 6 August 2017 Available online 7 August 2017
Many industrial companies use material and energy flow analysis as a tool to improve the resource efficiency and in particular the climate impact of their production plants. As material flow models of industrial production systems are typically large and complex, optimal operating conditions or designs are hard to find without computational support. To overcome this shortcoming an integrated approach that combines material and energy flow analysis with mathematical optimization has recently been proposed. This paper presents a prototypic optimization tool for material flow based optimization via mathematical programming. A particularly interesting area of application arises from using the optimization tool already during the model building process. Such an optimization-aided material and energy flow analysis facilitates the systematic exploration of the decision-makers action space and thus supports the construction of valid optimization models. It furthermore helps to find adequate compromises, when there are conflicts between objectives like costs and greenhouse gas emissions. The optimization tool as well as these conceptual aspects are illustrated by applying it to a production plant from the technology metals sector. © 2017 Elsevier Ltd. All rights reserved.
Handling Editor: Yutao Wang Keywords: Material and energy flow analysis (MEFA) Operational research Mathematical programming Resource efficiency Trade-off greenhouse gas emissions-costs Tungsten production
1. Introduction e optimization-aided identification of greenhouse gas reduction potentials Latest since the Paris Agreement negotiated on the Conferences of the Parties (COPs) of the United Nations Framework Convention on Climate Change (UNFCCC) in Paris in December 2015 leading economies underlined the importance of a transition to a low carbon society as a major strategy for mitigating climate change (European Commission, 2011; Goldenberg, 2016). As producer of most goods and services, the industry controls an important part of all material and energy flows within the technosphere. Moreover, companies possess the technological expertise needed for an environmentally benign design of products and processes (Lifset and Graedel, 2002: 3). Low carbon industrial production is thus without doubt an important pillar of a low carbon economy. This is particularly true for the material and energy intensive chemical industry that is, moreover, a supplier to many other industries. Reducing greenhouse gas emissions of chemical production sites requires an integrated analysis and optimization of
* Corresponding author. E-mail address: [email protected] (H. Lambrecht). http://dx.doi.org/10.1016/j.jclepro.2017.08.053 0959-6526/© 2017 Elsevier Ltd. All rights reserved.
complex production systems. To meet this challenge, a softwarebased modeling and evaluation framework has been developed within the research project InReff (Integrated Resource Efficiency Analysis for Reducing Climate Impacts in the Chemical Industry) (Viere et al., 2014). As companies have to fulfill economic goals in the first place, the consideration of environmental impacts is, as a rule, related to economic performance measures such as the production output, costs or revenue. The core of an integrated resource efficiency analysis is a material and energy flow analysis (MEFA) of the considered production system that serves as the quantitative basis for calculating the key performance indicators of an improvement process. MEFA offers both an intuitive approach to analyzing production systems and provides a useful interface to methods and databases from life cycle assessment (LCA) needed for including supply chain information on indirect environmental impacts. It thus becomes possible to comprehensively address issues of resource efficiency and climate change without compromising the production performance. As industrial production sites tend to be complex, with interlinked processes and feedback loops of materials and utilities, sometimes even beyond the boundaries of single plants or companies (Herczeg et al., 2013), it is often difficult to manually identify
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optimal designs or operating conditions for a given production system. For those reasons an extension of MEFA by optimization methods from Operations Research (OR) that enable material flow analysts to perform automatic searches for optimal solutions has been suggested (Bode et al., 2012: 1513). A first step in this direction has been material flow based optimization (Lambrecht and Schmidt, 2010): First, the material flow network (MFN) of a production system is expanded to a material flow based optimization problem (MFBO) by adding an objective function and, if necessary, additional constraints. The MFBO is then solved using direct search heuristics in a simulation-based optimization framework (Fu, 2002; Kolda et al., 2003). A major drawback of this approach is that the MFN is only used to evaluate the objective function and the restrictions. It remains a black box to the optimization algorithms that conceals important information on its mathematical structure that could help to find better solutions with less computing time. This is why Lambrecht and Thißen (2015) have proposed another approach: Instead of embedding the MFN in a simulation-based optimization, the entire MFBO is transformed into an algebraic optimization problem (mathematical program) which can be solved with more powerful solvers of mathematical programming. This paper presents a prototypic tool for such a material flow based optimization via mathematical programming (Ch. 2). The focus is laid on first experiences from applying this tool in practice, i.e. to the tungsten production line at H.C.Starck GmbH in Goslar, Germany (Ch. 3). A particularly interesting area of application arises from using the optimization results (Ch. 4) already during the model building process. Such an optimization-aided MEFA facilitates the systematic exploration of the decision-makers action space and thus supports the construction of valid optimization models. The optimization results are furthermore used to analyze the trade-off between reducing production costs and greenhouse gas emissions, thereby supporting the production planers at H.C. Starck GmbH in adequately dealing with the multi-dimensional decision-situation and helping them to find options for a cost efficient reduction of GHG emissions. 2. Material flow based optimization via algebraic transformation In this chapter the basic concepts of material flow based optimization as used in the case study (Ch. 3) are briefly introduced. For a more thorough description of the conceptual foundations as well as technical and mathematical details we refer to Lambrecht and Schmidt (2010) and Lambrecht and Thißen (2015). 2.1. Material flow networks A specific technique for MEFA is the modeling of material flow € ller et al., 2001). Fig. 1 shows the MFN of an networks (MFNs) (Mo exemplary production plant that converts different raw materials plus energy into several useful products as well as some waste. The production system's structure is represented as a graph with two different node types: transitions (rectangular nodes) and places (circular nodes). Transitions represent single production units where materials are processed or transformed into other materials. Functional relationships between process inputs and outputs are specified in the transitions; either as coefficients of linear production functions, equations involving arbitrary mathematical terms or as subnets, i.e. material flow networks on a lower level that are embedded in the “global” network. Places may represent material stocks, e.g. a warehouse or a store. In most cases they merely connect processes and represent branching-points of material flows within the modeled system. They are also used to assess the material balance at the system boundary (P1-P5 and P9-P13 in
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Fig. 1. An exemplary material flow network (MFN) for a production facility.
Fig. 1). Transitions and places are linked by arrows that correspond to the material and energy flows. MFNs offer a visual and thus quite intuitive approach to system analysis. They have been implemented in the professional MEFA€uslein, software Umberto® (ifu Hamburg, 2015; Schmidt and Ha 1997). Umberto® provides useful interfaces to evaluation methods from cost accounting and life cycle assessment (LCA) that allow for a straight-forward calculation of meaningful environmental and economic performance indicators. As companies have to fulfill economic goals in the first place, the consideration of environmental impacts is often related to economic performance measures such as the production output or revenue. Table 1 shows that virtually any such resource efficiency indicator, e.g. the greenhouse gas emissions for a given product portfolio, can be calculated based on the material and energy flows at the system boundary (Lambrecht and Thißen, 2015). For those reasons MFN has been used in numerous industrial projects on resource efficiency improvement (e.g. Bode et al., 2012; Viere et al., 2010). As Umberto® in its current version NXT is a flexible platform for the integration with other planning tools such as flow sheet simulation, spread sheet calculation or heat integration it has been chosen as the conceptual starting point for integration of mathematical programming for optimization tasks.
2.2. Two complementary optimization approaches There are two different approaches to optimize a MFN model: Simulation-based optimization (SBO) or mathematical programming (MP). In a SBO context (Fig. 2) the model is used as it is to evaluate the objectives and restrictions of the related optimization problem (Driessen, 2006; Fu, 2002). The main advantage of SBO is that any kind of model is tractable. If a MFN contains processes that are specified using algorithmic sequences or embeds other software tools like a flow sheet simulator SBO is actually the only possible way for model optimization (Lambrecht and Schmidt, 2010). For this very reason, the SBO approach has been
Table 1 Typology of material flow based resource efficiency (RE) indicators. The first column indicates the goal when used as optimization objective.
max max min min min max min
RE Indicator
Example
benefit revenue effort costs environm. impact efficiency intensities
product output [kg] waste generation [kg] global warming potential [kg CO2 e] kg product/kg raw material specific energy requirement [MJ/t product]
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parameter varia on restric ons objec ve material flow model as „black box“ calcula on results
(heuris c) search algorithms Fig. 2. Simulation-based optimization.
considered in the above mentioned research project InReff as well, notwithstanding a number of severe drawbacks: As the MFN is a black box to the optimization algorithms that conceals any information on its mathematical structure, only search heuristics can be used that lead in many cases to long searches, high computational effort and suboptimal solutions. Moreover, solutions strongly depend on the configuration of specific search heuristics which is a difficult task for users who are not familiar with Operations Research (Schmidt et al., 2009).
the optimal value of the chosen objective as well as the corresponding values of the decision variables. The optimal solution may then be retransferred to the MFN (upper left) in order to visualize the optimal material and energy flows. The quick visualization of optimization results strongly simplifies their interpretation (causeanalysis) and communication within a company; either between different involved departments or from process engineers to the management. 2.4. Optimization-oriented material and energy flow analysis
2.3. Interface towards mathematical programming & prototypic tool An alternative to SBO is optimization via an “algebraic transformation” of the material flow model: the MFN is first automatically transformed to a mathematical program, i.e. a completely algebraic optimization model; in a subsequent step optimal solutions are searched using the more powerful and robust analytical solvers from mathematical programming. In this section we briefly describe a prototypical optimization module for the MEFA software Umberto NXT® that is based on this approach. For the conceptual and technical details the reader is referred respectively to Lambrecht and Thißen (2015) and Denz et al. (2014). In order to become a meaningful optimization model any MFN needs first to be completed by specifying the optimization objective, identifying decision variables and defining additional constraints. The optimization module that can be launched from within Umberto® NXT provides the necessary functionality (lower part of Fig. 3). A part from choosing single material flows such as a product output as optimization objective, there are a number of predefined objectives available that can easily be derived from the information contained in the MFN: i.e. revenue, costs, direct and indirect1 greenhouse gas emissions (carbon footprint), productivity and eco-efficiency (cf. Table 1). By switching to the “results” tab of the optimization module (upper right in Fig. 3) the LINGO solver (LINDO Systems, 2015) is invoked and returns the solution of the optimization problem, i.e.
1
Indirect means those emissions resulting from the production of e.g. raw materials, auxiliary materials or energy.
The close integration of MEFA and MP provided by the prototypic optimization module thus accelerates the feedback loop of model formulation, optimization, result analysis and communication leading to possibly necessary model adjustments. In this sense the optimization module becomes a tool for an optimization-aided material and energy flow analysis which supports a systematic exploration of the action space. By the term “action space” we understand the whole collection of options for a company to reduce environmental impacts or costs of its production activities or, in a broader sense, to improve its resource efficiency. In general, the action space is restricted by the used technology (as defined by the MFN), product quality requirements, resources availability, and plant capacities. There may also be non-technical restrictions like specifications from the sales department. The optimization-aided exploration of the action space helps to identify the relevant restrictions in practice with an iterative analysis that consists of the following steps: 1. System analysis and representation as a MFN 2. Algebraic transformation and optimization of the MFN 3. Critical review of solutions: which part of a solution is due to “real” resource efficiency potentials and which part is caused by model artefacts i.e. in particular missing restrictions? 4. If necessary: Adaptation and completion of the models constraints and/or structure to make it a better representation of the real situation; Consecutive iteration starting with step 2. Subsequent iterations of a material flow based optimization allow for a gradual exploration of those constraints that really matter in a specific case. The next chapter shows the first iteration
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Update material flow model
Transformation and solution with LINGO Solver [www.lindo.com]
Complete specification
Fig. 3. Algebraic reformulation of a MFN and optimization.
of an optimization-aided action space exploration in practice. 3. Case study: tungsten production at H.C. Starck In this chapter material flow based optimization via algebraic transformation is applied to the tungsten production at H.C. Starck. After a short overview of the company and its main activities the tungsten production line as well as the corresponding MFN is presented (3.1). The following section specifies the optimization problem (3.2). 3.1. Tungsten production at H.C. Starck in Goslar H.C. Starck's essentially produces two groups of materials: The high melting refractory metals tungsten, molybdenum, tantalum, niobium, rhenium and their compounds on the one hand and ceramic materials on the other hand. Refractory metals are of special significance for recycling. Today the recovery of tungsten, tantalum, molybdenum and other refractory metals belongs to the core business of H.C. Starck and is mainly based on recycling. The tungsten recovery is situated in Goslar, Germany. The tungsten chain consists of seven major production stages (Fig. 4). The material flow model also covers important ancillary processes such as steam generation, storage and distribution of steam condensate. Various starting materials (hard scraps, soft scraps, ores like scheelite or wolframite) are supplied after different pretreatment processes as sodium tungstate leach to the extraction/crystallization stage, where ammonium paratungstate (APT) and ammonium metatungstate (AMT) are produced. The latter is not obtained directly from sodium tungstate leach, but from WO3 meta glowed, which is provided by the downstream calcination stage. APT may be sold but also serves as a precursor for tungstic acid and tungsten oxide (WO3) produced in the next step. In the subsequent calcination step, WO3 with various grades is produced from both in-house produced and bought-in APT. WO3 recoiling from this stage is fed back into the pressure leach. Again, WO3 may be sold or used as a precursor for tungsten metal powder (TMP) production in the reduction step. To the reduction a mixture of WO3 from own production or WO3 purchased externally with TMP filter
Fig. 4. Material flow network (MFN) for H.C. Starcks tungsten production.
dust, which originates from the reduction is supplied. TMP leaves the process partially as a product; the greater part goes on in the carburization stage, where tungsten carbide (WC) is produced. The MFN calculates production costs and carbon emissions for a given production program (APT, AMT, WO3, TMP and WC) and
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feedstock mix (hard scraps, scheelite, wolframite, soft scraps, and WO3 purchase materials) of the tungsten production line. 3.2. Specification of the optimization problem The MFN of the tungsten production line has not been conceived as an optimization model in the first place. Its main purposes are (i) to provide a comprehensive quantitative basis for calculating the carbon footprint and (ii) by visualizing the material and energy flows to stimulate the search for improvement potentials. The MFN can be turned into an optimization model, however, by using it to answer the question: is it possible to produce the given product portfolio with lower costs and carbon footprint? To this end the optimization module described in section 2.3 has been applied to the MFN. The production program is fixed. The optimization is done with the aim of either minimizing costs or CO2 emissions. As described in section 2.4 model optimization leads to an iterative procedure, where the optimization model e the MFN itself plus complementary specifications within the optimization module e are refined progressively. The first optimization runs, not described in more detail here, indicated that obtaining meaningful results would require including particular prices, tungsten contents as well as the availabilities of the different feedstock scraps into the model (which were not needed in the initial, purely descriptive MFN). Hard scrap consists of eight and soft scrap of seven fractions which differ with respect to the above mentioned properties. Moreover, based on the first optimization results H.C. Starck decided to include the option of processing roasted soft scraps in the smelter instead of the pressure leach in the analysis. In order to facilitate the understanding of the effects of optimization on the production line, the results are presented in the following section in different ‘scenarios’, where a progressively increased number of model variables that have a fixed value for the status quo are treated as free decision variables (Table 2). In the first scenario, only the choice of pretreatment of soft scrap is variable. In the second scenario the quantities of the purchase materials (APT and WO3) is varied. The third scenario leaves the respective quantities of the scrap fractions used as an additional degree of freedom. Finally, in the fourth scenario the respective amounts of ores become variable. 4. Optimization results Model optimization shows that it is actually possible to produce the same product portfolio with the same production line at significantly lower costs (Fig. 5) or climate impact (Fig. 6). This is e at first sight e a surprising result in a project mainly focused on
Fig. 5. Results of cost minimization. For confidentiality reasons no numbers are shown.
Fig. 6. Results of emission minimization. For confidentiality reasons no numbers are shown.
technical measures. But it well reflects reality, where some of the modeled degrees of freedom (Table 2) are already used by the production management to react in particular on changes in market prices, e.g. to decide on in-house production versus purchase of APT (optimization scenario 2). But this potential has not been explored systematically in the past by the company. This section presents the optimization results obtained by solving the material flow based optimization model presented in the previous section. Note that all calculations are based on modified cost data for confidentiality reasons. Optimization results thus do not correspond exactly to the real tungsten production line.
Table 2 Decision variables in the different optimization scenarios.
4.1. Results for cost minimization optimization scenario 1 choice of pretreatment route for soft scrap share of soft scrap purchase material hard scraps soft scraps ores Legend
fixed variables
2 like 1 + share purchase material
3
4
like 2 + like 3 + choice scrap choice ores
free variables
Fig. 5 shows the effects of cost minimization (solid black line) for the different optimization scenarios described in the previous section. The other lines correspond to the values of the different decision variables (optimization results are actually point values, but lines are easier to distinguish visually). Scenario 1 shows that from a purely cost minimization perspective it is optimal to change the current practice for a pretreatment of the whole soft scrap flow in the smelter. If additionally the choice of purchase vs. in-house production for APT and WO3 is included, it becomes most costeffective to buy as much WO3 as possible (scenario 2). In scenario 3 the purchased amount of APT as well as the input of all soft scrap
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fractions rises to their respective maxima as defined by their market availability. This trend also applies to most of the hard scraps, except for the most expensive fraction, which is significantly reduced and thus leads to an overall decrease of the hard scrap input. As soon as the amount of ores becomes a decision variable (scenario 4), their use rises to a maximum at the expense of hard scraps which are further reduced. 4.2. Results for emission minimization For emission minimization (Fig. 6) the results are significantly different. In contrast to cost minimization, it is more favorable to pretreat the soft scraps in the pressure leach than in the smelter. Scenario 2 results in a reduction of WO3 in favor of APT. Involving the various types of scrap in the decision (scenario 3) shows that it is advantageous to buy as much of both purchase materials as possible, not to use soft scraps with low tungsten content and add a maximum amount of hard scraps. As for cost minimization the use of ores is raised to its maximum in scenario 4, whereas the use of soft scraps continues to fall. In the optimal state the same product portfolio as in the status quo is produced with emissions reduced by almost 16,000 tonnes CO2 equivalents which corresponds to 7% of the total (including indirect) greenhouse gas emissions of H.C. Starck's tungsten production. 4.3. Trade-off between costs and emissions Optimization results for cost minimization (Fig. 5) differ substantially from those for carbon footprint minimization (Fig. 6). In particular the results for scenario 4 indicate different pretreatment routes for soft scraps as well as different feedstock scrap mixes. Costs and carbon footprint thus are obviously conflicting objectives that cannot be minimized simultaneously. But how can the company handle this trade-off between costs and greenhouse gas emissions? A useful approach is to analyze the efficient or Pareto-frontier (Fig. 7), i.e. the subset of solutions in the objective value space where no objective may be further improved without degrading another (cf. e.g. Rardin, 2000: 378 f). The upper left point results from cost minimization without considering the carbon footprint. The lower right point, in contrast, from carbon footprint minimization without considering costs. The points in between are obtained by cost minimization with given constraints on the carbon footprint. The Pareto frontier indicates that the current state is not efficient: all solutions obtained by optimization have lower costs and most of them even lower emissions. Even in the case of emission minimization (lower right solution) costs can be reduced compared to the status quo. However, it is unlikely that the company will choose the solution that absolutely minimizes the carbon footprint. The more so as the convexity of the efficient frontier suggests that, starting from the emission minimum, further cost reductions only result in a quite modest emission increase. However, as costs approach the cost minimum the Pareto-curve becomes ever steeper. Hence, further cost reductions lead to ever higher environmental impacts. It is not a priori clear-cut which solution the company will finally opt for. But this kind of analysis obviously supports an informed decision of the company and helps to find a compromise that both reflects the companies economic interests and environmental goals. 5. Discussion of solutions and interpretation The solution for cost minimization shows that the optimum does not depend on a scrap's tungsten content but on its specific tungsten price. Conversely, the results for emission minimization
Fig. 7. Pareto curve for costs vs. CO2 emissions.
reveal a clear dependence on the tungsten content: hard scraps that have higher tungsten contents on average are clearly preferred over the soft scraps. The MFN represents, from the point of view of OR, essentially a “blending problem” for the feedstock mix: Different purchase prices, qualities (e.g. the tungsten contents of the scrap fractions), environmental burdens as well as different processing routes within the system for the different raw materials directly affect the optimization objectives. On the other hand, the system structure as modeled by the MFN model represents numerous technical restrictions making it difficult to manually identify the optimal feedstock mix. The problem is further complicated by the make-orbuy decisions concerning intermediate products (e.g. APT, WO3) which are interrelated in a complex way via the material flows in the system. The optimization results demonstrate that an increased number of degrees of freedom lead to stronger effects of optimization on the objective. This underlines the necessity to correctly representing the degrees of freedom of the real system in the MFN (cf. Lambrecht and Thißen, 2015: ch. 4). For an appropriate interpretation one should keep in mind that the optimization results are not, at this stage, a forecast of real production results. They rather indicate a direction for potential improvements and further analyses. As this direction has been considered promising by H.C. Starck, the model should, in a next step, be extended in order to explore this potential in more detail: the most relevant aspects of the real production system not yet considered are limits on plant availabilities and capacities, fixed process costs and idle costs that occur, whenever feedstock changes entail a temporary shutdown of individual production units. 6. Conclusions and outlook This paper illustrates how the close integration of MEFA and mathematical programming, as implemented in the presented prototypical optimization tool based on Umberto NXT®, facilitates resource efficiency analyses of plants thereby promoting low carbon industrial production. The tool's application to the tungsten production at H.C. Starck shows that optimization not only helps identifying optimal system configurations at the end of a system analysis and modeling endeavor. It can moreover be used in an early stage of the modeling process to systematically explore the decision maker's action space. For this purpose, a fast feedback of system representation, optimization, visualization and analysis of optimization results and, if necessary, a subsequent model refinement is crucial. The modeling results presented in this paper have
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contributed to a shift in H.C. Starck's perspective. A resource efficient tungsten production is not only a question of technically retrofitting isolated operation units. What is important is a more holistic view, where the feedstock mix and the product portfolio may also play an important role. The role of optimization thus is not necessarily to provide “ready to implement” optimal solutions for the companies. In many cases MFN are not detailed enough representations of the real production systems anyway. Optimization results can, however, be a valuable benchmark for potentially possible improvements. The exploration, via optimization, of a system's degrees of freedom that emerge from the holistic analysis of a production site via MEFA may lead to new ideas about how to reduce the system's carbon footprint that else would not have been considered. The usage of the optimization tool for such “limit-analyses”, almost like a “creativity technique”, as one of the industrial partners stated it, opens a new field of application for the tool that has not been anticipated at the beginning of the project. The practical application of the tool has also revealed requirements for further research and development. As the target group of the optimization tool are engineers and managers, i.e. non-experts in OR, in the first place, its applicability in practice strongly depends on making both the formulation of material flow based optimization models and the analysis of optimization results as easy and intuitive as possible. This requires a further integration of the “action-space-perspective” in the model building process. Constraints should e.g. be formulated in the MFN where they appear in the real system and not in a separate optimization module. Moreover, modelers should be guided towards a system representation that considers the relevant degrees of freedom for system optimization. On the other hand, the analysis of optimization results must be further facilitated. Examples are a visual comparison of different scenarios: Rather than showing absolute material and energy flows in a Sankey diagram, the visualization of differences between e.g. the status quo and an optimal solution could help to identify the most relevant improvement potentials. Finally, a better support is needed for the analysis of multiobjective problems. Where trade-offs between conflicting objectives like costs and carbon emissions make decision-making difficult, optimization-based analyses like the efficient frontier shown in the previous chapter help decision makers to find good compromises. Acknowledgements Funding of this research by the German Federal Ministry for Education and Research (BMBF) in the context of the project InReff (FKZ 01RC1111) within the program “Technologies for Sustainability and Climate Protection e Chemical Processes” is gratefully acknowledged. The authors would also like to thank all project
partners for many fruitful discussions on the issues covered by this article.
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