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Sustainable design of a reactive distillation system
Ian David Lockhart Bogle and Michael Fairweather (Editors), Proceedings of the 22nd European Symposium on Computer Aided Process Engineering, 17 - 20 June 2012, London. © 2012 Elsevier B.V. All rights reserved
Sustainable design of a reactive distillation system Edwin Zondervan, a, Aaron D. Bojarski, b, Antonio Espuña,b, André B. de Haan,a, Luis Puigjaner,b Eindhoven University of Technology, the Netherlands Universitat Polytecnica de Catalunya, Spain Abstract In this contribution we propose a framework that can be used as a tool for sustainable process design in which the combined assessment of economic and environmental impact is performed.. The framework combines three phases in the design procedure i) goal definition and model building, ii) sensitivity analysis and iii) interpretation of the collected data using pareto analysis and rank- and proximity criteria. The proposed methodology is implemented as a scenario-based optimization tool that can be used to study integer and continuous process variables. Keywords: Reactive distillation, sustainability metrics
1. Introduction In process design and operation economic feasibility is no longer the only objective that needs to be satisfied it also has to satisfy environmental and societal needs, which require a holistic view [1]. In recent history many tools have become available that offer such methodology, i.e. life cycle assessment (LCA). An integrated view gives often rise to multi-objectivity and additional constraints in the formulation of the design problem [2]. The design process should not be limited to the process itself but also has to take into account the environment, societal factors and other processes that are linked to the process upstream as well as downstream. Especially the environmental and societal impacts are in the current methodologies still considered as an ‘after-thought’ where the priorities are on the technical and economic components of the design. Such an approach inevitably leads to sub-optimal performance of the plant as the design choices are limited after construction and may not allow more sustainable process alternatives. Closely related to sustainable design is process intensification (PI), which is aiming to develop or improve processes of higher flexibility, reduced environmental impact, improved safety and higher energy efficiency [3]. Reactive distillation (RD) is a typical example of process intensification, which has become a mature technology and in these days new applications are found in the specialty chemicals. RD is proven useful for biofuel production, but also for fatty-acid production and polyester production. But, RD processes are difficult to design and control, as reaction and separation has complicated interactions. To attack the issues with the integration of economic- and environmental metrics, we are going to unite concepts from sustainability related methodologies (LCA) and PI. The application is done for the evaluation of a reactive distillation process used to
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produce fatty acid esters (ingredients for cosmetics) by an esterification reaction between isopropanol and myristic acid.
2. Proposed methodology As core of our method we will use a unique 3-points procedure based on life cycle concepts embedded in the LCA methodology and it starts by building a plausible model for generating the data needed for calculation of the metrics [4]. In this case the model is constructed using commercial process simulation tools. From the problem statement, first the goals have to be defined and the model has to be built. A key point in this study is how different input variables affect the model outputs. The model outputs are typically economic, environmental and operational in its nature. . Sensitivity analysis can be used to study how a change in the inputs of a model influences the outputs, or more formal: Sensitivity analysis (SA) is the study of how the variation in the output of a model can be apportioned, qualitatively or quantitatively, to different sources of variation and of how the given model depends upon the information fed into it. In principle three SA methods exist: i) screening, ii) local sensitivity analysis and iii) global sensitivity analysis. In this work we will use SA to study how engineering decision variables affect the overall optimization objective. Once the design objectives have been set, a proper model has been formulated and sensitivity analysis is done, data can be collected and interpretation of the data has to be done. Different multi-criteria decision analysis (MCDA) techniques could be used to rank the design alternatives obtained. However in order to prune the number of alternatives for analysis the decision maker should only focus on the Pareto efficient ones. Pareto analysis identifies the set of non dominated alternatives if the problem has multiple objectives. Such alternatives are known as the Pareto set of non-inferior alternatives, or Pareto Front (PF). A dominated alternative is one that is inferior to another feasible alternative in the set with respect to all attributes under consideration. This means that for each dominated alternative there is at least one win-win alternative that can be attained without sacrificing achievement in any of the design objectives. The set of alternatives that remains after all the dominated alternatives have been removed is called the set of non-dominated alternatives.
Sustainable design of a reactive distillation system
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Figure 1: Proposed strategy for sustainable design Figure 1 illustrates the whole procedure; step 1 sets the overall modelling hypothesis and the metrics that will measure the goodness of a design. Step 2, which encompasses the sensitivity analysis, uses the economic and environmental metrics Last step considers the generation of Pareto points considering the most influential variables and other considerations, from which a compromise solution will be selected.
3. Results and discussion The tool is tested in a reactive distillation process for the production of fatty acid esters (iso-propyl-myristate). At the first stage of the proposed framework, a reactive distillation model is developed in Aspen Plus, which contains thermodynamic and unit operation models. At the second stage, the sensitivity analysis is performed, showing that the catalyst requirement influences the economic impact and that waste water treatment is the key contributor for the environmental impact. At the third and final stage, Matlab is employed to compute the sustainability metrics using Pareto analysis, which, balances the economic impact (annualized costs) with the environmental impact (with metrics from the ecoinvent database) for different column configurations (number of stages) and operational conditions (operating pressure). From figure 2 follows that the set of efficient solutions is different depending on the metrics considered. In the case of end-point metrics compared to TAC, the profiles of climate change and ecosystem quality vs. TAC are very similar spanning the Pareto front along numerous solutions, in both cases the closest solution to the utopian point is:
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[60-2000]. The other PF for Human health and resource consumption vs. TAC contain less solutions and both give as closest to utopian point solution a column which has 90 stages but working at 4000mmHg and 5000mmHg respectively. In the case of the comparison of end-point metrics, also other PFs are obtained. The PFs of these indicators compared to the other metrics also show the same tendencies. The PF considering human health and resource depletion shows most solutions containing 90 or 40 stages high columns working at pressures below 4000mmHg. To end the Pareto efficiency analysis the comparison of the overall environmental impact against TAC is performed, the PF found resembles the one obtained for climate change and ecosystem quality, this is due to the large weight that the IMPACT2002+ methodology considers for such endpoint categories. For this case the closest to the utopian point is the design which considers a column with 60 stages and works at very low pressure 2000mmHg. However it is interesting to note that this solution selected using a very simple MCDA technique leads to a negative TAC, and consequently in order to make such scenario profitable it will require to increase the product sales price or to diminish the operating costs.
Figure 2: Pareto plot of different solutions obtained in terms of economic metric and overall environmental impact. Labels indicate number of stages and condenser operating pressure. Wide circles emphasise solutions which are closer to utopian point, while crosses show solutions farthest from nadir and utopian point. The different PFs obtained show how the process has a different behaviour depending on which metric is being used, and that there is a need for such analysis in order to consider possible tradeoffs. Each one of the compromise solutions obtained for each binary metric comparison shows a different decision maker point of view where only 2
Sustainable design of a reactive distillation system
65
criteria are considered to have the same importance, and hence the same weight. Furthermore it is noted that using a simplistic MCDA technique for selecting possible solutions could lead to wrong interpretation of the outcomes, for this reason more information into the MCDA should be considered, for example if negative TACs scenarios should be considered or not. The current methodology has been implemented as scenario based optimisation studying the combination of integer and continuous process variables, which were shown to be the most influential on the objective functions; however the use of derivative free optimisation methods can be readily used together with process simulation methods and is part of future work that could be undertaken.
Conclusions Results obtained in this work clearly demonstrate the suitability of the designed framework to effectively assess the sustainability of the reactive distillation process. Further work contemplates the use of derivative free optimisation methods can be readily utilized together with process simulation methods.
References [1] L. Puigjaner and G. Heyen, (Eds.) Computer Aided Process and Product Engineering. Chapter: Modeling in the process life cycle. Section 4.2, pp. 667-693. Wiley-vch Verlag GmbH & Co. KGaA, Weinheim. 2009. [2] A. Azapagic, A. Millington and A. Collet. A methodology for integrating sustainability considerations into process design. Chemical Engineering Research and Design, 84(A6):439-452, 2006. [3] A.I. Stankiewicz and J.A. Moulijn, Process intensification: Transforming chemical engineering. Chemical Engineering Progress, 96 (1):22-33, 2000. [4] A.D. Bojarski, G. Guillén-Gosálbez, L. Jiménez, A. Espuña and L. Puigjaner, Life cycle assessment coupled with process simulation under uncertainty for reduced environmental impact: Application to phosphoric acid production. Industrial & Engineering Chemistry Research, 47:8286-8200, 2008.
Sustainable design of a reactive distillation system Edwin Zondervan, a, Aaron D. Bojarski, b, Antonio Espuña,b, André B. de Haan,a, Luis Puigjaner,b Eindhoven University of Technology, the Netherlands Universitat Polytecnica de Catalunya, Spain Abstract In this contribution we propose a framework that can be used as a tool for sustainable process design in which the combined assessment of economic and environmental impact is performed.. The framework combines three phases in the design procedure i) goal definition and model building, ii) sensitivity analysis and iii) interpretation of the collected data using pareto analysis and rank- and proximity criteria. The proposed methodology is implemented as a scenario-based optimization tool that can be used to study integer and continuous process variables. Keywords: Reactive distillation, sustainability metrics
1. Introduction In process design and operation economic feasibility is no longer the only objective that needs to be satisfied it also has to satisfy environmental and societal needs, which require a holistic view [1]. In recent history many tools have become available that offer such methodology, i.e. life cycle assessment (LCA). An integrated view gives often rise to multi-objectivity and additional constraints in the formulation of the design problem [2]. The design process should not be limited to the process itself but also has to take into account the environment, societal factors and other processes that are linked to the process upstream as well as downstream. Especially the environmental and societal impacts are in the current methodologies still considered as an ‘after-thought’ where the priorities are on the technical and economic components of the design. Such an approach inevitably leads to sub-optimal performance of the plant as the design choices are limited after construction and may not allow more sustainable process alternatives. Closely related to sustainable design is process intensification (PI), which is aiming to develop or improve processes of higher flexibility, reduced environmental impact, improved safety and higher energy efficiency [3]. Reactive distillation (RD) is a typical example of process intensification, which has become a mature technology and in these days new applications are found in the specialty chemicals. RD is proven useful for biofuel production, but also for fatty-acid production and polyester production. But, RD processes are difficult to design and control, as reaction and separation has complicated interactions. To attack the issues with the integration of economic- and environmental metrics, we are going to unite concepts from sustainability related methodologies (LCA) and PI. The application is done for the evaluation of a reactive distillation process used to
62
E. Zondervan et al.
produce fatty acid esters (ingredients for cosmetics) by an esterification reaction between isopropanol and myristic acid.
2. Proposed methodology As core of our method we will use a unique 3-points procedure based on life cycle concepts embedded in the LCA methodology and it starts by building a plausible model for generating the data needed for calculation of the metrics [4]. In this case the model is constructed using commercial process simulation tools. From the problem statement, first the goals have to be defined and the model has to be built. A key point in this study is how different input variables affect the model outputs. The model outputs are typically economic, environmental and operational in its nature. . Sensitivity analysis can be used to study how a change in the inputs of a model influences the outputs, or more formal: Sensitivity analysis (SA) is the study of how the variation in the output of a model can be apportioned, qualitatively or quantitatively, to different sources of variation and of how the given model depends upon the information fed into it. In principle three SA methods exist: i) screening, ii) local sensitivity analysis and iii) global sensitivity analysis. In this work we will use SA to study how engineering decision variables affect the overall optimization objective. Once the design objectives have been set, a proper model has been formulated and sensitivity analysis is done, data can be collected and interpretation of the data has to be done. Different multi-criteria decision analysis (MCDA) techniques could be used to rank the design alternatives obtained. However in order to prune the number of alternatives for analysis the decision maker should only focus on the Pareto efficient ones. Pareto analysis identifies the set of non dominated alternatives if the problem has multiple objectives. Such alternatives are known as the Pareto set of non-inferior alternatives, or Pareto Front (PF). A dominated alternative is one that is inferior to another feasible alternative in the set with respect to all attributes under consideration. This means that for each dominated alternative there is at least one win-win alternative that can be attained without sacrificing achievement in any of the design objectives. The set of alternatives that remains after all the dominated alternatives have been removed is called the set of non-dominated alternatives.
Sustainable design of a reactive distillation system
63
Figure 1: Proposed strategy for sustainable design Figure 1 illustrates the whole procedure; step 1 sets the overall modelling hypothesis and the metrics that will measure the goodness of a design. Step 2, which encompasses the sensitivity analysis, uses the economic and environmental metrics Last step considers the generation of Pareto points considering the most influential variables and other considerations, from which a compromise solution will be selected.
3. Results and discussion The tool is tested in a reactive distillation process for the production of fatty acid esters (iso-propyl-myristate). At the first stage of the proposed framework, a reactive distillation model is developed in Aspen Plus, which contains thermodynamic and unit operation models. At the second stage, the sensitivity analysis is performed, showing that the catalyst requirement influences the economic impact and that waste water treatment is the key contributor for the environmental impact. At the third and final stage, Matlab is employed to compute the sustainability metrics using Pareto analysis, which, balances the economic impact (annualized costs) with the environmental impact (with metrics from the ecoinvent database) for different column configurations (number of stages) and operational conditions (operating pressure). From figure 2 follows that the set of efficient solutions is different depending on the metrics considered. In the case of end-point metrics compared to TAC, the profiles of climate change and ecosystem quality vs. TAC are very similar spanning the Pareto front along numerous solutions, in both cases the closest solution to the utopian point is:
64
E. Zondervan et al.
[60-2000]. The other PF for Human health and resource consumption vs. TAC contain less solutions and both give as closest to utopian point solution a column which has 90 stages but working at 4000mmHg and 5000mmHg respectively. In the case of the comparison of end-point metrics, also other PFs are obtained. The PFs of these indicators compared to the other metrics also show the same tendencies. The PF considering human health and resource depletion shows most solutions containing 90 or 40 stages high columns working at pressures below 4000mmHg. To end the Pareto efficiency analysis the comparison of the overall environmental impact against TAC is performed, the PF found resembles the one obtained for climate change and ecosystem quality, this is due to the large weight that the IMPACT2002+ methodology considers for such endpoint categories. For this case the closest to the utopian point is the design which considers a column with 60 stages and works at very low pressure 2000mmHg. However it is interesting to note that this solution selected using a very simple MCDA technique leads to a negative TAC, and consequently in order to make such scenario profitable it will require to increase the product sales price or to diminish the operating costs.
Figure 2: Pareto plot of different solutions obtained in terms of economic metric and overall environmental impact. Labels indicate number of stages and condenser operating pressure. Wide circles emphasise solutions which are closer to utopian point, while crosses show solutions farthest from nadir and utopian point. The different PFs obtained show how the process has a different behaviour depending on which metric is being used, and that there is a need for such analysis in order to consider possible tradeoffs. Each one of the compromise solutions obtained for each binary metric comparison shows a different decision maker point of view where only 2
Sustainable design of a reactive distillation system
65
criteria are considered to have the same importance, and hence the same weight. Furthermore it is noted that using a simplistic MCDA technique for selecting possible solutions could lead to wrong interpretation of the outcomes, for this reason more information into the MCDA should be considered, for example if negative TACs scenarios should be considered or not. The current methodology has been implemented as scenario based optimisation studying the combination of integer and continuous process variables, which were shown to be the most influential on the objective functions; however the use of derivative free optimisation methods can be readily used together with process simulation methods and is part of future work that could be undertaken.
Conclusions Results obtained in this work clearly demonstrate the suitability of the designed framework to effectively assess the sustainability of the reactive distillation process. Further work contemplates the use of derivative free optimisation methods can be readily utilized together with process simulation methods.
References [1] L. Puigjaner and G. Heyen, (Eds.) Computer Aided Process and Product Engineering. Chapter: Modeling in the process life cycle. Section 4.2, pp. 667-693. Wiley-vch Verlag GmbH & Co. KGaA, Weinheim. 2009. [2] A. Azapagic, A. Millington and A. Collet. A methodology for integrating sustainability considerations into process design. Chemical Engineering Research and Design, 84(A6):439-452, 2006. [3] A.I. Stankiewicz and J.A. Moulijn, Process intensification: Transforming chemical engineering. Chemical Engineering Progress, 96 (1):22-33, 2000. [4] A.D. Bojarski, G. Guillén-Gosálbez, L. Jiménez, A. Espuña and L. Puigjaner, Life cycle assessment coupled with process simulation under uncertainty for reduced environmental impact: Application to phosphoric acid production. Industrial & Engineering Chemistry Research, 47:8286-8200, 2008.