The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Serge Domenech - One of the best experts on this subject based on the ideXlab platform.
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A grid-based environment for multiparametric PSE applications: batch Plant Design case study
2008Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Luc Pibouleau, Serge Domenech, Iréa Touche, Michel DaydéAbstract:Complex optimization problems are of high interest for Process Systems Engineering. The selection of the relevant technique for the treatment of a given problem has already been studied for batch Plant Design issues. Classically, most works reported in the dedicated literature yet considered item sizes as continuous variables. In a view of realism, a similar approach is proposed in this paper, with discrete variables for representing equipment capacities, which leads to a combinatorial problem. For this purpose, a Genetic Algorithm was used, which is multiparametric by nature and a grid approach is perfectly relevant to this case study, since the GA code must be run several times, with different values of some input parameters, to guarantee its stochastic nature. This paper is devoted to the presentation of a grid-oriented GA methodology. Some significant results are highlighted and discussed.
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strategies for multiobjective genetic algorithm development application to optimal batch Plant Design in process systems engineering
Computers & Industrial Engineering, 2008Co-Authors: Adrian Dietz, Luc Pibouleau, Catherine Azzaropantel, Serge DomenechAbstract:This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process Design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a Design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions. Then, the results of two multiobjective case studies in batch Plant Design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems.
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Constraint handling strategies in Genetic Algorithms application to optimal batch Plant Design
Chemical Engineering and Processing: Process Intensification, 2008Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Serge Domenech, Luc PibouleauAbstract:Optimal batch Plant Design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one.
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Mixed-Integer Nonlinear Programming Optimization Strategies for Batch Plant Design Problems
Industrial and engineering chemistry research, 2006Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Serge Domenech, Luc PibouleauAbstract:Due to their large variety of applications, complex optimisation problems induced a great effort to develop efficient solution techniques, dealing with both continuous and discrete variables involved in non-linear functions. But among the diversity of those optimisation methods, the choice of the relevant technique for the treatment of a given problem keeps being a thorny issue. Within the Process Engineering context, batch Plant Design problems provide a good framework to test the performances of various optimisation methods : on the one hand, two Mathematical Programming techniques – DICOPT++ and SBB, implemented in the GAMS environment – and, on the other hand, one stochastic method, i.e. a genetic algorithm. Seven examples, showing an increasing complexity, were solved with these three techniques. The result comparison enables to evaluate their efficiency in order to highlight the most appropriate method for a given problem instance. It was proved that the best performing method is SBB, even if the GA also provides interesting solutions, in terms of quality as well as of computational time.
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Multiobjective optimization for multiproduct batch Plant Design under economic and environmental considerations
Computers and Chemical Engineering, 2006Co-Authors: Adrian Dietz, Catherine Azzaro-pantel, Luc Pibouleau, Serge DomenechAbstract:This work deals with the multicriteria cost–environment Design of multiproduct batch Plants, where the Design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch Plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch Plant Design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch Plant Design.
Luc Pibouleau - One of the best experts on this subject based on the ideXlab platform.
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A grid-based environment for multiparametric PSE applications: batch Plant Design case study
2008Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Luc Pibouleau, Serge Domenech, Iréa Touche, Michel DaydéAbstract:Complex optimization problems are of high interest for Process Systems Engineering. The selection of the relevant technique for the treatment of a given problem has already been studied for batch Plant Design issues. Classically, most works reported in the dedicated literature yet considered item sizes as continuous variables. In a view of realism, a similar approach is proposed in this paper, with discrete variables for representing equipment capacities, which leads to a combinatorial problem. For this purpose, a Genetic Algorithm was used, which is multiparametric by nature and a grid approach is perfectly relevant to this case study, since the GA code must be run several times, with different values of some input parameters, to guarantee its stochastic nature. This paper is devoted to the presentation of a grid-oriented GA methodology. Some significant results are highlighted and discussed.
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strategies for multiobjective genetic algorithm development application to optimal batch Plant Design in process systems engineering
Computers & Industrial Engineering, 2008Co-Authors: Adrian Dietz, Luc Pibouleau, Catherine Azzaropantel, Serge DomenechAbstract:This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process Design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a Design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions. Then, the results of two multiobjective case studies in batch Plant Design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems.
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Constraint handling strategies in Genetic Algorithms application to optimal batch Plant Design
Chemical Engineering and Processing: Process Intensification, 2008Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Serge Domenech, Luc PibouleauAbstract:Optimal batch Plant Design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one.
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Mixed-Integer Nonlinear Programming Optimization Strategies for Batch Plant Design Problems
Industrial and engineering chemistry research, 2006Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Serge Domenech, Luc PibouleauAbstract:Due to their large variety of applications, complex optimisation problems induced a great effort to develop efficient solution techniques, dealing with both continuous and discrete variables involved in non-linear functions. But among the diversity of those optimisation methods, the choice of the relevant technique for the treatment of a given problem keeps being a thorny issue. Within the Process Engineering context, batch Plant Design problems provide a good framework to test the performances of various optimisation methods : on the one hand, two Mathematical Programming techniques – DICOPT++ and SBB, implemented in the GAMS environment – and, on the other hand, one stochastic method, i.e. a genetic algorithm. Seven examples, showing an increasing complexity, were solved with these three techniques. The result comparison enables to evaluate their efficiency in order to highlight the most appropriate method for a given problem instance. It was proved that the best performing method is SBB, even if the GA also provides interesting solutions, in terms of quality as well as of computational time.
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Multiobjective optimization for multiproduct batch Plant Design under economic and environmental considerations
Computers and Chemical Engineering, 2006Co-Authors: Adrian Dietz, Catherine Azzaro-pantel, Luc Pibouleau, Serge DomenechAbstract:This work deals with the multicriteria cost–environment Design of multiproduct batch Plants, where the Design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch Plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch Plant Design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch Plant Design.
Catherine Azzaro-pantel - One of the best experts on this subject based on the ideXlab platform.
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A grid-based environment for multiparametric PSE applications: batch Plant Design case study
2008Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Luc Pibouleau, Serge Domenech, Iréa Touche, Michel DaydéAbstract:Complex optimization problems are of high interest for Process Systems Engineering. The selection of the relevant technique for the treatment of a given problem has already been studied for batch Plant Design issues. Classically, most works reported in the dedicated literature yet considered item sizes as continuous variables. In a view of realism, a similar approach is proposed in this paper, with discrete variables for representing equipment capacities, which leads to a combinatorial problem. For this purpose, a Genetic Algorithm was used, which is multiparametric by nature and a grid approach is perfectly relevant to this case study, since the GA code must be run several times, with different values of some input parameters, to guarantee its stochastic nature. This paper is devoted to the presentation of a grid-oriented GA methodology. Some significant results are highlighted and discussed.
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Constraint handling strategies in Genetic Algorithms application to optimal batch Plant Design
Chemical Engineering and Processing: Process Intensification, 2008Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Serge Domenech, Luc PibouleauAbstract:Optimal batch Plant Design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one.
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Mixed-Integer Nonlinear Programming Optimization Strategies for Batch Plant Design Problems
Industrial and engineering chemistry research, 2006Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Serge Domenech, Luc PibouleauAbstract:Due to their large variety of applications, complex optimisation problems induced a great effort to develop efficient solution techniques, dealing with both continuous and discrete variables involved in non-linear functions. But among the diversity of those optimisation methods, the choice of the relevant technique for the treatment of a given problem keeps being a thorny issue. Within the Process Engineering context, batch Plant Design problems provide a good framework to test the performances of various optimisation methods : on the one hand, two Mathematical Programming techniques – DICOPT++ and SBB, implemented in the GAMS environment – and, on the other hand, one stochastic method, i.e. a genetic algorithm. Seven examples, showing an increasing complexity, were solved with these three techniques. The result comparison enables to evaluate their efficiency in order to highlight the most appropriate method for a given problem instance. It was proved that the best performing method is SBB, even if the GA also provides interesting solutions, in terms of quality as well as of computational time.
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Multiobjective optimization for multiproduct batch Plant Design under economic and environmental considerations
Computers and Chemical Engineering, 2006Co-Authors: Adrian Dietz, Catherine Azzaro-pantel, Luc Pibouleau, Serge DomenechAbstract:This work deals with the multicriteria cost–environment Design of multiproduct batch Plants, where the Design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch Plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch Plant Design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch Plant Design.
Kaisa Miettinen - One of the best experts on this subject based on the ideXlab platform.
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wastewater treatment Plant Design and operation under multiple conflicting objective functions
Environmental Modelling and Software, 2013Co-Authors: Jussi Hakanen, Kristian Sahlstedt, Kaisa MiettinenAbstract:Wastewater treatment Plant Design and operation involve multiple objective functions, which are often in conflict with each other. Traditional optimization tools convert all objective functions to a single objective optimization problem (usually minimization of a total cost function by using weights for the objective functions), hiding the interdependencies between different objective functions. We present an interactive approach that is able to handle multiple objective functions simultaneously. As an illustration of our approach, we consider a case study of Plant-wide operational optimization where we apply an interactive optimization tool. In this tool, a commercial wastewater treatment simulation software is combined with an interactive multiobjective optimization software, providing an entirely new approach in wastewater treatment. We compare our approach to a traditional approach by solving the case study also as a single objective optimization problem to demonstrate the advantages of interactive multiobjective optimization in wastewater treatment Plant Design and operation. New interactive approach to WWTP Design using interactive multiobjective optimization.Interactive approach combined with dynamic simulation in a Plant-wide operational optimization.An objective function is used for each criterion reflecting their interdependencies.Interactive optimization among Pareto optimal solutions guided by an expert decision maker.Comparison of interactive approach to approach with one combined objective function.
Antonin Ponsich - One of the best experts on this subject based on the ideXlab platform.
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an ahp based decision making tool for the solution of multiproduct batch Plant Design problem under imprecise demand
Computers & Operations Research, 2009Co-Authors: Alberto A Aguilarlasserre, Antonin Ponsich, Marco Bautista A Bautista, Magno Gonzalez A HuertaAbstract:This paper addresses the problem of the optimal Design of batch Plants with imprecise demands in product amounts. The Design of such Plants necessarily involves the way that equipment may be utilized, which means that Plant scheduling and production must form an integral part of the Design problem. This work relies on a previous study, which proposed an alternative treatment of the imprecision (demands) by introducing fuzzy concepts, embedded in a multi-objective Genetic Algorithm (GA) that takes into account simultaneously maximization of the net present value (NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The results showed that an additional interpretation step might be necessary to help the managers choosing among the non-dominated solutions provided by the GA. The analytic hierarchy process (AHP) is a strategy commonly used in Operations Research for the solution of this kind of multicriteria decision problems, allowing the apprehension of manager subjective judgments. The major aim of this study is thus to propose a software integrating the AHP theory for the analysis of the GA Pareto-optimal solutions, as an alternative decision-support tool for the batch Plant Design problem solution.
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A grid-based environment for multiparametric PSE applications: batch Plant Design case study
2008Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Luc Pibouleau, Serge Domenech, Iréa Touche, Michel DaydéAbstract:Complex optimization problems are of high interest for Process Systems Engineering. The selection of the relevant technique for the treatment of a given problem has already been studied for batch Plant Design issues. Classically, most works reported in the dedicated literature yet considered item sizes as continuous variables. In a view of realism, a similar approach is proposed in this paper, with discrete variables for representing equipment capacities, which leads to a combinatorial problem. For this purpose, a Genetic Algorithm was used, which is multiparametric by nature and a grid approach is perfectly relevant to this case study, since the GA code must be run several times, with different values of some input parameters, to guarantee its stochastic nature. This paper is devoted to the presentation of a grid-oriented GA methodology. Some significant results are highlighted and discussed.
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Constraint handling strategies in Genetic Algorithms application to optimal batch Plant Design
Chemical Engineering and Processing: Process Intensification, 2008Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Serge Domenech, Luc PibouleauAbstract:Optimal batch Plant Design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one.
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Mixed-Integer Nonlinear Programming Optimization Strategies for Batch Plant Design Problems
Industrial and engineering chemistry research, 2006Co-Authors: Antonin Ponsich, Catherine Azzaro-pantel, Serge Domenech, Luc PibouleauAbstract:Due to their large variety of applications, complex optimisation problems induced a great effort to develop efficient solution techniques, dealing with both continuous and discrete variables involved in non-linear functions. But among the diversity of those optimisation methods, the choice of the relevant technique for the treatment of a given problem keeps being a thorny issue. Within the Process Engineering context, batch Plant Design problems provide a good framework to test the performances of various optimisation methods : on the one hand, two Mathematical Programming techniques – DICOPT++ and SBB, implemented in the GAMS environment – and, on the other hand, one stochastic method, i.e. a genetic algorithm. Seven examples, showing an increasing complexity, were solved with these three techniques. The result comparison enables to evaluate their efficiency in order to highlight the most appropriate method for a given problem instance. It was proved that the best performing method is SBB, even if the GA also provides interesting solutions, in terms of quality as well as of computational time.
