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Auteur B. V. Babu |
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Ajouter le résultat dans votre panier Faire une suggestion Affiner la rechercheOptimization of adiabatic styrene reactor / Ashish M. Gujarathi in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009)
Titre : Optimization of adiabatic styrene reactor : a hybrid multiobjective differential evolution (H-MODE) approach Type de document : texte imprimé Auteurs : Ashish M. Gujarathi, Auteur ; B. V. Babu, Auteur Année de publication : 2010 Article en page(s) : pp. 11115–11132 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Optimization--Adiabatic--Styrene--Reactor--Hybrid--Multiobjective--Differential--Evolution--(H-MODE)--Approach Résumé : In this study, a hybrid strategy of multiobjective differential evolution (hybrid-MODE) algorithm is proposed that consists of an evolutionary algorithm for global search and a deterministic algorithm for local search. To begin, the proposed algorithm is tested for its performance using a benchmark test function (KUR) as case study 1 with the nondominated sorting genetic algorithm-II (NSGA-II) (both binary- and real-coded versions), strength Pareto evolutionary algorithm (SPEA), Pareto archived evolutionary strategy (PAES), multiobjective differential evolution (MODE), and an improved strategy of MODE algorithms. Subsequently, the multiobjective optimization of an industrial adiabatic styrene reactor is carried out as case study 2, employing a prevalidated model using the hybrid-MODE algorithm and an improved strategy of MODE. Four cases (three sets of two-objective optimization, cases 1−3, and one set of three-objective optimization, case 4) are considered consisting of simultaneous maximization of styrene productivity, selectivity, and yield with four decision variables and two constraints. The proposed algorithm converges to a better set of nondominated solutions (possibly a Pareto front) as compared to the nondominated solutions obtained using NSGA and an improved strategy of MODE algorithms. The limitations of previous studies are reported in terms of key decision variables. The hybrid strategy of MODE is found to converge to the true Pareto front more rapidly (in fewer function evaluations), resulting in a well-diversified Pareto front as compared to the stand-alone evolutionary approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901074k
in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11115–11132[article] Optimization of adiabatic styrene reactor : a hybrid multiobjective differential evolution (H-MODE) approach [texte imprimé] / Ashish M. Gujarathi, Auteur ; B. V. Babu, Auteur . - 2010 . - pp. 11115–11132.
Industrial chemistry
Langues : Anglais (eng)
in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11115–11132
Mots-clés : Optimization--Adiabatic--Styrene--Reactor--Hybrid--Multiobjective--Differential--Evolution--(H-MODE)--Approach Résumé : In this study, a hybrid strategy of multiobjective differential evolution (hybrid-MODE) algorithm is proposed that consists of an evolutionary algorithm for global search and a deterministic algorithm for local search. To begin, the proposed algorithm is tested for its performance using a benchmark test function (KUR) as case study 1 with the nondominated sorting genetic algorithm-II (NSGA-II) (both binary- and real-coded versions), strength Pareto evolutionary algorithm (SPEA), Pareto archived evolutionary strategy (PAES), multiobjective differential evolution (MODE), and an improved strategy of MODE algorithms. Subsequently, the multiobjective optimization of an industrial adiabatic styrene reactor is carried out as case study 2, employing a prevalidated model using the hybrid-MODE algorithm and an improved strategy of MODE. Four cases (three sets of two-objective optimization, cases 1−3, and one set of three-objective optimization, case 4) are considered consisting of simultaneous maximization of styrene productivity, selectivity, and yield with four decision variables and two constraints. The proposed algorithm converges to a better set of nondominated solutions (possibly a Pareto front) as compared to the nondominated solutions obtained using NSGA and an improved strategy of MODE algorithms. The limitations of previous studies are reported in terms of key decision variables. The hybrid strategy of MODE is found to converge to the true Pareto front more rapidly (in fewer function evaluations), resulting in a well-diversified Pareto front as compared to the stand-alone evolutionary approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901074k Exemplaires
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Titre : Optimization of adiabatic styrene reactor : a hybrid multiobjective differential evolution (H-MODE) approach Type de document : texte imprimé Auteurs : Ashish M. Gujarathi, Auteur ; B. V. Babu, Auteur Année de publication : 2010 Article en page(s) : pp. 11115–11132 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Hybrid-mode algorithm Résumé : In this study, a hybrid strategy of multiobjective differential evolution (hybrid-MODE) algorithm is proposed that consists of an evolutionary algorithm for global search and a deterministic algorithm for local search. To begin, the proposed algorithm is tested for its performance using a benchmark test function (KUR) as case study 1 with the nondominated sorting genetic algorithm-II (NSGA-II) (both binary- and real-coded versions), strength Pareto evolutionary algorithm (SPEA), Pareto archived evolutionary strategy (PAES), multiobjective differential evolution (MODE), and an improved strategy of MODE algorithms. Subsequently, the multiobjective optimization of an industrial adiabatic styrene reactor is carried out as case study 2, employing a prevalidated model using the hybrid-MODE algorithm and an improved strategy of MODE. Four cases (three sets of two-objective optimization, cases 1−3, and one set of three-objective optimization, case 4) are considered consisting of simultaneous maximization of styrene productivity, selectivity, and yield with four decision variables and two constraints. The proposed algorithm converges to a better set of nondominated solutions (possibly a Pareto front) as compared to the nondominated solutions obtained using NSGA and an improved strategy of MODE algorithms. The limitations of previous studies are reported in terms of key decision variables. The hybrid strategy of MODE is found to converge to the true Pareto front more rapidly (in fewer function evaluations), resulting in a well-diversified Pareto front as compared to the stand-alone evolutionary approach. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901074k
in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11115–11132[article] Optimization of adiabatic styrene reactor : a hybrid multiobjective differential evolution (H-MODE) approach [texte imprimé] / Ashish M. Gujarathi, Auteur ; B. V. Babu, Auteur . - 2010 . - pp. 11115–11132.
Chemical engineering
Langues : Anglais (eng)
in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11115–11132
Mots-clés : Hybrid-mode algorithm Résumé : In this study, a hybrid strategy of multiobjective differential evolution (hybrid-MODE) algorithm is proposed that consists of an evolutionary algorithm for global search and a deterministic algorithm for local search. To begin, the proposed algorithm is tested for its performance using a benchmark test function (KUR) as case study 1 with the nondominated sorting genetic algorithm-II (NSGA-II) (both binary- and real-coded versions), strength Pareto evolutionary algorithm (SPEA), Pareto archived evolutionary strategy (PAES), multiobjective differential evolution (MODE), and an improved strategy of MODE algorithms. Subsequently, the multiobjective optimization of an industrial adiabatic styrene reactor is carried out as case study 2, employing a prevalidated model using the hybrid-MODE algorithm and an improved strategy of MODE. Four cases (three sets of two-objective optimization, cases 1−3, and one set of three-objective optimization, case 4) are considered consisting of simultaneous maximization of styrene productivity, selectivity, and yield with four decision variables and two constraints. The proposed algorithm converges to a better set of nondominated solutions (possibly a Pareto front) as compared to the nondominated solutions obtained using NSGA and an improved strategy of MODE algorithms. The limitations of previous studies are reported in terms of key decision variables. The hybrid strategy of MODE is found to converge to the true Pareto front more rapidly (in fewer function evaluations), resulting in a well-diversified Pareto front as compared to the stand-alone evolutionary approach. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901074k Exemplaires
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