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Multiscale model and informatics-based optimal design of experiments / Vinay Prasad in Industrial & engineering chemistry research, Vol. 47 N°17 (Septembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°17 (Septembre 2008) . - p. 6555–6567 Titre : Multiscale model and informatics-based optimal design of experiments : application to the catalytic decomposition of ammonia on ruthenium Type de document : texte imprimé Auteurs : Vinay Prasad, Auteur ; Dionisios G. Vlachos, Auteur Année de publication : 2008 Article en page(s) : p. 6555–6567 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Ammonia  Multiscale  models  Complex  systems  Physics-aided  methods  Statistics-based  methods  Monte  Carlo  method Résumé : Fundamental multiscale models are increasingly being used to describe complex systems. Microkinetic models, which consider a detailed surface reaction mechanism containing all relevant reactions, are a prototypical multiscale model example. The computational effort in calculating all parameters of a multiscale model for real systems from first principles is prohibitive, and parameter uncertainty still limits full quantitative capabilities of these models. This motivates the development of rational model-based techniques in order to refine uncertain parameters and assess the global (in the entire experimental parameter space) model robustness. Herein we describe physics-aided methods (sensitivity, partial equilibrium, and most abundant reactive intermediate analyses) and statistics-based methods (A, D, and E optimal designs) for the design of experiments. While our methods are fairly general, we demonstrate them for the catalytic decomposition of ammonia on ruthenium to produce hydrogen. A global Monte Carlo method is used to search the operating space to generate possible optimal operating conditions for experiments. Our analysis illustrates that the D optimal and sensitivity-based designs are most promising and generate conditions that delineate important chemistry. It is shown that a standard design around the D optimal point may not be useful for highly nonlinear problems. Instead, informatics methods are proposed to identify optimal regions of the operating space. It is found that the experiments conducted within these regions have a high probability of providing useful kinetics information. It is also shown that the overall direction of the reaction (ammonia decomposition vs synthesis) and the macroenvironment (type of reactor) significantly affect the optimal design. This demonstrates for the first time the effect of macroscopic scales on microscopic ones with important implications for optimal design and product design. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800343s [article] Multiscale model and informatics-based optimal design of experiments : application to the catalytic decomposition of ammonia on ruthenium [texte imprimé] / Vinay Prasad, Auteur ; Dionisios G. Vlachos, Auteur . - 2008 . - p. 6555–6567. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°17 (Septembre 2008) . - p. 6555–6567 Mots-clés : Ammonia  Multiscale  models  Complex  systems  Physics-aided  methods  Statistics-based  methods  Monte  Carlo  method Résumé : Fundamental multiscale models are increasingly being used to describe complex systems. Microkinetic models, which consider a detailed surface reaction mechanism containing all relevant reactions, are a prototypical multiscale model example. The computational effort in calculating all parameters of a multiscale model for real systems from first principles is prohibitive, and parameter uncertainty still limits full quantitative capabilities of these models. This motivates the development of rational model-based techniques in order to refine uncertain parameters and assess the global (in the entire experimental parameter space) model robustness. Herein we describe physics-aided methods (sensitivity, partial equilibrium, and most abundant reactive intermediate analyses) and statistics-based methods (A, D, and E optimal designs) for the design of experiments. While our methods are fairly general, we demonstrate them for the catalytic decomposition of ammonia on ruthenium to produce hydrogen. A global Monte Carlo method is used to search the operating space to generate possible optimal operating conditions for experiments. Our analysis illustrates that the D optimal and sensitivity-based designs are most promising and generate conditions that delineate important chemistry. It is shown that a standard design around the D optimal point may not be useful for highly nonlinear problems. Instead, informatics methods are proposed to identify optimal regions of the operating space. It is found that the experiments conducted within these regions have a high probability of providing useful kinetics information. It is also shown that the overall direction of the reaction (ammonia decomposition vs synthesis) and the macroenvironment (type of reactor) significantly affect the optimal design. This demonstrates for the first time the effect of macroscopic scales on microscopic ones with important implications for optimal design and product design. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800343s

Stochastic nonlinear optimization for robust design of catalysts / Chang Jun Lee in Industrial & engineering chemistry research, Vol. 50 N° 7 (Avril 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 7 (Avril 2011) . - pp. 3938–3946 Titre : Stochastic nonlinear optimization for robust design of catalysts Type de document : texte imprimé Auteurs : Chang Jun Lee, Auteur ; Vinay Prasad, Auteur ; Jong Min Lee, Auteur Année de publication : 2011 Article en page(s) : pp. 3938–3946 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Optimal  catalyst Résumé : Computational methods for designing an optimal catalyst have recently received much attention, especially for energy-related applications. What is lacking in the previous methods is an explicit method to handle uncertainties in the complex models used, so that a robust design is achieved. This work proposes a stochastic optimization method for designing a robust catalyst. In particular, reactions involved in catalytic decomposition of ammonia are presented, and uncertainties associated with experimental determination of kinetic parameters are represented as exogenous variables with assumed probability distributions. The problem is formulated in terms of finding the optimal binding energies that maximize conversion in a microreactor. The resulting stochastic optimization problem is nonlinear, and involves the expectation operator as well as integration in the objective function. This difficult optimization problem is tackled by a population sample based approach, referred to as particle swarm optimization. The results show that the value of solving the stochastic problem is significant, and that it can provide a more robust solution compared to the certainty equivalence approach. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie102103w [article] Stochastic nonlinear optimization for robust design of catalysts [texte imprimé] / Chang Jun Lee, Auteur ; Vinay Prasad, Auteur ; Jong Min Lee, Auteur . - 2011 . - pp. 3938–3946. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 7 (Avril 2011) . - pp. 3938–3946 Mots-clés : Optimal  catalyst Résumé : Computational methods for designing an optimal catalyst have recently received much attention, especially for energy-related applications. What is lacking in the previous methods is an explicit method to handle uncertainties in the complex models used, so that a robust design is achieved. This work proposes a stochastic optimization method for designing a robust catalyst. In particular, reactions involved in catalytic decomposition of ammonia are presented, and uncertainties associated with experimental determination of kinetic parameters are represented as exogenous variables with assumed probability distributions. The problem is formulated in terms of finding the optimal binding energies that maximize conversion in a microreactor. The resulting stochastic optimization problem is nonlinear, and involves the expectation operator as well as integration in the objective function. This difficult optimization problem is tackled by a population sample based approach, referred to as particle swarm optimization. The results show that the value of solving the stochastic problem is significant, and that it can provide a more robust solution compared to the certainty equivalence approach. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie102103w