Documents disponibles écrits par cet auteur

Design of dynamic experiments in modeling for optimization of batch processes / Ernesto C. Martinez in Industrial & engineering chemistry research, Vol. 48 N° 7 (Avril 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 7 (Avril 2009) . - pp. 3453–3465 Titre : Design of dynamic experiments in modeling for optimization of batch processes Type de document : texte imprimé Auteurs : Ernesto C. Martinez, Auteur ; Mariano D. Cristaldi, Auteur ; Ricardo J. Grau, Auteur Année de publication : 2009 Article en page(s) : pp. 3453–3465 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Designing  dynamic  experiments  Hamilton−Jacobi−Bellman  optimality  equation  Model-based  policy  iteration  algorithm Résumé : Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speeding up the development of innovative products and processes. Modeling for optimization is proposed as a systematic approach to bias data gathering for iterative policy improvement through experimental design using first-principles models. Designing dynamic experiments that are optimally informative in order to reduce the uncertainty about the optimal operating conditions is addressed by integrating policy iteration based on the Hamilton−Jacobi−Bellman optimality equation with global sensitivity analysis. A conceptual framework for run-to-run convergence of a model-based policy iteration algorithm is proposed. Results obtained in the fed-batch fermentation of penicillin G are presented. The well-known Bajpai and Reuss bioreactor model validated with industrial data is used to increase on a run-to-run basis the amount of penicillin obtained by input policy optimization and selective (re)estimation of relevant model parameters. A remarkable improvement in productivity can be gain using a simple policy structure after only two modeling runs despite initial modeling uncertainty. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8000953 [article] Design of dynamic experiments in modeling for optimization of batch processes [texte imprimé] / Ernesto C. Martinez, Auteur ; Mariano D. Cristaldi, Auteur ; Ricardo J. Grau, Auteur . - 2009 . - pp. 3453–3465. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 7 (Avril 2009) . - pp. 3453–3465 Mots-clés : Designing  dynamic  experiments  Hamilton−Jacobi−Bellman  optimality  equation  Model-based  policy  iteration  algorithm Résumé : Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speeding up the development of innovative products and processes. Modeling for optimization is proposed as a systematic approach to bias data gathering for iterative policy improvement through experimental design using first-principles models. Designing dynamic experiments that are optimally informative in order to reduce the uncertainty about the optimal operating conditions is addressed by integrating policy iteration based on the Hamilton−Jacobi−Bellman optimality equation with global sensitivity analysis. A conceptual framework for run-to-run convergence of a model-based policy iteration algorithm is proposed. Results obtained in the fed-batch fermentation of penicillin G are presented. The well-known Bajpai and Reuss bioreactor model validated with industrial data is used to increase on a run-to-run basis the amount of penicillin obtained by input policy optimization and selective (re)estimation of relevant model parameters. A remarkable improvement in productivity can be gain using a simple policy structure after only two modeling runs despite initial modeling uncertainty. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8000953

Finding the simplest mechanistic kinetic model describing the homogeneous catalytic hydrogenation of avermectin to ivermectin / Mariano D. Cristaldi in Industrial & engineering chemistry research, Vol. 50 N° 8 (Avril 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 8 (Avril 2011) . - pp. 4252–4263 Titre : Finding the simplest mechanistic kinetic model describing the homogeneous catalytic hydrogenation of avermectin to ivermectin Type de document : texte imprimé Auteurs : Mariano D. Cristaldi, Auteur ; Maria I. Cabrera, Auteur ; Ernesto C. Martinez, Auteur Année de publication : 2011 Article en page(s) : pp. 4252–4263 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Kinetic  Catalytic  hydrogenation Résumé : Six mechanistic kinetic models of increasing complexity are analyzed to describe the RhCl(Ph3P)3 catalyzed hydrogenation process to produce ivermectin from avermectins B1a and B1b. Global sensitivity analysis (GSA) usefulness for selecting the simplest and the most suitable model is shown. First-order and total effect sensitivity indices for model parameters computed from GSA have been used for establishing those elementary reaction steps which were the most important in an extensive reaction framework. The prediction capability of the chosen model is corroborated by comparing its predictions with experimental data from both a lab-scale reactor and an industrial-scale reactor operating under isothermal and nonisothermal conditions, respectively. The best model is simple to use while resulting in a significant computational effort saving because there is no need to perform iterative algorithms for solving model equations. Another interesting feature is that ODEs for such a model have an analytical solution for isothermal hydrogenation processes. These features make modeling more amenable for cost-effective simulation and to include the selected model into computational frameworks for design of the hydrogenation process and control systems of the most usual catalytic method for producing ivermectin. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101289h [article] Finding the simplest mechanistic kinetic model describing the homogeneous catalytic hydrogenation of avermectin to ivermectin [texte imprimé] / Mariano D. Cristaldi, Auteur ; Maria I. Cabrera, Auteur ; Ernesto C. Martinez, Auteur . - 2011 . - pp. 4252–4263. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 8 (Avril 2011) . - pp. 4252–4263 Mots-clés : Kinetic  Catalytic  hydrogenation Résumé : Six mechanistic kinetic models of increasing complexity are analyzed to describe the RhCl(Ph3P)3 catalyzed hydrogenation process to produce ivermectin from avermectins B1a and B1b. Global sensitivity analysis (GSA) usefulness for selecting the simplest and the most suitable model is shown. First-order and total effect sensitivity indices for model parameters computed from GSA have been used for establishing those elementary reaction steps which were the most important in an extensive reaction framework. The prediction capability of the chosen model is corroborated by comparing its predictions with experimental data from both a lab-scale reactor and an industrial-scale reactor operating under isothermal and nonisothermal conditions, respectively. The best model is simple to use while resulting in a significant computational effort saving because there is no need to perform iterative algorithms for solving model equations. Another interesting feature is that ODEs for such a model have an analytical solution for isothermal hydrogenation processes. These features make modeling more amenable for cost-effective simulation and to include the selected model into computational frameworks for design of the hydrogenation process and control systems of the most usual catalytic method for producing ivermectin. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101289h

Optimal operation of discretely controlled continuous systems under uncertainty / Mariano De Paula in Industrial & engineering chemistry research, Vol. 51 N° 42 (Octobre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - .13743–13764 Titre : Optimal operation of discretely controlled continuous systems under uncertainty Type de document : texte imprimé Auteurs : Mariano De Paula, Auteur ; Ernesto C. Martinez, Auteur Année de publication : 2012 Article en page(s) : .13743–13764 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Optimal  operation  Continuous  systems Résumé : Discretely controlled continuous systems constitute a special class of continuous-time hybrid dynamical systems where timely switching to alternative control modes is used for dynamic optimization in uncertain environments. Each mode implements a parametrized feedback control law until a stopping condition triggers due to the activation of a constraint related to states, controls, or disturbances. For optimal operation under uncertainty, a novel simulation-based algorithm that combines dynamic programming with event-driven execution and Gaussian processes is proposed to learn a switching policy for mode selection. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Probabilistic models of the state transition dynamics following each mode execution are fitted upon data obtained by increasingly biasing operating conditions. Throughput maximization in a hybrid chemical plant is used as a representative case study. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie301015z [article] Optimal operation of discretely controlled continuous systems under uncertainty [texte imprimé] / Mariano De Paula, Auteur ; Ernesto C. Martinez, Auteur . - 2012 . - .13743–13764. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - .13743–13764 Mots-clés : Optimal  operation  Continuous  systems Résumé : Discretely controlled continuous systems constitute a special class of continuous-time hybrid dynamical systems where timely switching to alternative control modes is used for dynamic optimization in uncertain environments. Each mode implements a parametrized feedback control law until a stopping condition triggers due to the activation of a constraint related to states, controls, or disturbances. For optimal operation under uncertainty, a novel simulation-based algorithm that combines dynamic programming with event-driven execution and Gaussian processes is proposed to learn a switching policy for mode selection. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Probabilistic models of the state transition dynamics following each mode execution are fitted upon data obtained by increasingly biasing operating conditions. Throughput maximization in a hybrid chemical plant is used as a representative case study. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie301015z