Documents disponibles écrits par cet auteur

Bayesian framework for building kinetic models of catalytic systems / Shuo-Huan Hsu in Industrial & engineering chemistry research, Vol. 48 N° 10 (Mai 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 10 (Mai 2009) . - pp. 4768–4790 Titre : Bayesian framework for building kinetic models of catalytic systems Type de document : texte imprimé Auteurs : Shuo-Huan Hsu, Auteur ; Stephen D. Stamatis, Auteur ; James M. Caruthers, Auteur Année de publication : 2009 Article en page(s) : pp. 4768–4790 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Catalytic  systems  Bayesian  approach  Monte  Carlo  based  methods Résumé : Recent advances in statistical procedures, coupled with the availability of high performance computational resources and the large mass of data generated from high throughput screening, have enabled a new paradigm for building mathematical models of the kinetic behavior of catalytic reactions. A Bayesian approach is used to formulate the model building problem, estimate model parameters by Monte Carlo based methods, discriminate rival models, and design new experiments to improve the discrimination and fidelity of the parameter estimates. The methodology is illustrated with a typical, model building problem involving three proposed Langmuir−Hinshelwood rate expressions. The Bayesian approach gives improved discrimination of the three models and higher quality model parameters for the best model selected as compared to the traditional methods that employ linearized statistical tools. This paper describes the methodology and its capabilities in sufficient detail to allow kinetic model builders to evaluate and implement its improved model discrimination and parameter estimation features. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801651y [article] Bayesian framework for building kinetic models of catalytic systems [texte imprimé] / Shuo-Huan Hsu, Auteur ; Stephen D. Stamatis, Auteur ; James M. Caruthers, Auteur . - 2009 . - pp. 4768–4790. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 10 (Mai 2009) . - pp. 4768–4790 Mots-clés : Catalytic  systems  Bayesian  approach  Monte  Carlo  based  methods Résumé : Recent advances in statistical procedures, coupled with the availability of high performance computational resources and the large mass of data generated from high throughput screening, have enabled a new paradigm for building mathematical models of the kinetic behavior of catalytic reactions. A Bayesian approach is used to formulate the model building problem, estimate model parameters by Monte Carlo based methods, discriminate rival models, and design new experiments to improve the discrimination and fidelity of the parameter estimates. The methodology is illustrated with a typical, model building problem involving three proposed Langmuir−Hinshelwood rate expressions. The Bayesian approach gives improved discrimination of the three models and higher quality model parameters for the best model selected as compared to the traditional methods that employ linearized statistical tools. This paper describes the methodology and its capabilities in sufficient detail to allow kinetic model builders to evaluate and implement its improved model discrimination and parameter estimation features. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801651y

OntoMODEL: ontological mathematical modeling knowledge management in pharmaceutical product development, 1: conceptual framework / Pradeep Suresh in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7758–7767 Titre : OntoMODEL: ontological mathematical modeling knowledge management in pharmaceutical product development, 1: conceptual framework Type de document : texte imprimé Auteurs : Pradeep Suresh, Auteur ; Shuo-Huan Hsu, Auteur ; Pavan Akkisetty, Auteur Année de publication : 2010 Article en page(s) : pp 7758–7767 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Management  pharmaceutical  product. Résumé : Pharmaceutical product development is a critical step in the path of a drug therapy from its discovery to its delivery to the patient. It is capital-intensive, time-consuming, and extremely information- and knowledge-intensive. This presents various challenges to manage the information and knowledge involved in a systematic, reusable, and user-friendly manner. Knowledge, in this context, means decision-making knowledge and mathematical knowledge that capture the families of mathematical models that exist in this domain. In this paper, which is the first of this two-part series of papers, we describe OntoMODEL, which is an ontological tool for mechanistic mathematical model management that facilitates systematic and standardizable methods for model storage, usage, and solution. [Suresh and co-workers have presented discussions on OntoMODEL at AIChE meetings in San Francisco, CA (2006) and Salt Lake City, UT (2007), as well as at the 18th European Symposium on Computer-Aided Process Engineering (ESCAPE-18) in Lyon, France (2008).] While the declarative knowledge in mathematical models is captured using ontologies, the procedural knowledge required for solving these models is handled by commercially available scientific computing software such as Mathematica and an execution engine written in Java. The interactions involved are well-established and the approach-intuitive; therefore, they do not require user familiarity with any particular programming language or modeling software. Apart from this key benefit, the fact that OntoMODEL lends itself to more-advanced applications such as model-based fault diagnosis, model predictive control (which is decribed in the second paper of this series), knowledge-based decisionmaking, and process flowsheet simulation, making it a useful tool in the intelligent automation of process operations. This paper describes the framework and use of OntoMODEL and discusses how it overcomes the shortcomings of existing approaches toward managing mathematical modeling knowledge. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100246w [article] OntoMODEL: ontological mathematical modeling knowledge management in pharmaceutical product development, 1: conceptual framework [texte imprimé] / Pradeep Suresh, Auteur ; Shuo-Huan Hsu, Auteur ; Pavan Akkisetty, Auteur . - 2010 . - pp 7758–7767. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7758–7767 Mots-clés : Management  pharmaceutical  product. Résumé : Pharmaceutical product development is a critical step in the path of a drug therapy from its discovery to its delivery to the patient. It is capital-intensive, time-consuming, and extremely information- and knowledge-intensive. This presents various challenges to manage the information and knowledge involved in a systematic, reusable, and user-friendly manner. Knowledge, in this context, means decision-making knowledge and mathematical knowledge that capture the families of mathematical models that exist in this domain. In this paper, which is the first of this two-part series of papers, we describe OntoMODEL, which is an ontological tool for mechanistic mathematical model management that facilitates systematic and standardizable methods for model storage, usage, and solution. [Suresh and co-workers have presented discussions on OntoMODEL at AIChE meetings in San Francisco, CA (2006) and Salt Lake City, UT (2007), as well as at the 18th European Symposium on Computer-Aided Process Engineering (ESCAPE-18) in Lyon, France (2008).] While the declarative knowledge in mathematical models is captured using ontologies, the procedural knowledge required for solving these models is handled by commercially available scientific computing software such as Mathematica and an execution engine written in Java. The interactions involved are well-established and the approach-intuitive; therefore, they do not require user familiarity with any particular programming language or modeling software. Apart from this key benefit, the fact that OntoMODEL lends itself to more-advanced applications such as model-based fault diagnosis, model predictive control (which is decribed in the second paper of this series), knowledge-based decisionmaking, and process flowsheet simulation, making it a useful tool in the intelligent automation of process operations. This paper describes the framework and use of OntoMODEL and discusses how it overcomes the shortcomings of existing approaches toward managing mathematical modeling knowledge. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100246w

OntoMODEL: ontological mathematical modeling knowledge management in pharmaceutical product development, 2: applications / Pradeep Suresh in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7768–7781 Titre : OntoMODEL: ontological mathematical modeling knowledge management in pharmaceutical product development, 2: applications Type de document : texte imprimé Auteurs : Pradeep Suresh, Auteur ; Shuo-Huan Hsu, Auteur ; Gintaras V. Reklaitis, Auteur Année de publication : 2010 Article en page(s) : pp 7768–7781 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Management  pharmaceutical  product Résumé : The challenges of managing the information and knowledge involved in pharmaceutical product development in a systematic and reusable, yet user-friendly, manner are very real and substantive. The benefits of managing the mathematical knowledge component in this can, by itself, lead to significant monetary and time savings, because of the reduced cost of interoperability. In the first part of this series, an ontological approach—namely, the OntoMODEL conceptual framework that addresses this need—has been presented. In this paper, applications of this framework are described, which involve the use of OntoMODEL as an integrated component of model-based decision support processes in the domain of model predictive control, in decision making associated with drug formulation and in drug process validation. OntoMODEL is shown to provide an easy and intuitive approach to analyze controller performance, which allows the user to experiment with various controller configurations with minimum effort. OntoMODEL can also effectively support the multilevel product formulation process involving both experimental results and evaluation of model-based performance indices. Finally, aspects of regulatory compliance that involve evaluation of models are also well-supported. This paper demonstrates that OntoMODEL can effectively serve as the multifunctional platform for mathematical model-based applications in the domain of chemical and pharmaceutical process and product development. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100258p [article] OntoMODEL: ontological mathematical modeling knowledge management in pharmaceutical product development, 2: applications [texte imprimé] / Pradeep Suresh, Auteur ; Shuo-Huan Hsu, Auteur ; Gintaras V. Reklaitis, Auteur . - 2010 . - pp 7768–7781. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7768–7781 Mots-clés : Management  pharmaceutical  product Résumé : The challenges of managing the information and knowledge involved in pharmaceutical product development in a systematic and reusable, yet user-friendly, manner are very real and substantive. The benefits of managing the mathematical knowledge component in this can, by itself, lead to significant monetary and time savings, because of the reduced cost of interoperability. In the first part of this series, an ontological approach—namely, the OntoMODEL conceptual framework that addresses this need—has been presented. In this paper, applications of this framework are described, which involve the use of OntoMODEL as an integrated component of model-based decision support processes in the domain of model predictive control, in decision making associated with drug formulation and in drug process validation. OntoMODEL is shown to provide an easy and intuitive approach to analyze controller performance, which allows the user to experiment with various controller configurations with minimum effort. OntoMODEL can also effectively support the multilevel product formulation process involving both experimental results and evaluation of model-based performance indices. Finally, aspects of regulatory compliance that involve evaluation of models are also well-supported. This paper demonstrates that OntoMODEL can effectively serve as the multifunctional platform for mathematical model-based applications in the domain of chemical and pharmaceutical process and product development. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100258p