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Online model-based redesign of experiments for parameter estimation in dynamic systems / Federico Galvanin in Industrial & engineering chemistry research, Vol. 48 N° 9 (Mai 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 9 (Mai 2009) . - pp. 4415–4427 Titre : Online model-based redesign of experiments for parameter estimation in dynamic systems Type de document : texte imprimé Auteurs : Federico Galvanin, Auteur ; Massimiliano Barolo, Auteur ; Fabrizio Bezzo, Auteur Année de publication : 2009 Article en page(s) : pp. 4415–4427 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Dynamic  systems  Online  model-based Résumé : The optimal model-based design of experiments aims at designing a set of dynamic experiments yielding the most informative process data to be used for the estimation of the parameters of a first-principles dynamic process model. According to the usual procedure for parameter estimation, the experiment is first designed offline; then, the experiment is carried out in the plant, and process measurements are collected; and finally, parameters are estimated after completion of the experiment. Therefore, the information gathered during the evolution of the experiment is analyzed only at the end of the experiment itself. Since the experiment is designed on the basis of the parameter estimates available before the experiment is started, the progressive increase of the information resulting from the progress of the experiment is not exploited by the designer until the end of that experiment. In this paper, a strategy for the online model-based redesign of experiments is proposed to exploit the information as soon as it is generated from the execution of an experiment, and its performance is compared to that of a standard optimal experiment design approach. Intermediate parameter estimations are carried out while the experiment is running, and by exploiting the information obtained, the experiment is partially redesigned before its termination, with the purpose of updating the experimental settings to generate more valuable information for subsequent analysis. This enables us to reduce the number of experimental trials that are needed to reach a statistically sound estimation of the model parameters and results in a reduction of experimental time, raw materials needs, number of samples to be analyzed, control effort, and labor. Two simulated case studies of increasing level of complexity are used to demonstrate the benefits of the proposed approach with respect to a state-of-the-art sequential model-based experiment design. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8018356 [article] Online model-based redesign of experiments for parameter estimation in dynamic systems [texte imprimé] / Federico Galvanin, Auteur ; Massimiliano Barolo, Auteur ; Fabrizio Bezzo, Auteur . - 2009 . - pp. 4415–4427. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 9 (Mai 2009) . - pp. 4415–4427 Mots-clés : Dynamic  systems  Online  model-based Résumé : The optimal model-based design of experiments aims at designing a set of dynamic experiments yielding the most informative process data to be used for the estimation of the parameters of a first-principles dynamic process model. According to the usual procedure for parameter estimation, the experiment is first designed offline; then, the experiment is carried out in the plant, and process measurements are collected; and finally, parameters are estimated after completion of the experiment. Therefore, the information gathered during the evolution of the experiment is analyzed only at the end of the experiment itself. Since the experiment is designed on the basis of the parameter estimates available before the experiment is started, the progressive increase of the information resulting from the progress of the experiment is not exploited by the designer until the end of that experiment. In this paper, a strategy for the online model-based redesign of experiments is proposed to exploit the information as soon as it is generated from the execution of an experiment, and its performance is compared to that of a standard optimal experiment design approach. Intermediate parameter estimations are carried out while the experiment is running, and by exploiting the information obtained, the experiment is partially redesigned before its termination, with the purpose of updating the experimental settings to generate more valuable information for subsequent analysis. This enables us to reduce the number of experimental trials that are needed to reach a statistically sound estimation of the model parameters and results in a reduction of experimental time, raw materials needs, number of samples to be analyzed, control effort, and labor. Two simulated case studies of increasing level of complexity are used to demonstrate the benefits of the proposed approach with respect to a state-of-the-art sequential model-based experiment design. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8018356

Optimal design of clinical tests for the identification of physiological models of type 1 diabetes mellitus / Federico Galvanin in Industrial & engineering chemistry research, Vol. 48 N°4 (Février 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°4 (Février 2009) . - pp. 1989–2002 Titre : Optimal design of clinical tests for the identification of physiological models of type 1 diabetes mellitus Type de document : texte imprimé Auteurs : Federico Galvanin, Auteur ; Massimiliano Barolo, Auteur ; Sandro Macchietto, Auteur Année de publication : 2009 Article en page(s) : pp. 1989–2002 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Type  1  diabetes  mellitus  Artificial  pancreas  Dynamic  simulation  model  Glucose-insulin  system Résumé : Type 1 diabetes mellitus is a disease affecting millions of people worldwide and causing the expenditure of millions of euros every year for health care. One of the most promising therapies derives from the use of an artificial pancreas, based on a control system able to maintain the normoglycaemia in the subject affected by diabetes. A dynamic simulation model of the glucose−insulin system can be useful in several circumstances for diabetes care, including testing of glucose sensors, insulin infusion algorithms, and decision support systems for diabetes. This paper considers the problem of the identification of single individual parameters in detailed dynamic models of glucose homeostasis. Optimal model-based design of experiment techniques are used to design a set of clinical tests that allow the model parameters to be estimated in a statistically sound way, while meeting constraints related to safety of the subject and ease of implementation. The model with the estimated set of parameters represents a specific subject and can thus be used for customized diabetes care solutions. Simulated results demonstrate how such an approach can improve the effectiveness of clinical tests and serve as a tool to devise safer and more efficient clinical protocols, thus providing a contribution to the development of an artificial pancreas. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801209g [article] Optimal design of clinical tests for the identification of physiological models of type 1 diabetes mellitus [texte imprimé] / Federico Galvanin, Auteur ; Massimiliano Barolo, Auteur ; Sandro Macchietto, Auteur . - 2009 . - pp. 1989–2002. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°4 (Février 2009) . - pp. 1989–2002 Mots-clés : Type  1  diabetes  mellitus  Artificial  pancreas  Dynamic  simulation  model  Glucose-insulin  system Résumé : Type 1 diabetes mellitus is a disease affecting millions of people worldwide and causing the expenditure of millions of euros every year for health care. One of the most promising therapies derives from the use of an artificial pancreas, based on a control system able to maintain the normoglycaemia in the subject affected by diabetes. A dynamic simulation model of the glucose−insulin system can be useful in several circumstances for diabetes care, including testing of glucose sensors, insulin infusion algorithms, and decision support systems for diabetes. This paper considers the problem of the identification of single individual parameters in detailed dynamic models of glucose homeostasis. Optimal model-based design of experiment techniques are used to design a set of clinical tests that allow the model parameters to be estimated in a statistically sound way, while meeting constraints related to safety of the subject and ease of implementation. The model with the estimated set of parameters represents a specific subject and can thus be used for customized diabetes care solutions. Simulated results demonstrate how such an approach can improve the effectiveness of clinical tests and serve as a tool to devise safer and more efficient clinical protocols, thus providing a contribution to the development of an artificial pancreas. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801209g