Documents disponibles écrits par cet auteur

Dealing with irregular data in soft sensors / Khatibisepehr Shima ; Biao Huang in Industrial & engineering chemistry research, Vol. 47 n°22 (Novembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°22 (Novembre 2008) . - p. 8713–8723 Titre : Dealing with irregular data in soft sensors : bayesian method and comparative study Type de document : texte imprimé Auteurs : Khatibisepehr Shima, Auteur ; Biao Huang, Auteur Année de publication : 2008 Article en page(s) : p. 8713–8723 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Irregular  Data Résumé : The main challenge in developing soft sensors in process industry is the existence of irregularity of data, such as measurement noises, outliers, and missing data. This paper is concerned with a comparative study among various data-driven soft sensor algorithms and the Bayesian methods. The algorithms to be considered for a comparative study in this paper include ordinary least-squares, robust regression, error-in-variable methods, partial least-squares, and the Bayesian inference algorithms. Methods for handling irregular data are reviewed. An iterative Bayesian algorithm for handling measurement noise and outliers is proposed. Performance of the Bayesian methods is compared with other existing methods through simulations, a pilot-scale experiment, and an industrial application. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800386v [article] Dealing with irregular data in soft sensors : bayesian method and comparative study [texte imprimé] / Khatibisepehr Shima, Auteur ; Biao Huang, Auteur . - 2008 . - p. 8713–8723. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°22 (Novembre 2008) . - p. 8713–8723 Mots-clés : Irregular  Data Résumé : The main challenge in developing soft sensors in process industry is the existence of irregularity of data, such as measurement noises, outliers, and missing data. This paper is concerned with a comparative study among various data-driven soft sensor algorithms and the Bayesian methods. The algorithms to be considered for a comparative study in this paper include ordinary least-squares, robust regression, error-in-variable methods, partial least-squares, and the Bayesian inference algorithms. Methods for handling irregular data are reviewed. An iterative Bayesian algorithm for handling measurement noise and outliers is proposed. Performance of the Bayesian methods is compared with other existing methods through simulations, a pilot-scale experiment, and an industrial application. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800386v

Dynamic bayesian approach for control loop diagnosis with underlying mode dependency / Fei Qi in Industrial & engineering chemistry research, Vol. 49 N° 18 (Septembre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8613–8623 Titre : Dynamic bayesian approach for control loop diagnosis with underlying mode dependency Type de document : texte imprimé Auteurs : Fei Qi, Auteur ; Biao Huang, Auteur Année de publication : 2010 Article en page(s) : pp. 8613–8623 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Dynamic  bayesian Résumé : In this article, first, a hidden Markov model is built to address the temporal mode dependency problem in control loop diagnosis. A data-driven algorithm is developed to estimate the mode transition probability. The new solution to mode dependency is then further synthesized with the solution to evidence dependency to develop a recursive autoregressive hidden Markov model for online control loop diagnosis. When both the mode and evidence transition information sets are considered, the temporal information is effectively synthesized under the Bayesian framework. A simulated distillation column example and a pilot-scale experiment example are investigated to demonstrate the ability of the proposed diagnosis approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100058y [article] Dynamic bayesian approach for control loop diagnosis with underlying mode dependency [texte imprimé] / Fei Qi, Auteur ; Biao Huang, Auteur . - 2010 . - pp. 8613–8623. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8613–8623 Mots-clés : Dynamic  bayesian Résumé : In this article, first, a hidden Markov model is built to address the temporal mode dependency problem in control loop diagnosis. A data-driven algorithm is developed to estimate the mode transition probability. The new solution to mode dependency is then further synthesized with the solution to evidence dependency to develop a recursive autoregressive hidden Markov model for online control loop diagnosis. When both the mode and evidence transition information sets are considered, the temporal information is effectively synthesized under the Bayesian framework. A simulated distillation column example and a pilot-scale experiment example are investigated to demonstrate the ability of the proposed diagnosis approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100058y

Estimation of instrument variance and bias using bayesian methods / Ruben Gonzalez in Industrial & engineering chemistry research, Vol. 50 N° 10 (Mai 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6229-6239 Titre : Estimation of instrument variance and bias using bayesian methods Type de document : texte imprimé Auteurs : Ruben Gonzalez, Auteur ; Biao Huang, Auteur ; Fangwei Xu, Auteur Année de publication : 2011 Article en page(s) : pp. 6229-6239 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Bias  Instruments Résumé : Imprecision of sensors is one of the main causes of poor control and process performance. Often, instrument measurement bias and variance change over the time and online calibration/re-estimation is necessary. Originated from a real industrial application problem, this paper proposed a Bayesian approach to determine the inconsistency of sensors, based on mass-balance principles. A mass-balance factor model is then introduced, where the factor analysis method is used to determine initial values for estimating instrument noise and process disturbance variance. Because of the structural constraint of mass-balance equations, a gray-box estimation procedure must be adopted for which Bayesian network estimation via the expectation-maximization (EM) algorithm is a very suitable method. Therefore, this paper uses factor analysis to determine the initial values, and, afterward, estimates process and sensor variance by means of Bayesian estimation. After estimating the process and instrument variance, the process steady state and instrument bias can be similarly estimated. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24158920 [article] Estimation of instrument variance and bias using bayesian methods [texte imprimé] / Ruben Gonzalez, Auteur ; Biao Huang, Auteur ; Fangwei Xu, Auteur . - 2011 . - pp. 6229-6239. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6229-6239 Mots-clés : Bias  Instruments Résumé : Imprecision of sensors is one of the main causes of poor control and process performance. Often, instrument measurement bias and variance change over the time and online calibration/re-estimation is necessary. Originated from a real industrial application problem, this paper proposed a Bayesian approach to determine the inconsistency of sensors, based on mass-balance principles. A mass-balance factor model is then introduced, where the factor analysis method is used to determine initial values for estimating instrument noise and process disturbance variance. Because of the structural constraint of mass-balance equations, a gray-box estimation procedure must be adopted for which Bayesian network estimation via the expectation-maximization (EM) algorithm is a very suitable method. Therefore, this paper uses factor analysis to determine the initial values, and, afterward, estimates process and sensor variance by means of Bayesian estimation. After estimating the process and instrument variance, the process steady state and instrument bias can be similarly estimated. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24158920

MPC constraint analysis / Seyi Akande in Industrial & engineering chemistry research, Vol. 48 N° 8 (Avril 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 8 (Avril 2009) . - pp. 3944–3954 Titre : MPC constraint analysis : Bayesian approach via a continuous-valued profit function Type de document : texte imprimé Auteurs : Seyi Akande, Auteur ; Biao Huang, Auteur ; Kwan Ho Lee, Auteur Année de publication : 2009 Article en page(s) : pp. 3944–3954 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Model  predictive  control  Bayesian  probabilistic  framework Résumé : Model predictive control (MPC) is one of the most studied modern control technologies. Among the various subjects investigated, controller performance assessment of MPC has received considerable attention in recent time. Various approaches and algorithms have been proposed for the assessment of MPCs. In this work, we propose a novel approach to MPC constraint analysis by considering the economic objective function as a continuous-valued function within a Bayesian probabilistic framework. The analysis involves inference of the effect of a decision to adjust the limits of the constrained variables with regards to the achievable profits (decision evaluation) as well as inference of constraint limits that should be adjusted so as to achieve a specified profit value (decision making). The benefits of this approach include a more generalized definition of quality variables, the development of a more rigorous formulation of the problem to address linear and quadratic objective functions and thereby obtaining closed form solutions, and maximum-likelihood location determination of the quality variables in the decision making process. The approach is illustrated with the use of simulations and a pilot-scale experiment. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8011566 [article] MPC constraint analysis : Bayesian approach via a continuous-valued profit function [texte imprimé] / Seyi Akande, Auteur ; Biao Huang, Auteur ; Kwan Ho Lee, Auteur . - 2009 . - pp. 3944–3954. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 8 (Avril 2009) . - pp. 3944–3954 Mots-clés : Model  predictive  control  Bayesian  probabilistic  framework Résumé : Model predictive control (MPC) is one of the most studied modern control technologies. Among the various subjects investigated, controller performance assessment of MPC has received considerable attention in recent time. Various approaches and algorithms have been proposed for the assessment of MPCs. In this work, we propose a novel approach to MPC constraint analysis by considering the economic objective function as a continuous-valued function within a Bayesian probabilistic framework. The analysis involves inference of the effect of a decision to adjust the limits of the constrained variables with regards to the achievable profits (decision evaluation) as well as inference of constraint limits that should be adjusted so as to achieve a specified profit value (decision making). The benefits of this approach include a more generalized definition of quality variables, the development of a more rigorous formulation of the problem to address linear and quadratic objective functions and thereby obtaining closed form solutions, and maximum-likelihood location determination of the quality variables in the decision making process. The approach is illustrated with the use of simulations and a pilot-scale experiment. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8011566

Subspace approach to identification of step-response model from closed-loop data / Nima Danesh Pour in Industrial & engineering chemistry research, Vol. 49 N° 18 (Septembre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8558–8567 Titre : Subspace approach to identification of step-response model from closed-loop data Type de document : texte imprimé Auteurs : Nima Danesh Pour, Auteur ; Biao Huang, Auteur ; Sirish L. Shah, Auteur Année de publication : 2010 Article en page(s) : pp. 8558–8567 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Silicone  surfactant Résumé : We investigate direct estimation of step-response models from closed-loop data using subspace identification. Necessary information concerning impulse-response coefficients is embedded in subspace matrices. Therefore, the step-response coefficients can be directly obtained from this matrix by integration without the need of extracting state space models first, as the conventional subspace identification does. Since the estimated subspace matrix contains more than one set of impulse-response coefficients, a question arises about how to best synthesize them to obtain an optimal estimate of the impulse-response coefficients and subsequently the step-response coefficients. For this purpose, a reformulation of the subspace identification problem is required for which the variance of all elements in the related subspace matrix can be evaluated. The calculated variances are then used to perform a weighted averaging on the estimated impulse-response coefficients to attenuate the noise influence on the final step-response model estimation. Monte Carlo simulations and pilot-scale experiments are provided to illustrate the proposed method. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie1012213 [article] Subspace approach to identification of step-response model from closed-loop data [texte imprimé] / Nima Danesh Pour, Auteur ; Biao Huang, Auteur ; Sirish L. Shah, Auteur . - 2010 . - pp. 8558–8567. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8558–8567 Mots-clés : Silicone  surfactant Résumé : We investigate direct estimation of step-response models from closed-loop data using subspace identification. Necessary information concerning impulse-response coefficients is embedded in subspace matrices. Therefore, the step-response coefficients can be directly obtained from this matrix by integration without the need of extracting state space models first, as the conventional subspace identification does. Since the estimated subspace matrix contains more than one set of impulse-response coefficients, a question arises about how to best synthesize them to obtain an optimal estimate of the impulse-response coefficients and subsequently the step-response coefficients. For this purpose, a reformulation of the subspace identification problem is required for which the variance of all elements in the related subspace matrix can be evaluated. The calculated variances are then used to perform a weighted averaging on the estimated impulse-response coefficients to attenuate the noise influence on the final step-response model estimation. Monte Carlo simulations and pilot-scale experiments are provided to illustrate the proposed method. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie1012213

Tuning a soft sensor’s bias update term. 1. / Yuri A. W. Shardt in Industrial & engineering chemistry research, Vol. 51 N° 13 (Avril 2012) 

Permalink

Tuning a soft sensor’s bias update term. 2. / Yuri A. W. Shardt in Industrial & engineering chemistry research, Vol. 51 N° 13 (Avril 2012) 

Permalink