Documents disponibles écrits par cet auteur

Constrained Bayesian state estimation using a cell filter / Sridhar Ungarala in Industrial & engineering chemistry research, Vol. 47 N°19 (Octobre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°19 (Octobre 2008) . - p. 7312–7322 Titre : Constrained Bayesian state estimation using a cell filter Type de document : texte imprimé Auteurs : Sridhar Ungarala, Auteur ; Keyu Li, Auteur ; Zhongzhou Chen, Auteur Année de publication : 2008 Article en page(s) : p. 7312–7322 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Nonlinear/non-Gaussian  processes  Constrained  cell  filter Résumé : Constrained state estimation in nonlinear/non-Gaussian processes has been the domain of optimization based methods such as moving horizon estimation (MHE). MHE has a Bayesian interpretation, but it is not practical to implement a recursive MHE without assumptions of Gaussianity and linearized dynamics at various stages. This paper presents the constrained cell filter (CCF) as an alternative to MHE, requiring no linearization, jacobians, or nonlinear program. The CCF computes a piecewise constant approximation of the state probability density function with support defined by constraints; thus, all point estimates are constrained. The CCF can be more accurate and orders of magnitude faster than MHE for problems of a size as investigated in this work. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie070249q [article] Constrained Bayesian state estimation using a cell filter [texte imprimé] / Sridhar Ungarala, Auteur ; Keyu Li, Auteur ; Zhongzhou Chen, Auteur . - 2008 . - p. 7312–7322. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°19 (Octobre 2008) . - p. 7312–7322 Mots-clés : Nonlinear/non-Gaussian  processes  Constrained  cell  filter Résumé : Constrained state estimation in nonlinear/non-Gaussian processes has been the domain of optimization based methods such as moving horizon estimation (MHE). MHE has a Bayesian interpretation, but it is not practical to implement a recursive MHE without assumptions of Gaussianity and linearized dynamics at various stages. This paper presents the constrained cell filter (CCF) as an alternative to MHE, requiring no linearization, jacobians, or nonlinear program. The CCF computes a piecewise constant approximation of the state probability density function with support defined by constraints; thus, all point estimates are constrained. The CCF can be more accurate and orders of magnitude faster than MHE for problems of a size as investigated in this work. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie070249q

Nonlinear model predictive control of high purity distillation columns for cryogenic air separation / Zhongzhou Chen in IEEE Transactions on control systems technology, Vol. 18 N° 4 (Juillet 2010) 

Public ISBD [article]  in IEEE Transactions on control systems technology > Vol. 18 N° 4 (Juillet 2010) . - pp. 811-821 Titre : Nonlinear model predictive control of high purity distillation columns for cryogenic air separation Type de document : texte imprimé Auteurs : Zhongzhou Chen, Auteur ; Michael A. Henson, Auteur ; Paul Belanger, Auteur Année de publication : 2011 Article en page(s) : pp. 811-821 Note générale : Génie Aérospatial Langues : Anglais (eng) Mots-clés : Nonlinear  model  predictive  control  (NMPC)  Process  control  Real  time  optimization  Reduced-order  modeling Index. décimale : 629.1 Résumé : High purity distillation columns are critical unit operations in cryogenic air separation plants that supply purified gases to a number of industries. We have developed a nonlinear model predictive control (NMPC) strategy based on the assumption of full-state feedback for a prototypical cryogenic distillation column to allow effective operation over a wide range of plant production rates. The controller design was based on a reduced-order compartmental model derived from detailed mass and energy balances by exploiting time-scale separations. Temporal discretization of the compartmental model produced a very large set of nonlinear differential and algebraic equations with advantageous sparsity properties, enabling online solution of the NMPC problem. The synergistic combination of several real-time implementation techniques were found to be essential for further reducing computation time and allowing reliable solution within the 2-min controller sampling interval. Closed-loop simulation studies demonstrated the performance advantages of NMPC compared to linear model predictive control technology currently used in the air separation industry. DEWEY : 629.1 ISSN : 1063-6536 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5288540 [article] Nonlinear model predictive control of high purity distillation columns for cryogenic air separation [texte imprimé] / Zhongzhou Chen, Auteur ; Michael A. Henson, Auteur ; Paul Belanger, Auteur . - 2011 . - pp. 811-821. Génie Aérospatial Langues : Anglais (eng) in IEEE Transactions on control systems technology > Vol. 18 N° 4 (Juillet 2010) . - pp. 811-821 Mots-clés : Nonlinear  model  predictive  control  (NMPC)  Process  control  Real  time  optimization  Reduced-order  modeling Index. décimale : 629.1 Résumé : High purity distillation columns are critical unit operations in cryogenic air separation plants that supply purified gases to a number of industries. We have developed a nonlinear model predictive control (NMPC) strategy based on the assumption of full-state feedback for a prototypical cryogenic distillation column to allow effective operation over a wide range of plant production rates. The controller design was based on a reduced-order compartmental model derived from detailed mass and energy balances by exploiting time-scale separations. Temporal discretization of the compartmental model produced a very large set of nonlinear differential and algebraic equations with advantageous sparsity properties, enabling online solution of the NMPC problem. The synergistic combination of several real-time implementation techniques were found to be essential for further reducing computation time and allowing reliable solution within the 2-min controller sampling interval. Closed-loop simulation studies demonstrated the performance advantages of NMPC compared to linear model predictive control technology currently used in the air separation industry. DEWEY : 629.1 ISSN : 1063-6536 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5288540

Nonlinear model predictive control of high purity distillation columns for cryogenic air separation / Zhongzhou Chen in IEEE Transactions on control systems technology, Vol. 18 N° 4 (Juillet 2010) 

Public ISBD [article]  in IEEE Transactions on control systems technology > Vol. 18 N° 4 (Juillet 2010) . - pp. 811-821 Titre : Nonlinear model predictive control of high purity distillation columns for cryogenic air separation Type de document : texte imprimé Auteurs : Zhongzhou Chen, Auteur ; Michael A. Henson, Auteur ; Paul Belanger, Auteur Année de publication : 2011 Article en page(s) : pp. 811-821 Note générale : Génie Aérospatial Langues : Anglais (eng) Mots-clés : Nonlinear  model  predictive  control  (NMPC)  Process  control  Real  time  optimization  Reduced-order  modeling Index. décimale : 629.1 Résumé : High purity distillation columns are critical unit operations in cryogenic air separation plants that supply purified gases to a number of industries. We have developed a nonlinear model predictive control (NMPC) strategy based on the assumption of full-state feedback for a prototypical cryogenic distillation column to allow effective operation over a wide range of plant production rates. The controller design was based on a reduced-order compartmental model derived from detailed mass and energy balances by exploiting time-scale separations. Temporal discretization of the compartmental model produced a very large set of nonlinear differential and algebraic equations with advantageous sparsity properties, enabling online solution of the NMPC problem. The synergistic combination of several real-time implementation techniques were found to be essential for further reducing computation time and allowing reliable solution within the 2-min controller sampling interval. Closed-loop simulation studies demonstrated the performance advantages of NMPC compared to linear model predictive control technology currently used in the air separation industry. DEWEY : 629.1 ISSN : 1063-6536 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5288540 [article] Nonlinear model predictive control of high purity distillation columns for cryogenic air separation [texte imprimé] / Zhongzhou Chen, Auteur ; Michael A. Henson, Auteur ; Paul Belanger, Auteur . - 2011 . - pp. 811-821. Génie Aérospatial Langues : Anglais (eng) in IEEE Transactions on control systems technology > Vol. 18 N° 4 (Juillet 2010) . - pp. 811-821 Mots-clés : Nonlinear  model  predictive  control  (NMPC)  Process  control  Real  time  optimization  Reduced-order  modeling Index. décimale : 629.1 Résumé : High purity distillation columns are critical unit operations in cryogenic air separation plants that supply purified gases to a number of industries. We have developed a nonlinear model predictive control (NMPC) strategy based on the assumption of full-state feedback for a prototypical cryogenic distillation column to allow effective operation over a wide range of plant production rates. The controller design was based on a reduced-order compartmental model derived from detailed mass and energy balances by exploiting time-scale separations. Temporal discretization of the compartmental model produced a very large set of nonlinear differential and algebraic equations with advantageous sparsity properties, enabling online solution of the NMPC problem. The synergistic combination of several real-time implementation techniques were found to be essential for further reducing computation time and allowing reliable solution within the 2-min controller sampling interval. Closed-loop simulation studies demonstrated the performance advantages of NMPC compared to linear model predictive control technology currently used in the air separation industry. DEWEY : 629.1 ISSN : 1063-6536 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5288540