Documents disponibles écrits par cet auteur

Genetically tuned decentralized proportional-integral controllers for composition control of reactive distillation / C. Sumana in Industrial & engineering chemistry research, Vol. 49 N° 3 (Fevrier 2010)

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 3 (Fevrier 2010) . - pp. 1297–1311 Titre : Genetically tuned decentralized proportional-integral controllers for composition control of reactive distillation Type de document : texte imprimé Auteurs : C. Sumana, Auteur ; Ch. Venkateswarlu, Auteur Année de publication : 2010 Article en page(s) : pp. 1297–1311 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Genetically--Decentralized--Proportional-Integral--Controllers--Composition--Control--Reactive--Distillation Résumé : This paper presents a genetic algorithm (GA) based autotuning method to design a decentralized proportional-integral (PI) control system for composition control of a highly interactive and nonlinear reactive distillation column. The control relevant characteristics such as nonlinearities, interactions, and stability are analyzed for assessing the complexity of the process. The objective of GA tuning is to account the multivariable interactions and nonlinear dynamics of the process to find a unique set of parameters for the control system that is robust to all kinds of disturbances. The performance function in GA is formulated by incorporating the dynamic state information of the process derived from its model for various closed-loop disturbance conditions. The controller tuning problem of this multivariable process is resolved as an optimization problem and multiloop PI controllers are designed by exploiting the powerful global search features of GA. An estimator is designed to provide the compositions which serve as inferential measurements to the controllers. The performance of the proposed GA-tuned decentralized control scheme is evaluated by applying it to a metathesis reactive distillation column, and the results are compared with conventionally tuned PI controllers. The results demonstrate the better regulatory and servo performance of the GA-tuned PI controllers for composition control of reactive distillation column. ISSN : 0888-5885 [article] Genetically tuned decentralized proportional-integral controllers for composition control of reactive distillation [texte imprimé] / C. Sumana, Auteur ; Ch. Venkateswarlu, Auteur . - 2010 . - pp. 1297–1311. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 3 (Fevrier 2010) . - pp. 1297–1311 Mots-clés : Genetically--Decentralized--Proportional-Integral--Controllers--Composition--Control--Reactive--Distillation Résumé : This paper presents a genetic algorithm (GA) based autotuning method to design a decentralized proportional-integral (PI) control system for composition control of a highly interactive and nonlinear reactive distillation column. The control relevant characteristics such as nonlinearities, interactions, and stability are analyzed for assessing the complexity of the process. The objective of GA tuning is to account the multivariable interactions and nonlinear dynamics of the process to find a unique set of parameters for the control system that is robust to all kinds of disturbances. The performance function in GA is formulated by incorporating the dynamic state information of the process derived from its model for various closed-loop disturbance conditions. The controller tuning problem of this multivariable process is resolved as an optimization problem and multiloop PI controllers are designed by exploiting the powerful global search features of GA. An estimator is designed to provide the compositions which serve as inferential measurements to the controllers. The performance of the proposed GA-tuned decentralized control scheme is evaluated by applying it to a metathesis reactive distillation column, and the results are compared with conventionally tuned PI controllers. The results demonstrate the better regulatory and servo performance of the GA-tuned PI controllers for composition control of reactive distillation column. ISSN : 0888-5885

Improved fault diagnosis using dynamic kernel scatter-difference-based discriminant analysis / C. Sumana 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. 8575–8586 Titre : Improved fault diagnosis using dynamic kernel scatter-difference-based discriminant analysis Type de document : texte imprimé Auteurs : C. Sumana, Auteur ; Bhushan Mani, Auteur ; Ch. Venkateswarlu, Auteur Année de publication : 2010 Article en page(s) : pp. 8575–8586 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Discriminant  analysis Résumé : Performing fault diagnosis of nonlinear processes involving data with serial correlations, nonlinearities, and overlapping signatures is a challenging task. This article proposes a dynamic kernel scatter-difference-based discriminant analysis (DKSDA) for resolving such complex data so as to improve fault resolution for efficient diagnosis. The DKSDA considers a suitably time lagged extension of the original data and allows its transformation into high-dimensional feature space via nonlinear kernel functions, and then solves the scatter difference form of the Fisher criterion. This fault diagnosis method successfully addresses the problem of dynamic correlations that are typically associated with chemical process measurements and efficiently captures the nonlinearities in data. A systematic procedure is proposed to configure the interactive parameters, namely, the number of lags, the scatter difference, and the kernel width, which govern the performance of DKSDA. The procedure involves a two-dimensional grid search at two levels to minimize a performance criterion defined in terms of the misclassification of DKSDA scores evaluated by cross validation using the nearest mean classifiers. The performance of the proposed method is evaluated by applying it for the diagnosis of overlapping and nonoverlapping faults of Tennessee Eastman challenge process and the overlapping faults of a general multivariable nonlinear dynamic process. The comparison of results with the recently reported methods demonstrates the superior performance of DKSDA for nonlinear process fault diagnosis involving complex overlapping data. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie902019h [article] Improved fault diagnosis using dynamic kernel scatter-difference-based discriminant analysis [texte imprimé] / C. Sumana, Auteur ; Bhushan Mani, Auteur ; Ch. Venkateswarlu, Auteur . - 2010 . - pp. 8575–8586. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8575–8586 Mots-clés : Discriminant  analysis Résumé : Performing fault diagnosis of nonlinear processes involving data with serial correlations, nonlinearities, and overlapping signatures is a challenging task. This article proposes a dynamic kernel scatter-difference-based discriminant analysis (DKSDA) for resolving such complex data so as to improve fault resolution for efficient diagnosis. The DKSDA considers a suitably time lagged extension of the original data and allows its transformation into high-dimensional feature space via nonlinear kernel functions, and then solves the scatter difference form of the Fisher criterion. This fault diagnosis method successfully addresses the problem of dynamic correlations that are typically associated with chemical process measurements and efficiently captures the nonlinearities in data. A systematic procedure is proposed to configure the interactive parameters, namely, the number of lags, the scatter difference, and the kernel width, which govern the performance of DKSDA. The procedure involves a two-dimensional grid search at two levels to minimize a performance criterion defined in terms of the misclassification of DKSDA scores evaluated by cross validation using the nearest mean classifiers. The performance of the proposed method is evaluated by applying it for the diagnosis of overlapping and nonoverlapping faults of Tennessee Eastman challenge process and the overlapping faults of a general multivariable nonlinear dynamic process. The comparison of results with the recently reported methods demonstrates the superior performance of DKSDA for nonlinear process fault diagnosis involving complex overlapping data. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie902019h

Nonlinear model predictive control of reactive distillation based on stochastic optimization / Ch. Venkateswarlu in Industrial & engineering chemistry research, Vol. 47 N°18 (Septembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°18 (Septembre 2008) . - p. 6949–6960 Titre : Nonlinear model predictive control of reactive distillation based on stochastic optimization Type de document : texte imprimé Auteurs : Ch. Venkateswarlu, Auteur ; A. Damodar Reddy, Auteur Année de publication : 2008 Article en page(s) : p. 6949–6960 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Stochastic  optimization  algorithms  Nonlinear  model  predictive  control  Nonlinear  input-output  process  Single  input-single  output  control Résumé : Stochastic optimization algorithms such as genetic algorithm (GA) and simulated annealing (SA) are combined with a polynomial-type empirical process model to develop nonlinear model predictive control (NMPC) strategies, namely, GANMPC and SANMPC, in the perspective of control of a nonlinear reactive distillation column. In these strategies, the nonlinear input−output process model is cascaded itself to generate future predictions for the process output based on which the control sequence is computed by stochastic optimizers while satisfying the specified performance criteria. The performance of the proposed controllers is evaluated by applying to single input−single output (SISO) control of an ethyl acetate reactive distillation column with double-feed configuration involving an esterification reaction with azeotropism. The results demonstrate better performance of the stochastic optimization based NMPCs over a conventional proportional−integral (PI) controller, a linear model predictive controller (LMPC), and a NMPC based on sequential quadratic programming (SQP) in tracking the setpoint changes as well as stabilizing the operation in the presence of input disturbances. Although both the GANMPC and SANMPC are found to exhibit almost equal performance, the easier tuning and the lower computational effort suggests the better suitability of SANMPC for the control of a nonlinear reactive distillation column. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie070972g [article] Nonlinear model predictive control of reactive distillation based on stochastic optimization [texte imprimé] / Ch. Venkateswarlu, Auteur ; A. Damodar Reddy, Auteur . - 2008 . - p. 6949–6960. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°18 (Septembre 2008) . - p. 6949–6960 Mots-clés : Stochastic  optimization  algorithms  Nonlinear  model  predictive  control  Nonlinear  input-output  process  Single  input-single  output  control Résumé : Stochastic optimization algorithms such as genetic algorithm (GA) and simulated annealing (SA) are combined with a polynomial-type empirical process model to develop nonlinear model predictive control (NMPC) strategies, namely, GANMPC and SANMPC, in the perspective of control of a nonlinear reactive distillation column. In these strategies, the nonlinear input−output process model is cascaded itself to generate future predictions for the process output based on which the control sequence is computed by stochastic optimizers while satisfying the specified performance criteria. The performance of the proposed controllers is evaluated by applying to single input−single output (SISO) control of an ethyl acetate reactive distillation column with double-feed configuration involving an esterification reaction with azeotropism. The results demonstrate better performance of the stochastic optimization based NMPCs over a conventional proportional−integral (PI) controller, a linear model predictive controller (LMPC), and a NMPC based on sequential quadratic programming (SQP) in tracking the setpoint changes as well as stabilizing the operation in the presence of input disturbances. Although both the GANMPC and SANMPC are found to exhibit almost equal performance, the easier tuning and the lower computational effort suggests the better suitability of SANMPC for the control of a nonlinear reactive distillation column. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie070972g