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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