Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms
In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system.
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Brazilian Society of Chemical Engineering
2009
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oai:scielo:S0104-663220090001000112009-03-10Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic AlgorithmsMarcolla,R. F.Machado,R. A. F.Cancelier,A.Claumann,C. A.Bolzan,A. Predictive Control Neural Networks Genetic Algorithms Polystyrene Artificial Intelligence In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system.info:eu-repo/semantics/openAccessBrazilian Society of Chemical EngineeringBrazilian Journal of Chemical Engineering v.26 n.1 20092009-03-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322009000100011en10.1590/S0104-66322009000100011 |
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Marcolla,R. F. Machado,R. A. F. Cancelier,A. Claumann,C. A. Bolzan,A. |
| spellingShingle |
Marcolla,R. F. Machado,R. A. F. Cancelier,A. Claumann,C. A. Bolzan,A. Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
| author_facet |
Marcolla,R. F. Machado,R. A. F. Cancelier,A. Claumann,C. A. Bolzan,A. |
| author_sort |
Marcolla,R. F. |
| title |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
| title_short |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
| title_full |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
| title_fullStr |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
| title_full_unstemmed |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
| title_sort |
modeling techniques and processes control application based on neural networks with on-line adjustment using genetic algorithms |
| description |
In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system. |
| publisher |
Brazilian Society of Chemical Engineering |
| publishDate |
2009 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322009000100011 |
| work_keys_str_mv |
AT marcollarf modelingtechniquesandprocessescontrolapplicationbasedonneuralnetworkswithonlineadjustmentusinggeneticalgorithms AT machadoraf modelingtechniquesandprocessescontrolapplicationbasedonneuralnetworkswithonlineadjustmentusinggeneticalgorithms AT canceliera modelingtechniquesandprocessescontrolapplicationbasedonneuralnetworkswithonlineadjustmentusinggeneticalgorithms AT claumannca modelingtechniquesandprocessescontrolapplicationbasedonneuralnetworkswithonlineadjustmentusinggeneticalgorithms AT bolzana modelingtechniquesandprocessescontrolapplicationbasedonneuralnetworkswithonlineadjustmentusinggeneticalgorithms |
| _version_ |
1756411215241805824 |