Neural network based modelling and control in batch reactor
The use of neural networks (NNs) in all aspects of process engineering activities, such as modelling, design, optimization and control has considerably increased in recent years (Mujtaba and Hussain, 2001). In this work, three different types of nonlinear control strategies are developed and impleme...
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| Format: | Article |
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Elsevier
2006
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| _version_ | 1825719317860188160 |
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| author | Mujtaba, I.M. Aziz, N. Hussain, Mohd Azlan |
| author_facet | Mujtaba, I.M. Aziz, N. Hussain, Mohd Azlan |
| author_sort | Mujtaba, I.M. |
| collection | UM |
| description | The use of neural networks (NNs) in all aspects of process engineering activities, such as modelling, design, optimization and control has considerably increased in recent years (Mujtaba and Hussain, 2001). In this work, three different types of nonlinear control strategies are developed and implemented in batch reactors using NN techniques. These are generic model control (GMC), direct inverse model control (DIC) and internal model control (IMC) strategies. Within the control strategies, NNs have been used as dynamic estimator, dynamic model (forward model) and control (inverse model). An exothermic complex reaction scheme in a batch reactor is considered to explain all these control strategies and their robustness. A dynamic optimization problem with a simple model is solved a priori to obtain optimal operation policy in terms of the reactor temperature with an objective to maximize the desired product in a given batch time. The resulting optimal temperature policy is used as set-point in the control study. All types of controllers performed well in tracking the optimal temperature profile and achieving target conversion to the desired product. However, the NNs used in DIC and IMC controllers need training beyond the nominal operating condition to cope with uncertainties better. |
| first_indexed | 2024-03-06T05:17:50Z |
| format | Article |
| id | um.eprints-7053 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:17:50Z |
| publishDate | 2006 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-70532019-09-25T09:16:53Z http://eprints.um.edu.my/7053/ Neural network based modelling and control in batch reactor Mujtaba, I.M. Aziz, N. Hussain, Mohd Azlan TA Engineering (General). Civil engineering (General) TP Chemical technology The use of neural networks (NNs) in all aspects of process engineering activities, such as modelling, design, optimization and control has considerably increased in recent years (Mujtaba and Hussain, 2001). In this work, three different types of nonlinear control strategies are developed and implemented in batch reactors using NN techniques. These are generic model control (GMC), direct inverse model control (DIC) and internal model control (IMC) strategies. Within the control strategies, NNs have been used as dynamic estimator, dynamic model (forward model) and control (inverse model). An exothermic complex reaction scheme in a batch reactor is considered to explain all these control strategies and their robustness. A dynamic optimization problem with a simple model is solved a priori to obtain optimal operation policy in terms of the reactor temperature with an objective to maximize the desired product in a given batch time. The resulting optimal temperature policy is used as set-point in the control study. All types of controllers performed well in tracking the optimal temperature profile and achieving target conversion to the desired product. However, the NNs used in DIC and IMC controllers need training beyond the nominal operating condition to cope with uncertainties better. Elsevier 2006 Article PeerReviewed Mujtaba, I.M. and Aziz, N. and Hussain, Mohd Azlan (2006) Neural network based modelling and control in batch reactor. Chemical Engineering Research and Design, 84 (A8). pp. 635-644. ISSN 0263-8762, DOI https://doi.org/10.1205/Cherd.05096 <https://doi.org/10.1205/Cherd.05096>. https://doi.org/10.1205/cherd.05096 doi:10.1205/Cherd.05096 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TP Chemical technology Mujtaba, I.M. Aziz, N. Hussain, Mohd Azlan Neural network based modelling and control in batch reactor |
| title | Neural network based modelling and control in batch reactor |
| title_full | Neural network based modelling and control in batch reactor |
| title_fullStr | Neural network based modelling and control in batch reactor |
| title_full_unstemmed | Neural network based modelling and control in batch reactor |
| title_short | Neural network based modelling and control in batch reactor |
| title_sort | neural network based modelling and control in batch reactor |
| topic | TA Engineering (General). Civil engineering (General) TP Chemical technology |
| work_keys_str_mv | AT mujtabaim neuralnetworkbasedmodellingandcontrolinbatchreactor AT azizn neuralnetworkbasedmodellingandcontrolinbatchreactor AT hussainmohdazlan neuralnetworkbasedmodellingandcontrolinbatchreactor |