An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery
The sugarcane crushing stage is one of the most important technologies being developed at the moment. In this paper, the control of the multiple-stage evaporation system was addressed, as it is a crucial stage in the first- and second-generation ethanol production from sugarcane. A neural network mo...
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| Format: | Article |
| Language: | English |
| Published: |
Croatian Society of Chemical Engineers
2023-04-01
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| Series: | Chemical and Biochemical Engineering Quarterly |
| Subjects: | |
| Online Access: | http://silverstripe.fkit.hr/cabeq/assets/Uploads/02-2-2023.pdf |
| _version_ | 1797849792295993344 |
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| author | E. Emori M. A. D. S. S. Ravagnani C. B. B. Costa |
| author_facet | E. Emori M. A. D. S. S. Ravagnani C. B. B. Costa |
| author_sort | E. Emori |
| collection | DOAJ |
| description | The sugarcane crushing stage is one of the most important technologies being developed at the moment. In this paper, the control of the multiple-stage evaporation system was addressed, as it is a crucial stage in the first- and second-generation ethanol production from sugarcane. A neural network model was proposed based on a dynamic phenomenological model developed in EMSO (Environment for Modeling, Simulation and Optimization). The phenomenological model was used to build a neural network prediction model for an MPC (Model Predictive Control) scheme using a DMC (Dynamic Matrix Control) algorithm. Simulations were carried out to evaluate the performance for tracking the set-point. Also, disturbance rejection tests were performed, considering different step disturbances. The analysis demonstrated that the MPC scheme performed well in the tests and showed superiority when compared to classical PID controllers. |
| first_indexed | 2024-04-09T18:49:51Z |
| format | Article |
| id | doaj.art-c886bd5890b049488426b426932e1283 |
| institution | Directory Open Access Journal |
| issn | 0352-9568 1846-5153 |
| language | English |
| last_indexed | 2024-04-09T18:49:51Z |
| publishDate | 2023-04-01 |
| publisher | Croatian Society of Chemical Engineers |
| record_format | Article |
| series | Chemical and Biochemical Engineering Quarterly |
| spelling | doaj.art-c886bd5890b049488426b426932e12832023-04-09T19:16:36ZengCroatian Society of Chemical EngineersChemical and Biochemical Engineering Quarterly0352-95681846-51532023-04-01371173210.15255/CABEQ.2022.2048An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane BiorefineryE. Emori0M. A. D. S. S. Ravagnani1C. B. B. Costa2Chemical Engineering Department, Universidade Estadual de Maringá, Av. Colombo, 5790 Bloco D90, CEP 87020-290, Maringá, PR, BrazilChemical Engineering Department, Universidade Estadual de Maringá, Av. Colombo, 5790 Bloco D90, CEP 87020-290, Maringá, PR, BrazilChemical Engineering Department, Universidade Estadual de Maringá, Av. Colombo, 5790 Bloco D90, CEP 87020-290, Maringá, PR, BrazilThe sugarcane crushing stage is one of the most important technologies being developed at the moment. In this paper, the control of the multiple-stage evaporation system was addressed, as it is a crucial stage in the first- and second-generation ethanol production from sugarcane. A neural network model was proposed based on a dynamic phenomenological model developed in EMSO (Environment for Modeling, Simulation and Optimization). The phenomenological model was used to build a neural network prediction model for an MPC (Model Predictive Control) scheme using a DMC (Dynamic Matrix Control) algorithm. Simulations were carried out to evaluate the performance for tracking the set-point. Also, disturbance rejection tests were performed, considering different step disturbances. The analysis demonstrated that the MPC scheme performed well in the tests and showed superiority when compared to classical PID controllers.http://silverstripe.fkit.hr/cabeq/assets/Uploads/02-2-2023.pdfmodel predictive controlneural networkmultiple-effect evaporationemsosecond-generation ethanoldynamic matrix control |
| spellingShingle | E. Emori M. A. D. S. S. Ravagnani C. B. B. Costa An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery Chemical and Biochemical Engineering Quarterly model predictive control neural network multiple-effect evaporation emso second-generation ethanol dynamic matrix control |
| title | An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery |
| title_full | An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery |
| title_fullStr | An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery |
| title_full_unstemmed | An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery |
| title_short | An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery |
| title_sort | advanced control strategy for the evaporation section of an integrated first and second generation ethanol sugarcane biorefinery |
| topic | model predictive control neural network multiple-effect evaporation emso second-generation ethanol dynamic matrix control |
| url | http://silverstripe.fkit.hr/cabeq/assets/Uploads/02-2-2023.pdf |
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