Dynamic Multi-objective Optimization Applied to Biomethanation Process
Dynamic mathematical models could be beneficial for understanding and simulating processes to achieve an optimal operation. The optimum, however, could depend on several variables that can be conflicting. In this regard, Dynamic Multi-objective Optimization (DMO) is necessary for the trade-off betwe...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIDIC Servizi S.r.l.
2022-11-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/12901 |
| _version_ | 1811213588578172928 |
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| author | Juan C. Acosta-Pavas Carlos E. Robles-Rodríguez Camilo A. Suarez Méndez Jerome Morchain Claire Dumas Arnaud Cockx Cesar A. Aceves-Lara |
| author_facet | Juan C. Acosta-Pavas Carlos E. Robles-Rodríguez Camilo A. Suarez Méndez Jerome Morchain Claire Dumas Arnaud Cockx Cesar A. Aceves-Lara |
| author_sort | Juan C. Acosta-Pavas |
| collection | DOAJ |
| description | Dynamic mathematical models could be beneficial for understanding and simulating processes to achieve an optimal operation. The optimum, however, could depend on several variables that can be conflicting. In this regard, Dynamic Multi-objective Optimization (DMO) is necessary for the trade-off between several objectives. This work aims at proposing a DMO as a control strategy integrating two optimization problems. The objective is to maximize the yield and productivity of the biomethanation processes by modifying the inlet liquid and injected gas flowrates. First, multi-objective optimization was applied. Three Pareto optimal points were selected to develop five cases in dynamic optimization. Case 1 corresponded to experimental data. Cases 2, 3, and 4 considered as objectives the maximum productivity, maximum productivity and yield, and maximum yield, respectively. Case 5 was performed to assess a switch between objectives. For case 3, the yield decreased to 0.97 times, while the productivity increased 3.26 times with respect to case 1. The injected gas flowrate ranged from 2.69 to 8.43 ?? 3 ?? , and the inlet liquid flowrate reached an approximate value of 7.0×10-3 ?? 3 ?? . These results showed the feasibility and good efficiency of the proposed methodology. |
| first_indexed | 2024-04-12T05:49:06Z |
| format | Article |
| id | doaj.art-f35be13ceab64fc5bf497492d2de1d6a |
| institution | Directory Open Access Journal |
| issn | 2283-9216 |
| language | English |
| last_indexed | 2024-04-12T05:49:06Z |
| publishDate | 2022-11-01 |
| publisher | AIDIC Servizi S.r.l. |
| record_format | Article |
| series | Chemical Engineering Transactions |
| spelling | doaj.art-f35be13ceab64fc5bf497492d2de1d6a2022-12-22T03:45:22ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162022-11-019610.3303/CET2296054Dynamic Multi-objective Optimization Applied to Biomethanation ProcessJuan C. Acosta-PavasCarlos E. Robles-RodríguezCamilo A. Suarez MéndezJerome MorchainClaire DumasArnaud CockxCesar A. Aceves-LaraDynamic mathematical models could be beneficial for understanding and simulating processes to achieve an optimal operation. The optimum, however, could depend on several variables that can be conflicting. In this regard, Dynamic Multi-objective Optimization (DMO) is necessary for the trade-off between several objectives. This work aims at proposing a DMO as a control strategy integrating two optimization problems. The objective is to maximize the yield and productivity of the biomethanation processes by modifying the inlet liquid and injected gas flowrates. First, multi-objective optimization was applied. Three Pareto optimal points were selected to develop five cases in dynamic optimization. Case 1 corresponded to experimental data. Cases 2, 3, and 4 considered as objectives the maximum productivity, maximum productivity and yield, and maximum yield, respectively. Case 5 was performed to assess a switch between objectives. For case 3, the yield decreased to 0.97 times, while the productivity increased 3.26 times with respect to case 1. The injected gas flowrate ranged from 2.69 to 8.43 ?? 3 ?? , and the inlet liquid flowrate reached an approximate value of 7.0×10-3 ?? 3 ?? . These results showed the feasibility and good efficiency of the proposed methodology.https://www.cetjournal.it/index.php/cet/article/view/12901 |
| spellingShingle | Juan C. Acosta-Pavas Carlos E. Robles-Rodríguez Camilo A. Suarez Méndez Jerome Morchain Claire Dumas Arnaud Cockx Cesar A. Aceves-Lara Dynamic Multi-objective Optimization Applied to Biomethanation Process Chemical Engineering Transactions |
| title | Dynamic Multi-objective Optimization Applied to Biomethanation Process |
| title_full | Dynamic Multi-objective Optimization Applied to Biomethanation Process |
| title_fullStr | Dynamic Multi-objective Optimization Applied to Biomethanation Process |
| title_full_unstemmed | Dynamic Multi-objective Optimization Applied to Biomethanation Process |
| title_short | Dynamic Multi-objective Optimization Applied to Biomethanation Process |
| title_sort | dynamic multi objective optimization applied to biomethanation process |
| url | https://www.cetjournal.it/index.php/cet/article/view/12901 |
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