Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approach
In recirculating aquaculture systems (RASs), degassers maintain optimal water quality by removing dissolved carbon dioxide (CO2). The performance of a degasser is generally evaluated based on its standard stripping efficiency (SSE), which is affected by its operating parameters. The present study ai...
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| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525004022 |
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| author | Subha M. Roy Hyunsoo Choi Taeho Kim |
| author_facet | Subha M. Roy Hyunsoo Choi Taeho Kim |
| author_sort | Subha M. Roy |
| collection | DOAJ |
| description | In recirculating aquaculture systems (RASs), degassers maintain optimal water quality by removing dissolved carbon dioxide (CO2). The performance of a degasser is generally evaluated based on its standard stripping efficiency (SSE), which is affected by its operating parameters. The present study aimed to optimize the air flow rate (QA), water flow rate (QW), and packing media height (PMH) to enhance degasser performance. To achieve this, an artificial neural network (ANN) and particle swarm optimization (PSO) were combined for parametric optimization and the predictive modeling of the SSE. The ANN model was trained using experimental data to predict the SSE, and PSO was then employed to optimize the operational parameters to achieve the maximum SSE. The optimal QA, QW, and PMH were found to be 355 m³/h, 35 m³/h, and 0.65 m, respectively, generating a maximum SSE of 0.188 kg CO2/kWh. The hybrid ANN-PSO approach was then validated by comparing experimental and predicted SSE values, with a difference between the two of only ±2.08 %. This confirms that the proposed optimization technique can reliably improve the SSE of degassers in RASs. |
| format | Article |
| id | doaj-art-0eca4729f5074871a4cd3687d2b90b6d |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-0eca4729f5074871a4cd3687d2b90b6d2025-08-20T03:16:46ZengElsevierSmart Agricultural Technology2772-37552025-12-011210117010.1016/j.atech.2025.101170Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approachSubha M. Roy0Hyunsoo Choi1Taeho Kim2Smart Aquaculture Research Center, Chonnam National University, Yeosu 59626, Republic of KoreaSmart Aquaculture Research Center, Chonnam National University, Yeosu 59626, Republic of KoreaSmart Aquaculture Research Center, Chonnam National University, Yeosu 59626, Republic of Korea; Department of Marine Production Management, Chonnam National University, Yeosu 59626, Republic of Korea; aCorresponding author.In recirculating aquaculture systems (RASs), degassers maintain optimal water quality by removing dissolved carbon dioxide (CO2). The performance of a degasser is generally evaluated based on its standard stripping efficiency (SSE), which is affected by its operating parameters. The present study aimed to optimize the air flow rate (QA), water flow rate (QW), and packing media height (PMH) to enhance degasser performance. To achieve this, an artificial neural network (ANN) and particle swarm optimization (PSO) were combined for parametric optimization and the predictive modeling of the SSE. The ANN model was trained using experimental data to predict the SSE, and PSO was then employed to optimize the operational parameters to achieve the maximum SSE. The optimal QA, QW, and PMH were found to be 355 m³/h, 35 m³/h, and 0.65 m, respectively, generating a maximum SSE of 0.188 kg CO2/kWh. The hybrid ANN-PSO approach was then validated by comparing experimental and predicted SSE values, with a difference between the two of only ±2.08 %. This confirms that the proposed optimization technique can reliably improve the SSE of degassers in RASs.http://www.sciencedirect.com/science/article/pii/S2772375525004022Artificial neural networkParticle swarm optimizationStandard stripping efficiencyDegasserOperating parameterRecirculating aquaculture system |
| spellingShingle | Subha M. Roy Hyunsoo Choi Taeho Kim Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approach Smart Agricultural Technology Artificial neural network Particle swarm optimization Standard stripping efficiency Degasser Operating parameter Recirculating aquaculture system |
| title | Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approach |
| title_full | Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approach |
| title_fullStr | Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approach |
| title_full_unstemmed | Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approach |
| title_short | Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approach |
| title_sort | predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ann pso approach |
| topic | Artificial neural network Particle swarm optimization Standard stripping efficiency Degasser Operating parameter Recirculating aquaculture system |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525004022 |
| work_keys_str_mv | AT subhamroy predictivemodelingandoptimizationofdegasserefficiencyinrecirculatingaquaculturesystemsusingahybridannpsoapproach AT hyunsoochoi predictivemodelingandoptimizationofdegasserefficiencyinrecirculatingaquaculturesystemsusingahybridannpsoapproach AT taehokim predictivemodelingandoptimizationofdegasserefficiencyinrecirculatingaquaculturesystemsusingahybridannpsoapproach |