Fuzzy inference‐based control and decision system for precise aeration of sewage treatment process
Abstract The sewage treatment systems manage to reduce pollutants of wastewater to make it reach some requirement. The core of it is to effectively control and determine intermediate aeration amount, which is always challenging. Therefore, a multi‐objective planning mechanism (multi‐objective optimi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-02-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.12082 |
| _version_ | 1811261107055099904 |
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| author | Wenru Zeng Zhiwei Guo Huiyan Zhang Jianhui Wang Xu Gao Yu Shen Samir Ibrahim Gadow |
| author_facet | Wenru Zeng Zhiwei Guo Huiyan Zhang Jianhui Wang Xu Gao Yu Shen Samir Ibrahim Gadow |
| author_sort | Wenru Zeng |
| collection | DOAJ |
| description | Abstract The sewage treatment systems manage to reduce pollutants of wastewater to make it reach some requirement. The core of it is to effectively control and determine intermediate aeration amount, which is always challenging. Therefore, a multi‐objective planning mechanism (multi‐objective optimization design combining GRA and fuzzy logic inference) that combines grey relational analysis and fuzzy logic, to find the optimal dissolved oxygen solubility for achieving better outlet water quality, is proposed. First of all, grey correlation coefficient between each optimization target and the reference target is calculated, and are converted into fuzzy inference values through the four steps. After that, it is expected to analyse the average values of process variables to obtain the optimal parameter combinations. A real‐world dataset collected from a realistic sewage treatment plant is utilized as the simulation environment to evaluate the proposed multi‐objective optimization design combining GRA and fuzzy logic inference. Experimental results show that the multi‐objective optimization design combining GRA and fuzzy logic inference makes promotion of 47.34% for fitted outlet water quality compared with the original average annual water quality. |
| first_indexed | 2024-04-12T18:58:35Z |
| format | Article |
| id | doaj.art-45dc69ebd7534d89b150b2186ced6e44 |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-04-12T18:58:35Z |
| publishDate | 2021-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-45dc69ebd7534d89b150b2186ced6e442022-12-22T03:20:15ZengWileyElectronics Letters0013-51941350-911X2021-02-0157311211510.1049/ell2.12082Fuzzy inference‐based control and decision system for precise aeration of sewage treatment processWenru Zeng0Zhiwei Guo1Huiyan Zhang2Jianhui Wang3Xu Gao4Yu Shen5Samir Ibrahim Gadow6National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing ChinaNational Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing ChinaNational Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing ChinaNational Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing ChinaNational Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing ChinaNational Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing ChinaNational Research Centre Cairo EgyptAbstract The sewage treatment systems manage to reduce pollutants of wastewater to make it reach some requirement. The core of it is to effectively control and determine intermediate aeration amount, which is always challenging. Therefore, a multi‐objective planning mechanism (multi‐objective optimization design combining GRA and fuzzy logic inference) that combines grey relational analysis and fuzzy logic, to find the optimal dissolved oxygen solubility for achieving better outlet water quality, is proposed. First of all, grey correlation coefficient between each optimization target and the reference target is calculated, and are converted into fuzzy inference values through the four steps. After that, it is expected to analyse the average values of process variables to obtain the optimal parameter combinations. A real‐world dataset collected from a realistic sewage treatment plant is utilized as the simulation environment to evaluate the proposed multi‐objective optimization design combining GRA and fuzzy logic inference. Experimental results show that the multi‐objective optimization design combining GRA and fuzzy logic inference makes promotion of 47.34% for fitted outlet water quality compared with the original average annual water quality.https://doi.org/10.1049/ell2.12082Water quality and water resourcesOptimisation techniquesStatisticsEnvironmental issuesIndustrial processesOptimisation techniques |
| spellingShingle | Wenru Zeng Zhiwei Guo Huiyan Zhang Jianhui Wang Xu Gao Yu Shen Samir Ibrahim Gadow Fuzzy inference‐based control and decision system for precise aeration of sewage treatment process Electronics Letters Water quality and water resources Optimisation techniques Statistics Environmental issues Industrial processes Optimisation techniques |
| title | Fuzzy inference‐based control and decision system for precise aeration of sewage treatment process |
| title_full | Fuzzy inference‐based control and decision system for precise aeration of sewage treatment process |
| title_fullStr | Fuzzy inference‐based control and decision system for precise aeration of sewage treatment process |
| title_full_unstemmed | Fuzzy inference‐based control and decision system for precise aeration of sewage treatment process |
| title_short | Fuzzy inference‐based control and decision system for precise aeration of sewage treatment process |
| title_sort | fuzzy inference based control and decision system for precise aeration of sewage treatment process |
| topic | Water quality and water resources Optimisation techniques Statistics Environmental issues Industrial processes Optimisation techniques |
| url | https://doi.org/10.1049/ell2.12082 |
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