Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm
In this paper, a novel dynamic optimization control based on multi-objective ant lion optimization (DMOALO) and deep learning algorithm is proposed, which could optimize energy consumption (EC) and effluent quality (EQ) simultaneously in the wastewater treatment processes. In order to overcome the d...
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| Veröffentlicht in: | Journal of cleaner production Jg. 345; S. 131140 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
15.04.2022
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| ISSN: | 0959-6526, 1879-1786 |
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| Abstract | In this paper, a novel dynamic optimization control based on multi-objective ant lion optimization (DMOALO) and deep learning algorithm is proposed, which could optimize energy consumption (EC) and effluent quality (EQ) simultaneously in the wastewater treatment processes. In order to overcome the difficulty that there is no clear function relationship between the dynamic parameters and the performance indicators, a novel deep belief network (DBN) model for predicting EC and EQ as objective function is proposed. Then, this objective function with constraints is solved by DMOALO method, and the optimal solution would be selected by the intelligent decision system. Finally, Proportional Integral (PI) controllers would be used to track and control these optimal dynamic parameters. DBN-DMOALO-PI optimization control strategy is evaluated in benchmark simulation model 1(BSM1), the simulation results demonstrated this novel optimization control strategy could reduce the EC significantly while meeting the standards of effluent quality parameters. EC is decreased by 3.31% compared with PI optimization control strategy. Therefore, this novel method may reduce the cost of wastewater treatment process effectively, and realize the carbon neutrality in wastewater treatment process.
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•A novel dynamic optimization control based on multi-objective ant lion optimization (DMOALO) and deep learning is proposed.•A deep learning model for predicting energy consumption (EC) and effluent quality (EQ) as objective function is proposed.•DMOALO algorithm was proposed to solve objective function overcame the dynamic characteristic difficulties of process data.•The optimization control can optimize EC and EQ simultaneously in the wastewater treatment processes. |
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| AbstractList | In this paper, a novel dynamic optimization control based on multi-objective ant lion optimization (DMOALO) and deep learning algorithm is proposed, which could optimize energy consumption (EC) and effluent quality (EQ) simultaneously in the wastewater treatment processes. In order to overcome the difficulty that there is no clear function relationship between the dynamic parameters and the performance indicators, a novel deep belief network (DBN) model for predicting EC and EQ as objective function is proposed. Then, this objective function with constraints is solved by DMOALO method, and the optimal solution would be selected by the intelligent decision system. Finally, Proportional Integral (PI) controllers would be used to track and control these optimal dynamic parameters. DBN-DMOALO-PI optimization control strategy is evaluated in benchmark simulation model 1(BSM1), the simulation results demonstrated this novel optimization control strategy could reduce the EC significantly while meeting the standards of effluent quality parameters. EC is decreased by 3.31% compared with PI optimization control strategy. Therefore, this novel method may reduce the cost of wastewater treatment process effectively, and realize the carbon neutrality in wastewater treatment process.
[Display omitted]
•A novel dynamic optimization control based on multi-objective ant lion optimization (DMOALO) and deep learning is proposed.•A deep learning model for predicting energy consumption (EC) and effluent quality (EQ) as objective function is proposed.•DMOALO algorithm was proposed to solve objective function overcame the dynamic characteristic difficulties of process data.•The optimization control can optimize EC and EQ simultaneously in the wastewater treatment processes. In this paper, a novel dynamic optimization control based on multi-objective ant lion optimization (DMOALO) and deep learning algorithm is proposed, which could optimize energy consumption (EC) and effluent quality (EQ) simultaneously in the wastewater treatment processes. In order to overcome the difficulty that there is no clear function relationship between the dynamic parameters and the performance indicators, a novel deep belief network (DBN) model for predicting EC and EQ as objective function is proposed. Then, this objective function with constraints is solved by DMOALO method, and the optimal solution would be selected by the intelligent decision system. Finally, Proportional Integral (PI) controllers would be used to track and control these optimal dynamic parameters. DBN-DMOALO-PI optimization control strategy is evaluated in benchmark simulation model 1(BSM1), the simulation results demonstrated this novel optimization control strategy could reduce the EC significantly while meeting the standards of effluent quality parameters. EC is decreased by 3.31% compared with PI optimization control strategy. Therefore, this novel method may reduce the cost of wastewater treatment process effectively, and realize the carbon neutrality in wastewater treatment process. |
| ArticleNumber | 131140 |
| Author | Zhao, Yinzhong Chen, Chen Ying, Guangguo Huang, Mingzhi Niu, Guoqiang Wan, Xin Li, Xiaoyong Xujun, Liang He, Xinzhong Yi, Xiaohui |
| Author_xml | – sequence: 1 givenname: Guoqiang surname: Niu fullname: Niu, Guoqiang organization: SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, China – sequence: 2 givenname: Xiaoyong orcidid: 0000-0001-7371-7924 surname: Li fullname: Li, Xiaoyong organization: SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, China – sequence: 3 givenname: Xin orcidid: 0000-0003-3616-1503 surname: Wan fullname: Wan, Xin organization: SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, China – sequence: 4 givenname: Xinzhong surname: He fullname: He, Xinzhong organization: Fujian Environmental Protection Design Institute Co. Ltd, Fuzhou, 350000, China – sequence: 5 givenname: Yinzhong surname: Zhao fullname: Zhao, Yinzhong email: 276795653@qq.com organization: Fujian Environmental Protection Design Institute Co. Ltd, Fuzhou, 350000, China – sequence: 6 givenname: Xiaohui surname: Yi fullname: Yi, Xiaohui organization: SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, China – sequence: 7 givenname: Chen surname: Chen fullname: Chen, Chen organization: Guangdong Key Laboratory of Water and Air Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China – sequence: 8 givenname: Liang surname: Xujun fullname: Xujun, Liang organization: School of Resources and Environmental Sciences, Quanzhou Normal University, Quanzhou, 362000, China – sequence: 9 givenname: Guangguo surname: Ying fullname: Ying, Guangguo organization: SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, China – sequence: 10 givenname: Mingzhi orcidid: 0000-0002-2592-3544 surname: Huang fullname: Huang, Mingzhi email: mingzhi.huang@m.scnu.edu.cn organization: SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, China |
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| Keywords | Wastewater activated sludge treatment processes Carbon neutrality Deep belief network Dynamic multi-objective ant lion optimization |
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| SubjectTerms | algorithms carbon Carbon neutrality Deep belief network Dynamic multi-objective ant lion optimization energy Myrmeleontidae simulation models Wastewater activated sludge treatment processes wastewater treatment |
| Title | Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm |
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