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Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm

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Abstract

In this paper, for the operations of wastewater treatment processes (WWTPs), an intelligent multi-objective optimization control (IMOOC), based on an adaptive multi-objective differential evolution (AMODE) algorithm, is proposed to search for the suitable set-points to balance the treatment performance and the operational costs. In this IMOOC, the combination of an AMODE algorithm and the multi-objective critical issues helps us to fulfill all the control objectives simultaneously. To improve the optimization efficiency and achieve fast convergence, the AMODE algorithm is designed to improve the local search and the global exploration abilities: The adaptive adjustment strategies are developed to select the suitable scaling factor and crossover rate in the process of searching. Meanwhile, the multi-objective critical issues, according to the state of the processes, are given as a nonlinear multi-objective optimization problem to evaluate the operational performance of WWTPs. Therefore, once the nonlinear multi-objective optimization problem is solved at each sampling time, the most appropriate set of Pareto is selected as suitable set-points to achieve the process performance. To demonstrate the merits of our proposed method, the proposed IMOOC is applied to the Benchmark Simulation Model No. 1 of WWTPs. The results show that the proposed IMOOC effectively provides process control. The performance comparison with other algorithms also indicates that the proposed optimal strategy yields better effluent qualities and lower average operation consumption.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions, which helped improve this paper greatly. We declare that we agree to replace the corresponding author by Ying Hou, and we have no conflict of interest. This work was supported by the National Science Foundation of China under Grants 61622301 and 61533002, Beijing Natural Science Foundation under Grant 4172005.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Jun-Fei Qiao, Ying Hou & Hong-Gui Han

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    Jun-Fei Qiao, Ying Hou & Hong-Gui Han

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  1. Jun-Fei Qiao
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  2. Ying Hou
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  3. Hong-Gui Han
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Correspondence to Ying Hou.

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Qiao, JF., Hou, Y. & Han, HG. Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm. Neural Comput & Applic 31, 2537–2550 (2019). https://doi.org/10.1007/s00521-017-3212-4

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  • DOI: https://doi.org/10.1007/s00521-017-3212-4

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