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Dynamic multi-objective optimization control for wastewater treatment process

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Abstract

A dynamic multi-objective optimization control (DMOOC) scheme is proposed in this paper for the wastewater treatment process (WWTP), which can dynamically optimize the set-points of dissolved oxygen concentration and nitrate level with multiple performance indexes simultaneously. To overcome the difficulty of establishing multi-objective optimization (MOO) model for the WWTP, a neural network online modeling method is proposed, requiring only the process data of the plant. Then, the constructed MOO model with constraints is solved based on the NSGA-II (non-dominated sorting genetic algorithm-II), and the optimal set-point vector is selected from the Pareto set using the defined utility function. Simulation results, based on the benchmark simulation model 1 (BSM1), demonstrate that the energy consumption can be significantly reduced applying the DMOOC than the default PID control with the fixed set-points. Moreover, a tradeoff between energy consumption and effluent quality index can be considered.

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Acknowledgements

This work was supported by the National Science Foundation of China under Grants 61203099, 61225016, 61034008 and 61004051, and by the Beijing Municipal Natural Science Foundation under Grant 4122006.

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Authors and Affiliations

  1. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China

    Junfei Qiao & Wei Zhang

  2. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China

    Wei Zhang

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  1. Junfei Qiao
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  2. Wei Zhang
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Correspondence to Wei Zhang.

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Qiao, J., Zhang, W. Dynamic multi-objective optimization control for wastewater treatment process. Neural Comput & Applic 29, 1261–1271 (2018). https://doi.org/10.1007/s00521-016-2642-8

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  • DOI: https://doi.org/10.1007/s00521-016-2642-8

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