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Fuzzy Neural Network-Based Model Predictive Control for Dissolved Oxygen Concentration of WWTPs

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Abstract

Dissolved oxygen (DO) concentration is a key variable in the operation of wastewater treatment processes (WWTPs). In this paper, an adaptive fuzzy neural network-based model predictive control (AFNN-MPC) is proposed for the control problem of DO concentration. The main contributions of AFNN-MPC are threefolds: First, an AFNN, based on a novel learning method with adaptive learning rate, is designed to model the unknown nonlinearities of WWTPs with high predicting performance. Second, a gradient method is used to solve the optimal control problem of AFNN-MPC to reduce the computational cost. Third, the convergence of AFNN, as well as the stability analysis of AFNN-MPC, has been given in detail. Finally, the proposed AFNN-MPC is applied to the benchmark simulation model No. 2. The promising results indicate that the proposed AFNN-MPC is a suitable solution to control DO concentration. Moreover, the comprehensive experiments clearly show the superiority and efficacy of the proposed AFNN-MPC.

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Acknowledgements

This work was supported by National Key Research and Development Project under Grants 2018YFC1900800-5, National Science Foundation of China under Grants 61890930-5 and 61622301, Beijing Natural Science Foundation under Grant 4172005 and Major National Science and Technology Project under Grant 2017ZX07104.

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Authors and Affiliations

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

    Hong-Gui Han, Zheng Liu & Jun-Fei Qiao

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

    Hong-Gui Han, Zheng Liu & Jun-Fei Qiao

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  1. Hong-Gui Han
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  2. Zheng Liu
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  3. Jun-Fei Qiao
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Correspondence to Hong-Gui Han.

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Han, HG., Liu, Z. & Qiao, JF. Fuzzy Neural Network-Based Model Predictive Control for Dissolved Oxygen Concentration of WWTPs. Int. J. Fuzzy Syst. 21, 1497–1510 (2019). https://doi.org/10.1007/s40815-019-00644-8

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