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Use of a Battery of Chemical and Ecotoxicological Methods for the Assessment of the Efficacy of Wastewater Treatment Processes to Remove Estrogenic Potency
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Dynamic MOPSO-Based Optimal Control for Wastewater Treatment Process.

Hong-Gui Han, Zheng Liu, Wei Lu

    IEEE Transactions on Cybernetics
    |July 23, 2019
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    This summary is machine-generated.

    A new optimal controller using dynamic multiobjective particle swarm optimization (DMOPSO) improves wastewater treatment plant (WWTP) performance. This controller effectively balances effluent quality, cost, and stability for complex, time-varying conditions.

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    Area of Science:

    • Environmental Engineering
    • Control Systems Engineering
    • Computational Intelligence

    Background:

    • Wastewater treatment process (WWTP) operations present complex, time-varying characteristics, necessitating advanced control strategies.
    • Existing control methods struggle to address the dynamic, multiobjective nature of WWTPs, involving conflicting criteria like effluent quality, cost, and stability.

    Purpose of the Study:

    • To develop a novel optimal controller based on a dynamic multiobjective particle swarm optimization (DMOPSO) algorithm for WWTPs.
    • To address the dynamic and conflicting objectives of effluent quality (EQ), operation cost, and operation stability in WWTP control.
    • To enhance the optimization framework for capturing complex WWTP dynamics and improve control performance.

    Main Methods:

    • Development of a DMOPSO algorithm with an adaptive global best selection mechanism to solve the multiobjective optimization problem (MOP).
    • Integration of a novel optimization framework designed to handle time-varying and conflicting objectives inherent in WWTP operations.
    • Testing and validation of the proposed DMOPSO-based optimal controller (DMOPSO-OC) using the Benchmark Simulation Model No. 1 (BSM1) and a real WWTP.

    Main Results:

    • The DMOPSO-OC demonstrated significant improvements in optimal control performance compared to existing methods.
    • The controller effectively managed multiple conflicting criteria, including effluent quality, operation cost, and operation stability.
    • The integrated optimization framework successfully captured complex WWTP characteristics and dynamic changes.

    Conclusions:

    • The proposed DMOPSO-OC offers a robust solution for optimizing WWTP operations under dynamic and multiobjective conditions.
    • The DMOPSO algorithm with adaptive selection enhances the synthesis of optimal control strategies for complex environmental systems.
    • The controller's effectiveness is validated through simulation and real-world implementation, showing its practical applicability.