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    This study introduces a novel algorithm for wastewater treatment processes to enhance operational stability. The distribution-prediction-based robust multiobjective optimization (DP-RMO) algorithm effectively manages time-linked uncertainties, improving effluent quality and reducing costs.

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    Area of Science:

    • Environmental Engineering
    • Optimization Techniques

    Background:

    • Wastewater treatment processes (WWTP) face operational challenges due to uncertainties.
    • Time-linkage uncertainty in successive WWTP stages complicates robust optimization.

    Purpose of the Study:

    • To propose a distribution-prediction-based robust multiobjective optimization (DP-RMO) algorithm.
    • To enhance the operation stability of WWTP by obtaining robust optimal set points.
    • To address time-linkage uncertainty in effluent quality (EQ) and operation cost (OC) objectives.

    Main Methods:

    • Established robust multiobjective optimization (MOO) objectives using adaptive kernel functions.
    • Developed a Gaussian process (GP) based data-driven predictor to capture time-linkage uncertainty.
    • Implemented a self-adjustment evolutionary strategy to optimize robust objective functions.

    Main Results:

    • The DP-RMO algorithm effectively reduces the adverse effects of time-linkage uncertainties.
    • Optimal set points obtained via DP-RMO demonstrated improved EQ and OC.
    • Robustness performance was maintained while enhancing EQ and OC.

    Conclusions:

    • DP-RMO offers a robust solution for managing complex uncertainties in WWTP.
    • The algorithm enhances operational stability and economic efficiency in wastewater treatment.
    • DP-RMO provides a viable approach for dynamic optimization of WWTP.