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    A new data-driven model predictive control (DDMPC) strategy stabilizes dissolved oxygen concentration (DOC) in wastewater treatment plants (WWTPs) despite unpredictable sampling times. This approach ensures stable operation under system constraints.

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    Area of Science:

    • Environmental Engineering
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Wastewater treatment processes (WWTPs) face challenges in stable dissolved oxygen concentration (DOC) control due to stochastic sampling and operational constraints.
    • Existing control strategies struggle with the assumption of periodic data acquisition, impacting system performance.

    Purpose of the Study:

    • To propose a data-driven model predictive control (DDMPC) strategy for stable control of constrained WWTPs with stochastic sampling intervals.
    • To address the difficulties in achieving stable DOC control under variable data acquisition and operational limitations.

    Main Methods:

    • A DDMPC framework was designed with an objective function considering the mathematical expectation of predicted output and system constraints.
    • A data-driven multimodel prediction structure using fuzzy neural networks (FNNs) was developed to handle stochastic sampling intervals.
    • A controller solving algorithm based on the generalized multiplier method reformulated the optimization problem with penalty functions for constraints.

    Main Results:

    • The proposed DDMPC strategy effectively handles stochastic data acquisition caused by random sampling intervals.
    • Simulations on the benchmark simulation model No. 1 (BSM1) demonstrated the strategy's ability to ensure stable system operation under constraints.
    • The DDMPC strategy successfully achieved stable control of DOC in constrained WWTPs with stochastic sampling intervals.

    Conclusions:

    • The developed DDMPC strategy provides a robust solution for controlling DOC in WWTPs with inherent stochasticity and operational constraints.
    • The approach enhances the stability and reliability of wastewater treatment processes.
    • This data-driven method offers a promising direction for advanced process control in environmental engineering.