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Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

Jinna Li, Bahare Kiumarsi, Tianyou Chai

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    Summary

    This study introduces a model-free approach for optimizing industrial processes with two distinct time scales. The method uses off-policy reinforcement learning (RL) to find optimal set-points for improved operational control.

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    Area of Science:

    • Industrial process control
    • Artificial intelligence in engineering
    • Chemical engineering

    Background:

    • Industrial flow lines involve fast unit processes and slower operational indices.
    • Optimizing these two-time-scale systems presents significant control challenges.

    Purpose of the Study:

    • To develop a model-free optimal control solution for two-time-scale industrial processes.
    • To leverage reinforcement learning for real-time operational set-point optimization.

    Main Methods:

    • Modeling of fast unit process control loops and slow operational index dynamics.
    • Formulation of an optimal operational control problem for industrial processes.
    • Development of a zero-sum game off-policy reinforcement learning algorithm.

    Main Results:

    • The proposed off-policy RL algorithm effectively finds optimal set-points using real-time data.
    • Simulations on an industrial flotation process demonstrate the method's effectiveness.
    • The model-free approach overcomes limitations of traditional control strategies.

    Conclusions:

    • Off-policy reinforcement learning offers a robust solution for optimizing complex industrial processes with multiple time scales.
    • This approach enables adaptive and efficient control for improved operational performance.
    • The developed method shows promise for real-world industrial applications.