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What is Optimal Control of Industrial Processes about?

This paper introduces a novel method combining singular perturbation theory and reinforcement learning to optimize operational control in industrial processes characterized by two-time-scale dynamics and unknown models. The approach decomposes the original optimization problem into manageable subproblems, allowing for efficient real-time learning of control policies without requiring complete knowledge of system dynamics. An industrial thickener example demonstrates the effectiveness of the proposed method

Author

Sabir Hasnain

Language

EN