OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.
Global Self-Optimizing Control with Active-Set Changes:
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Reinforcement learning optimizes controlled variable selection for self-optimizing control by embedding the structure in an actor network and using economic rewards, showing better dynamic performance than a steady-state baseline in a CSTR simulation under disturbances.
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Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach
OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.