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:
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
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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Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning
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.