Hybrid modeling and energy optimization of NGL demethanizers using deep reinforcement learning
Abstract Cryogenic NGL demethanizers suffer from energy inefficiencies due to non-convex thermodynamic constraints. This study aims to implement a Deep Reinforcement Learning (DRL) framework to optimize energy consumption while respecting complex operational boundaries. A Soft Actor-Critic (SAC) agent was employed, tra...