Abstract
Reinforcement Learning (RL) is a machine learning technique which is capable of generating data and learning from it autonomously by interacting with the environment. RL has been successfully applied for learning and playing various games such as Go, Chess, Atari etc but its application to address process control and optimization problems is not trivial. There is a need for RL implementations in process industry to be safe, fast learning and explainable. A method for achieving such an implementation, for linear systems without disturbance variables, was published by the author in the past. Taking the work further, this paper proposes significant enhancements to the method in terms of ability to address severe process non-linearities, that can't be linearized, and ability to address disturbance variables explicitly in the RL problem formulation. Along with presenting the details on the enhancements, the paper also provides details on actual implementation of the enhanced method for optimization of NGL fractionation unit. Not only does the enhanced method successfully enable the RL agent to learn the non-monotonic non-linearity representing the tradeoff between production and energy consumption, it also enabled the RL agent to learn how the non-linearity changed with changes in product and utility prices. The authors believe that this will further the potential of intelligent process control and optimization capable of enabling autonomous operation in the process industry.