A Reinforcement Learning Approach for Simultaneous Generation, Design and Control of Reaction-Separation Process Flowsheets

This work presents a methodology for the simultaneous generation, design, and control of chemical process flowsheets (CPF) using RL, starting from an inlet flowrate and a set of unit operations (UOs) involving reaction-separation systems, each equipped with an embedded decentralized control system. The key innovation lies in embedding neural network surrogate models, which approximate the dynamic behaviour of complex UOs within the RL environment. The proposed framework was validated through a case study focused on the reaction and separation of products at varying purities. Results demonstrate the agent’s ability to generate economically attractive CPFs that can maintain the dynamic operation of the systems in closed-loop in the presence of external disturbances.