Reinforcement learning-based autonomous control of bench-scale primary separation vessel

In the process industry, smart automation of complex operations has great potential for efficient and safe operation, making it a key component for unlocking economic and sustainable large-scale production. However, real-world process units such as primary separation vessels (PSVs) pose numerous challenges, such as sensory uncertainty, nonlinear dynamics, and operational variability. This study introduces a novel autonomous control framework integrating model predictive control (MPC), reinforcement learning (RL), and state estimation techniques for building an adaptive, optimal, and safe control strategy. The proposed framework is demonstrated in a real-world scenario using a bench-scale experimental setup of the PSV that mimics the actual process. The implemented closed-loop control system accurately predicted a crucial process variable, optimized the operating point in real time, and achieved robust set-point tracking performance by tuning the controller for real process conditions. The results indicate that incorporating adaptive and data-driven techniques such as reinforcement learning into feedback control approaches is promising for building robust autonomous control strategies that maximize efficiency while respecting physical constraints, paving the way for autonomous control systems that are deployable in complex real-world scenarios.