Reinforcement Learning-Based Nonlinear Model Predictive Controller for a Jacketed Reactor: A Machine Learning Concept Validation Using Jetson Orin

Abstract

In this research work authors have experimentally validated a blend of Machine Learning and Nonlinear Model Predictive Control (NMPC) framework designed to track the temperature profile in a Batch Reactor (BR) with an actor-critic reinforcement learning (A2CRL) methodology for dynamic weight updates. Recurrent Neural Network (RNN)-based approach for modeling is used for the open loop data collected from the lab scale batch reactor. Batch reactors are extensively utilized in industries like specialty chemicals, pharmaceuticals, and food processing because of their adaptability, especially for small-to-medium-scale production, intricate reaction dynamics, and diverse operational conditions. Thermal runaway in batch reactor is still an open-ended problem in process industry to address. The actor-critic method proficiently integrates policy optimization and value function estimates to dynamically regulate the heat produced by exothermic reactions. RNNs are employed to capture temporal dependencies in the system dynamics, enabling more accurate predictions and efficient control actions. The proposed framework is trained using open-loop experimental data and optimized to dynamically adjust the coolant flow rate, ensuring precise temperature regulation and stability. Compared to existing deep learning-based NMPC implementations, the proposed actor-critic methodology enhances NMPC controller performance by balancing prediction accuracy and real-time computational efficiency. Results demonstrate significant improvements in process efficiency, energy consumption reduction, and operational safety, validating the potential of this approach for deployment in industrial-scale batch reactor systems.