A Reinforcement Learning-based Economic Model Predictive Control Framework for Autonomous Operation of Chemical Reactors
This work addresses the problem of autonomous operation for chemical reactors, offering an incremental improvement by combining existing EMPC and RL methods with minimal modifications.
The paper tackles the challenge of model inaccuracy in economic model predictive control (EMPC) for chemical reactors by integrating reinforcement learning (RL) for online parameter estimation, resulting in a framework that maintains stability and feasibility while enabling continuous optimization.
Economic model predictive control (EMPC) is a promising methodology for optimal operation of dynamical processes that has been shown to improve process economics considerably. However, EMPC performance relies heavily on the accuracy of the process model used. As an alternative to model-based control strategies, reinforcement learning (RL) has been investigated as a model-free control methodology, but issues regarding its safety and stability remain an open research challenge. This work presents a novel framework for integrating EMPC and RL for online model parameter estimation of a class of nonlinear systems. In this framework, EMPC optimally operates the closed loop system while maintaining closed loop stability and recursive feasibility. At the same time, to optimize the process, the RL agent continuously compares the measured state of the process with the model's predictions (nominal states), and modifies model parameters accordingly. The major advantage of this framework is its simplicity; state-of-the-art RL algorithms and EMPC schemes can be employed with minimal modifications. The performance of the proposed framework is illustrated on a network of reactions with challenging dynamics and practical significance. This framework allows control, optimization, and model correction to be performed online and continuously, making autonomous reactor operation more attainable.