Optimal control of batch processes via a deterministic Q-learning method

TL;DR

This paper introduces a deterministic Q-learning approach for robust online control of batch reactors, enabling adaptation to unplanned changes and improving process optimization over traditional time-based methods.

Contribution

It presents a novel deterministic Q-learning method tailored for real-time optimization of batch processes, enhancing robustness against system disturbances.

Findings

Q-learning outperforms traditional methods in handling unplanned process changes

The proposed method achieves higher final product yields

It effectively minimizes undesired side products

Abstract

Dynamic optimization of nonlinear chemical systems -- such as batch reactors -- should be applied online, and the suitable control taken should be according to the current state of the system rather than the current time instant. The recent state of the art methods applies the control based on the current time instant only. This is not suitable for most cases, as it is not robust to possible changes in the system. This paper proposes a Deterministic Q-Learning method to conduct robust online optimization of batch reactors. In this paper, the Q-Learning method is applied on simple batch reactor models; and in order to show the effectiveness of the proposed method the results are compared to other dynamic optimization methods. The main advantage of the Q-learning method or the proposed method is that it can accommodate unplanned changes during the process via changing the control action; i.e. the main advantage of the proposed method that it can overcome sudden changes during the reaction. In general, we try to maximize the final product obtained or meet certain specifications of the products (e.g. minimize side products).