CNN-LSTM-Based Nonlinear Model Predictive Controller for Temperature Trajectory Tracking in a Batch Reactor

Abstract

Batch reactors are type of chemical reactors, where the reactants are loaded to process for a defined batch time and the products are removed after the polymerization reaction completion. Specialty chemicals and food processing industries widely use BRs due to their versatility and suitability for handling small-to medium-scale production, complex reactions, and varying reaction conditions. This article employs a CNN-LSTM-based nonlinear model predictive controller (NMPC) to effectively track the temperature profile of a BR. This model offers significant advantages in NMPC by leveraging convolutional neural networks (CNNs) to capture spatial features and long short-term memory (LSTM) networks to manage temporal dependencies, thus enhancing prediction accuracy and control performance. The approach involves training the CNN-LSTM model using input and output data obtained from open-loop experimentation with the BR. This model evaluates the goal of optimizing the coolant flow rate while managing the heat generated by the exothermic reaction within the reactor. Additionally, a heuristic method incorporating a sigmoidal weighting functions are utilized to improve the computational efficiency of the model. The successful implementation of this CNN-LSTM-based NMPC model demonstrates its potential for large-scale usage in industrial applications. By providing accurate temperature predictions and optimizing control actions, this approach can enhance process efficiency, reduce energy consumption, and improve safety in various pharmaceutical industries.