Machine learning meets process control: Unveiling the potential of LSTMc

Abstract

AbstractIn the past three decades, proportional‐integral/PI‐differential (PI/PID) controllers and model predictive controller (MPCs) have predominantly governed complex chemical process control. Despite their advancements, these approaches have limitations, with PI/PID controllers requiring scenario‐specific tuning and MPC being computationally demanding. To tackle these issues, we introduce the long‐short‐term‐memory (LSTM)‐controller (LSTMc), a model‐free, data‐driven framework leveraging LSTM networks' robust time‐series prediction capabilities. The LSTMc predicts subsequent manipulated inputs by evaluating state evolution and error dynamics from both the current and previous time‐steps, which proved effective in our dextrose batch crystallization case study. Remarkably, the achieves less than 2% set‐point deviation, three times better than MPCs, and retains robustness even with 10%–15% sensor noise. With these results, LSTMc emerges as a promising alternative for process control, adeptly adjusting to changing process conditions and set‐points, providing efficient computation for an optimal input profile, and effectively filtering out common industrial process noise.