AI-Enhanced Chemical Separation Based on Deep Learning for Intelligent Process Control

Abstract

Chemical process operations require fault-adaptive and energy-aware automation, yet conventional control methods often struggle to maintain robustness, fault tolerance, and energy efficiency under dynamic operating conditions. This paper presents an artificial intelligence-enhanced process monitoring and control framework for the Tennessee Eastman process that integrates a hybrid temporal convolutional neural network-bidirectional gated recurrent unit encoder for fault detection with a safety-constrained reinforcement learning-based adaptive controller. The proposed framework achieves superior fault detection performance, with an F1 score of 0.957, an area under the receiver operating characteristic curve of 0.981, and a median detection time of 10.2 s, outperforming long short-term memory and autoencoder baseline models. It also delivers improved control performance, achieving an energy index of 0.866 and a control efficiency of 1.113, while reducing energy consumption compared with the long short-term memory, autoencoder, and model predictive control baselines. These findings demonstrate that combining temporal feature learning with safety-constrained policy optimization provides a practical approach for developing more resilient, energy-efficient industrial process automation systems.