Application of Artificial Neural Networks in Chemical Process Control

An important data-driven model is the artificial neural network. Artificial networks have been widely used in many domains of chemical processes due to its robustness, fault tolerance, self-adaptive capability, and self-learning ability. For process with nonlinearity strong coupling, can control well make up for lack traditional PID technology. As a result, ANN has emerged as significant positive trend control. In this paper, principle, development history, common structure are first outlined. Then role introduced three aspects: improved control, predictive hybrid models. The effect reflected by comparison. Finally, it proposed that be more developed applying deep learning algorithms developing multiple models