A Review of Artificial Neural Networks for Chemical Process Optimization and Compound Property Prediction

Abstract

Artificial neural networks are widely used in chemical processes because of their powerful data processing capabilities, fault tolerance, nonlinear relationship processing capabilities, and learning capabilities. This paper will introduce the development history and important models of artificial neural network, and focus on its application in chemical process optimization and prediction of physical properties of compounds.