Application of Neural Network in Optimization of Chemical Process

TL;DR

This paper reviews the use of artificial neural networks, especially BP, RBF, and CNN, in optimizing chemical processes, highlighting their effectiveness in multi-objective control and parameter enhancement.

Contribution

It provides a comprehensive overview of neural network types and their practical application in chemical process optimization, emphasizing recent advancements.

Findings

Neural networks effectively improve chemical process control.

Multi-objective optimization benefits from neural network models.

ANNs enhance process parameter accuracy.

Abstract

Artificial neural network (ANN) has been widely used due to its strong nonlinear mapping ability, fault tolerance and self-learning ability. This article summarizes the development history of artificial neural networks, introduces three common neural network types, BP neural network, RBF neural network and convolutional neural network, and focuses on the practical application in chemical process optimization, especially the results achieved in multi-objective control optimization and process parameter improvement.