Artificial Neural Network and Its Application Research Progress in Chemical Process

TL;DR

This paper reviews the development and application of artificial neural networks in chemical processes, highlighting their ability to model complex, nonlinear systems for control, diagnosis, and optimization.

Contribution

It provides a comprehensive overview of ANN principles, historical development, and recent research progress in chemical process applications.

Findings

ANN effectively models complex chemical processes.

ANN improves control and fault diagnosis accuracy.

Research progress shows increasing adoption of ANN in chemical engineering.

Abstract

Most chemical processes, such as distillation, absorption, extraction, and catalytic reactions, are extremely complex processes that are affected by multiple factors. The relationships between their input variables and output variables are non-linear, and it is difficult to optimize or control them using traditional methods. Artificial neural network (ANN) is a systematic structure composed of multiple neuron models. Its main function is to simulate multiple basic functions of the nervous system of living organisms. ANN can achieve nonlinear control without relying on mathematical models, and is especially suitable for more complex control objects. This article will introduce the basic principles and development history of artificial neural networks, and review its application research progress in chemical process control, fault diagnosis, and process optimization.