Artificial Neural Network and its Application Research Progress in Distillation

TL;DR

This paper reviews the progress of artificial neural networks in distillation, highlighting their advantages in chemical process simulation and the expanding research and applications in this field.

Contribution

It provides a comprehensive overview of neural network applications in distillation, emphasizing recent developments and research trends domestically and internationally.

Findings

Neural networks enable high-precision simulation of distillation processes.

They offer advantages like self-learning and high-speed search in chemical process optimization.

Research in this area is rapidly expanding with technological advancements.

Abstract

Artificial neural networks learn various rules and algorithms to form different ways of processing information, and have been widely used in various chemical processes. Among them, with the development of rectification technology, its production scale continues to expand, and its calculation requirements are also more stringent, because the artificial neural network has the advantages of self-learning, associative storage and high-speed search for optimized solutions, it can make high-precision simulation predictions for rectification operations, so it is widely used in the chemical field of rectification. This article gives a basic overview of artificial neural networks, and introduces the application research of artificial neural networks in distillation at home and abroad.