Application of Artificial Neural Network in the Control and Optimization of Distillation Tower

TL;DR

This paper explores how Artificial Neural Networks can effectively model, control, and optimize distillation towers, overcoming traditional control challenges due to the complex, nonlinear relationships involved.

Contribution

It introduces the application of ANN for distillation tower control and optimization, highlighting its advantages over traditional methods.

Findings

ANN provides fast response and strong robustness.

ANN effectively models complex nonlinear relationships.

Improves control and optimization efficiency.

Abstract

Distillation is a unit operation with multiple input parameters and multiple output parameters. It is characterized by multiple variables, coupling between input parameters, and non-linear relationship with output parameters. Therefore, it is very difficult to use traditional methods to control and optimize the distillation column. Artificial Neural Network (ANN) uses the interconnection between a large number of neurons to establish the functional relationship between input and output, thereby achieving the approximation of any non-linear mapping. ANN is used for the control and optimization of distillation tower, with short response time, good dynamic performance, strong robustness, and strong ability to adapt to changes in the control environment. This article will mainly introduce the research progress of ANN and its application in the modeling, control and optimization of distillation towers.