Application of Neural Network Algorithm in Optimal Control of Ethylene Distillation Tower

Abstract

For some nonlinear dynamic systems with uncertainties or disturbances, neural networks can perform intelligent cognition and simulation on them, achieve a good system description, and further realize intelligent control. Aiming at the ethylene rectification process, in order to avoid the time delay of complex rectification process modeling and large-scale process simulation software interface program, and to improve the simulation operation speed, the optimization model combined with the learning function of the neural network is used for the simulation calculation of the rectification process. It can meet the time and accuracy requirements of online optimization. This article outlines several commonly used neural network algorithms and their related applications in ethylene distillation, aiming to provide reference for the development and innovation of industry technology.

Artificial Neural Network and Its Application in Image Recognition

“…"Artificial neural network is a complex network structure composed of a large number of neurons connected with each other. It can be used in some aspects by simulating some mechanisms of the brain" [1,2]. "The unique structure and information processing method of neural network make it have obvious advantages in many aspects and wide application fields.…”

Artificial Neural Network Model and Its Application in Signal Processing

“…Because of the characteristics of self-organization, self-learning and parallel distributed information processing, neural networks have been widely used in pattern recognition, signal processing and optimization problems. The main application fields are intelligent driving [5][6][7], robot control, automatic control of power system, chemical process control and optimization [8][9][10][11], image processing [12][13][14], health care, medical treatment, signal processing [15,16]. This paper will introduce the development process of neural networks and several commonly used neural network models, and summarize the application research progress of neural networks in the field of signal processing.…”