Application of Neural Network Algorithm in Propylene Distillation

TL;DR

This paper explores the use of neural network algorithms to model and control the complex relationships in propylene distillation, aiming to improve yield and process accuracy without requiring detailed mechanistic understanding.

Contribution

It introduces a neural network model specifically applied to propylene distillation, demonstrating its effectiveness in handling complex process relationships.

Findings

Neural networks accurately predict product concentrations.

Improved control of distillation process.

Enhanced propylene yield in production.

Abstract

Artificial neural network modeling does not need to consider the mechanism. It can map the implicit relationship between input and output and predict the performance of the system well. At the same time, it has the advantages of self-learning ability and high fault tolerance. The gas-liquid two phases in the rectification tower conduct interphase heat and mass transfer through countercurrent contact. The functional relationship between the product concentration at the top and bottom of the tower and the process parameters is extremely complex. The functional relationship can be accurately controlled by artificial neural network algorithms. The key components of the propylene distillation tower are the propane concentration at the top of the tower and the propylene concentration at the bottom of the tower. Accurate measurement of them plays a key role in increasing propylene yield in ethylene production enterprises. This article mainly introduces the neural network model and its application in the propylene distillation tower.