Application of Neural Network Algorithm in Propylene Distillation
This work addresses a domain-specific problem for ethylene production enterprises by enabling more accurate measurement and control of key components in propylene distillation.
The paper tackles the complex functional relationship between product concentrations and process parameters in a propylene distillation tower by applying an artificial neural network algorithm to accurately control it, aiming to increase propylene yield in ethylene production.
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.