Application of Neural Network Algorithm in Propylene Distillation
Journal of Engineering Research and Reports · pp. 53–63 · Published 1 Sep 2021
10.9734/jerr/2021/v20i1217420Abstract
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 development process of neural network model and its application progress in propylene distillation tower.
Cited by 16
Jing Sun, Qiong Chen · Asian Journal of Chemical Sciences · 2022
Mengyu Wang, Zhu Zhu · Asian Journal of Chemical Sciences · 2022
Jing Sun, Qi Tang · Journal of Engineering Research and Reports · 2021
Zhihui Zhao, Jiying Li · Journal of Engineering Research and Reports · 2022
Chunyu Wang, Lujie Wang · Journal of Engineering Research and Reports · 2023
Ming Yin · 2023 World Conference on Communication & Computing (WCONF) · 2023
V. V. Klimchenko, O. Yu. Snegirev, S. A. Shevlyagina · Automation and Remote Control · 2022
Showing 7 of 16 known citations — external sources report more than can currently be individually listed.
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