Prediction and optimization model of C4 olefin production based on random forest and chaotic particle swarm optimization

Liyao Wang; Hongmei Xu

doi:10.1117/12.2635998

5 May 2022 Prediction and optimization model of C4 olefin production based on random forest and chaotic particle swarm optimization

Liyao Wang, Hongmei Xu

Author Affiliations +

Proceedings Volume 12245, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022); 122450Z (2022) https://doi.org/10.1117/12.2635998
Event: International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), 2022, Sanya, China

Abstract

Predicting C4 olefin output and optimising operating variables in the production process are of great practical significance for China's petrochemical enterprises to reduce production costs and improve the quality of petrochemical products. Firstly, this paper uses the nonlinear least square method (PLS), combines the advantages of statistical regression and neural network, and uses nonlinear partial least square regression method based on a neural network to establish the nonparametric model of catalyst and temperature on ethanol conversion rate and C4 olefin selectivity. Based on the nonlinear partial least squares regression method based on neural network, the random forest model is constructed to construct decision trees for different catalysts and temperatures. Next, a chaotic particle swarm optimization algorithm is constructed to optimize the parameters. The discrete particle swarm optimization algorithm is creatively designed and initialized by the label propagation method, and the feeding scheme for optimizing product quality is successfully provided, and the optimization result is good.

Citation Download Citation

Liyao Wang and Hongmei Xu "Prediction and optimization model of C4 olefin production based on random forest and chaotic particle swarm optimization", Proc. SPIE 12245, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), 122450Z (5 May 2022); https://doi.org/10.1117/12.2635998

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