Open AccessStudy on chemical looping reforming reaction of methane based on PSO-BP neural network
Author(s) -
Yongbin Liu,
Xiaoxun Ma,
Long Xu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/729/1/012107
Subject(s) - calcination , artificial neural network , methane , generalization , network model , materials science , biological system , chemistry , mathematics , computer science , catalysis , artificial intelligence , mathematical analysis , organic chemistry , biology , biochemistry
Through the study of methane chemical looping reforming reaction, the BP neural network model optimized by the PSO algorithm was established, and the network structure of the PSO-BP model was determined to be 9-11-3. After training the model, the mean square error of the network was finally stabilized at 0.013509, and the learning rate was finally fixed at 0.083453, while the fitting degree of the training sample and the test sample were both above 0.979, indicating that the network of the PSO-BP model had strong learning ability and generalization capability, and was a simulation prediction model with good performance. The PSO-BP model was used to simulate the full set of experimental condition data, and the most experimental condition was the cerium iron composite oxygen carrier prepared by co-precipitation method (No.2) with a molar ratio of 0.7/0.3, a calcination temperature of 800 °C, a calcination time of 6h, a reaction temperature of 850 °C, a reaction time of 13min, and a circulation number of 0, while it was consistent with the actual experimental.