Journal of System Simulation
Abstract
Abstract: The outlet temperature of the decomposition furnace is a key indicator in the cement production process. Aiming at the problem that traditional prediction methods only consider the influence of wind, coal, and materials, a temperature prediction model of ElasticNet combined with Long Short-Term Memory (LSTM) neural network is proposed. The ElasticNet-LSTM export temperature prediction model is constructed by using the ElasticNet method to estimate the parameters of different variables, fully considering the influencing factors and realizing the variable screening, and analyzing the influence of the number of hidden layers and nodes on the accuracy of the neural network. Simulation results show that the proposed method is superior to LSTM, Least Absolute Shrinkage and Selection Operator-LSTM, BP neural network, and RBF neural network, and has higher prediction accuracy.
Recommended Citation
Yu, Guangyu; Dong, Xueping; Wang, Xiangmin; and Min, Gan
(2021)
"Decomposition Furnace Outlet Temperature Prediction Based on ElasticNet and LSTM,"
Journal of System Simulation: Vol. 33:
Iss.
5, Article 9.
DOI: 10.16182/j.issn1004731x.joss.20-0012
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss5/9
First Page
1078
Revised Date
2020-02-01
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0012
Last Page
1085
CLC
TP391.9
Recommended Citation
Yu Guangyu, Dong Xueping, Wang Xiangmin, Gan Min. Decomposition Furnace Outlet Temperature Prediction Based on ElasticNet and LSTM[J]. Journal of System Simulation, 2021, 33(5): 1078-1085.
DOI
10.16182/j.issn1004731x.joss.20-0012
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