Decomposition Furnace Outlet Temperature Prediction Based on ElasticNet and LSTM

doi:10.16182/j.issn1004731x.joss.20-0012

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.

Key words: decomposition furnace outlet temperature, ElasticNet, long short-term memory neural network, feature selection

CLC Number:

TP391.9

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.

References

[1] 赵彦涛, 陈宇, 陈英豪, 等. 基于BFCM-iWM模糊规则自提取的水泥分解炉温度控制[J]. 控制与决策, 2019, 34(2): 383-389.
Zhao Yantao, Chen Yu, Chen Yinghao, et al.Temperature Control of Cement Decomposing Furnace Based on BFCM-iWM Fuzzy Rules Extraction[J]. Control and Decision, 2019, 34(2): 383-389.
[2] 何耀耀, 秦杨, 杨善林. 基于LASSO分位数回归的中期电力负荷概率密度预测方法[J]. 系统工程理论与实践, 2019, 39(7): 1845-1854.
He Yaoyao, Qin Yang, Yang Shanlin.Medium-Term Power Load Probability Density Forecasting Method based on LASSO Quantile Regression[J]. Systems Engineering- Theory & Practice, 2019, 39(7): 1845-1854.
[3] 王宏伟, 黄元生, 姜雨晴, 等. 基于套索算法和高斯过程回归的中长期居民用电量概率预测[J]. 华北电力大学学报, 2019, 46(6): 27-35.
Wang Hongwei, Huang Yuansheng, Jiang Yuqing, et al.Residential Probabilistic Load Forecasting based on LASSO-Gaussian Process Regression[J]. Journal of North China Electric Power University, 2019, 46(6): 27-35.
[4] 王沛立, 李恩平. 我国居民医疗负担及其影响因素分析-基于弹性网方法的实证研究[J]. 数学的实践与认识, 2019, 49(14): 97-107.
Wang Peili, Li Enping.Analysis of Medical Burden Influencing Factors-An of Chinese Residents and Its Empirical Study Based on Elastic Network Method[J]. Mathematics in Practice and Theory, 2019, 49(14): 97-107.
[5] Bartosz U, Grzegorz M.Understanding Intraday Electricity Markets: Variable Selection and Very Short-term Price Forecasting Using LASSO[J]. International Journal of Forecasting (S0169-2070), 2019, 35(4): 1533-1547.
[6] 宓登凯, 王彤, 相禹维, 等. 基于ElasticNet的暂态稳定裕度在线评估[J]. 电网技术, 2019, 44(1): 19-26.
Mi Dengkai, Wang Tong, Xiang Yuwei, et al.Elastic Net based Online Assessment of Power System Transient Stability Margin[J]. Power System Technology, 2019, 44(1): 19-26.
[7] 唐振浩, 赵赓楠, 曹生现, 等. 基于SWLSTM算法的超短期风向预测[J]. 中国电机工程学报, 2019, 39(15): 4459-4467.
Tang Zhenhao, Zhao Gengnan, Cao Shengxian, et al.Very Short-Term Wind Direction Prediction Via Self-tuning Wavelet Long-Short Term Memory Neural Network[J]. Proceedings of the CESS, 2019, 39(15): 4459-4467.
[8] 王伊宁, 解大, 王西田, 等. 基于PCA-LSTM模型的风电机网相互作用预测[J]. 中国电机工程学报, 2019, 39(14): 4070-4080.
Wang Yining, Xie Da, Wang Xitian, et al.Prediction of Interaction Between Grid and Wind Farms Based on PCA-LSTM Model[J]. Proceedings of the CESS, 2019, 39(14): 4070-4080.
[9] 宋刚, 张云峰, 包芳勋, 等. 基于粒子群优化LSTM的股票预测模型[J]. 北京航空航天大学学报, 2019, 45(12): 2533-2542.
Song Gang, Zhang Yunfeng, Bao Fangxun, et al.Stock Prediction Model based on Particle Swarm Optimization LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2533-2542.
[10] Tibshirani R.Regression Shrinkage and Selection via the Lasso[J]. Journal of the Royal Statistical Society Series B-Statistical Methodology (S1467-9868), 1996, 58(1): 267-288.
[11] Zou H, Hastie T.Regularization and Variable Selection Via the Elastic Net[J]. Journal of the Royal Statistical Society Series B-Statistical Methodology (S1467-9868), 2005, 67(2): 301-320.
[12] Song Q Y, Yuan Z G.Working Condition Recognition of Cement Decomposition Furnace Based on ART-2 Neural Network[C]// Sixth International Conference on Measuring Technology and Mechatronics Automation. Zhangjiajie, China: IEEE, 2014: 807-812.
[13] Li S W, Li B, Li C R.The Study of Temperature Control of Cement Decomposition Furnace on the Basis of Fuzzy Immune Self-Adaptive PID Control Method[C]// Third International Conference on Information Science and Technology. Yangzhou, China: IEEE, 2013: 86-90.
[14] Liu W, Dou Z H, Wang W G.Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization[J]. Applied Sciences (S2076-3417), 2018, 8(9): 1603.