Comparison on multi-step prediction of blast furnace gas generation based on LSTM/SARIMA time series model
BAO Xiang-jun1, WENG Si-hao1, CHEN Guang1, WANG Jing2, CHEN Xu2, XIE Jing-cheng1
1. School of Energy and Environment, Anhui University of Technology, Ma'anshan 243000, Anhui, China; 2. Shanghai Baosight Software Co., Ltd., Shanghai 201203, China
Abstract：In order to accurately predict the gas generation amount of blast furnace under normal and variable working conditions (such as wind off, production reduction, production shutdown, etc.) The amount of blast furnace gas generated. The prediction effects of the two models with different prediction steps under normal conditions are compared and that with the increase of the number of prediction steps, the prediction accuracy of the two models generally decreases, and the prediction accuracy of the LSTM model is generally higher than that of the SARIMA model are found. Model accuracy, the prediction effects of different input samples on the model under the condition of 30-step prediction are also compared. The results show that the optimal input sample size of the SARIMA model is about 200, the corresponding average relative error is 0.057 0, and the LSTM model is the best The input sample size is about 100, and the corresponding average relative error is 0.042 8. Therefore, the prediction effect of the LSTM model is better under normal working conditions; while the SARIMA model works better under variable working conditions, and the average relative error of the SARIMA model is 0.069 4, 0.094 0 for the LSTM model. Combining the advantages of the two models, a gradient-driven time series prediction composite model is established. The average relative error of the model's 30-step prediction under the composite working condition is 0.060 1, which is lower than the error when the two models are used alone. Therefore, it is recommended to run in the field. Using the gradient-driven time series prediction composite model for prediction provides better data support for blast furnace gas regulation, rationally distributes gas, improves gas utilization, and reduces gas emission.
包向军, 翁思浩, 陈光, 汪晶, 陈谞, 谢竟成. 基于时序模型的高炉煤气发生量多步预测对比[J]. 钢铁, 2022, 57(9): 166-172.
BAO Xiang-jun, WENG Si-hao, CHEN Guang, WANG Jing, CHEN Xu, XIE Jing-cheng. Comparison on multi-step prediction of blast furnace gas generation based on LSTM/SARIMA time series model[J]. Iron and Steel, 2022, 57(9): 166-172.
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2022, Vol. 57 
