基于时序模型的高炉煤气发生量多步预测对比

doi:10.13228/j.boyuan.issn0449-749x.20220166

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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.

Key words： blast furnace gas prediction time series prediction long short-term memory seasonal autoregressive integrated moving average multi-step forecast

Received: 10 March 2022

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	Articles by authors
	BAO Xiang-jun
	WENG Si-hao
	CHEN Guang
	WANG Jing
	CHEN Xu
	XIE Jing-cheng

Cite this article:

BAO Xiang-jun,WENG Si-hao,CHEN Guang等. 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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http://www.chinamet.cn/Jweb_gt/EN/10.13228/j.boyuan.issn0449-749x.20220166 OR http://www.chinamet.cn/Jweb_gt/EN/Y2022/V57/I9/166

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