LSTM-based modelling method for predicting pressure difference in blast furnace under oxygen-enriched blast conditions
In the process of blast furnace smelting,under the influence of dynamic changes of working conditions and complex factors at the production site,the fluctuation of differential pressure has a cer-tain time lag,and it is still difficult to realize the accurate forecast of differential pressure based on re-al-time online data.To address this problem,based on the actual smelting process of the blast fur-nace,which has the characteristics of multivariate and time-dependent time series data,the volatility analysis and decision tree feature importance analysis methods that can effectively reflect the degree of fluctuation of the production process parameters are adopted,and different subsets of the model in-put features are selected,so as to establish the temporal pressure difference prediction model based on the long short-term memory(LSTM).The comparison results of the two methods show that the LSTM prediction model based on volatility analysis to determine the input features has an improved hit rate of 0.761％within the prediction error range[-5,+5]kPa.The feature selection method based on the volatility analysis of production parameters can effectively improve the prediction accu-racy of the LSTM model,and verify the validity of the input feature selection method of the temporal differential pressure prediction model under the condition of oxygen-enriched blast furnace.
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