Prediction of blast furnace gas generation based on data quality improvement strategy

The real-time energy flow data obtained in industrial production processes are usually of low quality. It is difficult to accurately predict the short-term energy flow profile by using these field data, which diminishes the effect of industrial big data and artificial intelligence in industrial energy system. The real-time data of blast furnace gas (BFG) generation collected in iron and steel sites are also of low quality. In order to tackle this problem, a three-stage data quality improvement strategy was proposed to predict the BFG generation. In the first stage, correlation principle was used to test the sample set. In the second stage, the original sample set was rectified and updated. In the third stage, Kalman filter was employed to eliminate the noise of the updated sample set. The method was verified by autoregressive integrated moving average model, back propagation neural network model and long short-term memory model. The results show that the prediction model based on the proposed three-stage data quality improvement method performs well. Long short-term memory model has the best prediction performance, with a mean absolute error of 17.85 m(3)/min, a mean absolute percentage error of 0.21%, and an R squared of 95.17%.