Abstract:Byproduct gases account for 40% of total energy consumption in steel production process. Therefore, the accurate prediction of byproduct gas consumption can provide scientific guidance for the optimal scheduling of the gas system in steel enterprises. As one of the biggest byproduct gas consumers, the hot blast stoves suffer from huge and frequent fluctuations in gas consumption, and thus it is difficult to predict. Based on the shortage of previous prediction models whose prediction time is short, a BP neural network based time series prediction model was proposed and the prediction time was increased to 30 minutes with little influence on the prediction accuracy. The results of the case study indicated that the optimal volume of the training sample and the prediction sample were 2 000 and 30, respectively, and the absolute percentage error could reach 4.04%. In addition, different prediction models were compared and the results demonstrated that the proposed model was more suitable for the medium term prediction of byproduct gases.
郝聚显,赵贤聪,韩玉召,白皓. 热风炉煤气消耗量中期预测模型[J]. 中国冶金, 2018, 28(2): 17-22.
HAO Ju- xian,ZHAO Xian- cong,HAN Yu- zhao,BAI Hao. Medium- term prediction model for byproduct gas consumption in hot blast stove[J]. China Metallurgy, 2018, 28(2): 17-22.