Prediction model of converter gas production based on deep-learning
BAO Xiangjun1, CHEN Kai1, LI Xiuping2, YANG Xiaojing1, LIU Xiao3, CHEN Guang1
1. School of Energy and Environment, Anhui University of Technology, Maanshan 243000, Anhui, China; 2. Steel Industry Green and Intelligent Manufacturing Technology Center, China Iron and Steel Research Institute Group Co., Ltd., Beijing 100081, China; 3. Iron and Steel Research Institute Co., Ltd., Beijing 100081, China
Abstract：The prediction of the occurrence of converter gas provides important support for the micro differential pressure control at the converter inlet, the improvement of converter gas recovery quality, and the overall gas balance scheduling of the plant. Based on the actual occurrence data of converter gas during the blowing process of a certain steel plant, a deep learning method was used to establish three prediction models for converter gas occurrence: BP neural network, LSTM long short memory neural network, and RBFNN radial basis function neural network. The effects of three parameters, namely prediction steps, input sample size, and hidden unit number, on the accuracy and computational efficiency of the prediction model were compared and analyzed. The research results indicate that the prediction accuracy of the model decreases with the increase of prediction steps, and choosing 30 step prediction is more in line with the actual needs of steel mills. As the sample input increases, there is no significant change in the accuracy of LSTM, while the accuracy of BP shows a decreasing trend. The accuracy of RBF first increases significantly and then slowly decreases. The prediction efficiency of LSTM showed no significant change, BP significantly decreased, and RBF remained unchanged. When the three models are under the optimal sample input and 30 step prediction conditions, the accuracy of LSTM remains basically unchanged as the number of hidden units increases. BP first slightly increases and then slowly decreases, while RBF first increases significantly and then remains stable, and then decreases significantly. The prediction efficiency of LSTM has slightly decreased, BP has significantly decreased, and RBF remains unchanged. Finally, under the condition of 30 step prediction, the optimal parameter conditions for LSTM, BP, and RBF models are as follows: LSTM sample input quantity is 125, hidden unit number is 135, ERMS minimum is 13.38, and training duration is 4.7 min; The input amount of BP samples is 50, the number of hidden units is 60, and the minimum ERMS is 31.46, with a training duration of 16.8 min; The input amount of RBF samples is 210, and the number of hidden units is 210. At this time, the minimum ERMS is 2.07, and the training duration is 1.2 min. Compared with actual data, RBF has the best prediction effect. By using the prediction results of converter gas generation to regulate the speed of the fan, the micro differential pressure at the furnace mouth can be maintained in a more stable state, reducing the suction air volume, and improving the heat value of the recovered gas.
包向军, 陈凯, 郦秀萍, 杨筱静, 刘骁, 陈光. 基于深度学习的转炉煤气发生量预测模型[J]. 钢铁, 2024, 59(1): 67-74.
BAO Xiangjun, CHEN Kai, LI Xiuping, YANG Xiaojing, LIU Xiao, CHEN Guang. Prediction model of converter gas production based on deep-learning[J]. Iron and Steel, 2024, 59(1): 67-74.
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2024, Vol. 59 
