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Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

查看全文 作　　者：Xin [1]Shao;Qing [1,2]Liu;Zicheng [1]Xin;Jiangshan [1]Zhang;Tao [1]Zhou;Shaoshuai [3]Li 高影响力作者 机构地区：[1]State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,Beijing 100083,China;[2]Engineering Research Center of MES Technology for Iron&Steel Production,Ministry of Education,Beijing 100083,China;[3]Laiwu Iron and Steel Group Yinshan Section Steel Co.,Ltd.,Jinan 271104,China高影响力机构 出　　处：《International Journal of Minerals,Metallurgy and Materials》索引2024年第31卷第1期,共12页高影响力期刊 基　　金：financially supported by the National Natural Science Foundation of China (Nos.51974023 and52374321);the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,China (No.41620007)。 摘　　要：The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter. 关 键 词：basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model
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