Abstract：Basic oxygen furnace (BOF) steelmaking is a complex process with high-temperature physicochemical reactions. The composition of steel cannot be detected continuously during smelting, accurately predicting the carbon mass percent and temperature of the end-point is very meaningful to improve the end-point hit rate. The actual production samples of 80 t BOF were collected from one steel plant in Guangxi province. A twin support vector regression (TSVR) prediction model was established to realize the prediction of end-point temperature and carbon mass percent. The training process was carried out by using actual production samples of 100 heats, and the other 30 heats were adopted to verify the accuracy of the prediction model. The results show that the hit rate of the model with Δw([C])≤0.01% is 93.3％ and the hit rate of the model with Δt≤15 ℃ is 96.7％. In addition, the double hit rate of the model is 90%. By comparing with the BP neural network model, the end-point carbon content and the end-point temperature hit rate of this model are higher than those of the BP neural network model.
汪淼, 李胜利, 高闯, 范越. 80 t转炉终点预报TSVR模型精度[J]. 钢铁, 2020, 55(7): 53-57.
WANG Miao, LI Sheng-li, GAO Chuang, FAN Yue. End-point prediction TSVR model accuracy of 80 t BOF steelmaking[J]. Iron and Steel, 2020, 55(7): 53-57.
[1] 王玉昆,张勇.基于主元分析的转炉终点ESN静态预测模型[J].辽宁科技大学学报,2009,32(4):360.(WANG Yu-kun, ZHANG Yong. The ESN static prediction model of converter end point based on principal component analysis[J]. Journal of Liaoning University of Science and Technology,2009,32(4):360.) [2] 殷志辉,于春峰,郭宗华.转炉副枪炼钢过程计算模型开发[J].智慧工厂,2016(9):93.(YIN Zhi-hui, YU Chun-feng, GUO Zong-hua. Development model calculation of converter sub-gun steelmaking process[J].Smart Factory,2016(9):93.) [3] 李朋欢,包燕平,岳峰,等.超低碳钢的转炉终点控制[J].钢铁,2011,46(10):27.(LI Peng-huan, BAO Yan-ping, YUE Feng, et al. End-point control of ultra low carbon steel[J].Iron and Steel,2011,46(10):27.) [4] 王勇,胡建光,孙玉军,等.智能制造在梅钢炼钢厂的应用实践[J].中国冶金,2018,28(1):32.(WANG Yong, HU Jian-guang, SUN Yu-jun, et al. The application of intelligent manufacturing in Meigang Steelmaking Plant[J].China Metallurgy,2018,28(1):32.) [5] 王红君,蒋伟杰,赵辉,等.基于最优加权组合模型的转炉炼钢耗氧量预测[J].河南理工大学学报:自然学科版,2017,36(2):94.(WANG Hong-jun, JIANG Wei-jie, ZHAO Hui, et al. Predicting oxygen consumption of converter steelmaking based on optimal weighted combination model[J].Journal of Henan University of Technology:Natural Science,2017,36(2):94.) [6] LI W, WANG Q M, WANG X S, et al. Endpoint prediction of BOF steelmaking based on BP neural network combined with improved PSO[J]. Chemical Engineering Transactions, 2016, 51: 475. [7] HE F, ZHANG L. Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network[J]. Journal of Process Control, 2018, 66:51. [8] 李长荣,赵浩文,谢祥,等.基于L-M算法BP神经网络的转炉炼钢终点磷含量预报[J].钢铁,2011,46(4):23.(LI Chang-rong, ZHAO Hao-wen, XIE Xiang, et al. Prediction of end point phosphorus content in converter steelmaking based on L-M algorithm and BP neural network[J]. Iron and Steel, 2011, 46 (4): 23.) [9] 高闯,沈明钢.基于无约束小波权重TSVR的转炉炼钢终点静态预测模型[J].炼钢,2019,35(2):20.(GAO Chuang, SHEN Ming-gang. Static prediction model of BOF steelmaking end point based on unconstrained wavelet weight TSVR[J].Steelmaking, 2019, 35(2): 20.) [10] 高闯,沈明钢,王焕清.基于孪生支持向量回归机的转炉炼钢终点预测[J].中国冶金,2019,29(4):12.(GAO Chuang, SHEN Ming-gang, WANG Huan-qing. End-point prediction model of BOF steelmaking based on twin support vector regression[J].China Metallurgy,2019,29(4):12.) [11] PENG X. TSVR: an efficient twin support vector machine for regression[J]. Neural Networks, 2010, 23(3): 365. [12] 黄赫虹,周云,孟庆民,等.韶钢120 t转炉炼钢终点静态控制模型开发与应用[J].安徽工业大学学报:自然科学版,2014,31(3):242. (HUANG He-hong, ZHOU Yun, MENG Qing-min, et al. Development and application of static control model for steelmaking end point of 120 t converter in Handan Iron and Steel Co., Ltd.[J].Journal of Anhui University of Technology:Natural Science,2014,31(3):242.)
2020, Vol. 55 
