基于贝叶斯优化GBDT的转炉炼钢终点预测  

Prediction of converter steelmaking end point based on Bayesian optimization GBDT

作　　者：周翼男 崔桂梅[1] 皮理想 刘伟[1] 王东旭 ZHOU Yi'nan;CUI Guimei;PI Lixiang;LIU Wei;WANG Dongxu(Key Laboratory of Comprehensive Automation of Process Industry,School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China)

机构地区：[1]内蒙古科技大学信息工程学院流程工业综合自动化重点实验室,内蒙古包头014010

出　　处：《中国测试》2024年第7期33-39,共7页China Measurement & Test

基　　金：国家自然科学基金项目(61763039)。

摘　　要：为提高转炉炼钢终点碳含量和温度预报精度,提出基于贝叶斯优化梯度提升决策树(BOA_GBDT)的转炉炼钢终点碳含量和温度预测模型,将其与基础模型径向基函数(RBF)、支持向量机(SVM)、梯度提升决策树(GBDT)以及贝叶斯优化的径向基函数(BOA_RBF)、支持向量机(BOA_SVM)终点碳温预测模型对比分析。实验结果表明:BOA_GBDT各项误差指标最小,命中率最高,终点时刻碳含量在±0.01%误差区间内命中率为96.2%;终点温度在±10℃误差区间内命中率为92.1%。贝叶斯优化算法能够显著提升模型性能,更准确地判断转炉炼钢终点碳含量和温度,为吹炼出符合要求的钢水提供较为可靠的依据。In order to improve the prediction accuracy of end point carbon content and temperature in converter steelmaking,a prediction model of end point carbon content and temperature in converter steelmaking based on Bayesian optimized gradient lifting decision tree(BOA_GBDT)was proposed.It is compared and analyzed with the end-point carbon temperature prediction models of the base model radial basis function(RBF),support vector machine(SVM),gradient boosting decision tree(GBDT)and Bayesian algorithm optimized radial basis function(BOA_RBF),support vector machine(BOA_SVM)end point carbon temperature prediction model.The experimental results show that BOA_GBDT has the smallest error index and the highest hit rate.The hit rate of carbon content at the end point is 96.2%within the error interval of±0.01%;the hit rate of the end temperature is 92.1%within the error interval of±10℃.Bayesian optimization algorithm can significantly improve the performance of the model,more accurately judge the end-point carbon content and temperature of converter steelmaking,and provide a more reliable basis for converting qualified molten steel.

关 键 词：转炉炼钢 贝叶斯优化算法 梯度提升决策树 终点预测 

分 类 号：TB9[一般工业技术—计量学] TF345.3[机械工程—测试计量技术及仪器]