基于特征构建的钢包精炼炉元素收得率预报

元素收得率的预报是钢包精炼炉(LF)合金化的难点与关键点.为提高其预报精度,通过机理分析确定了元素收得率的主要影响因素,由于这些影响因素在生产中无法即时获得,因此使用基于文法进化(GE)的特征构建方法间接获得所需影响因素,最后将构建后的数据作为模型输入建立收得率预报模型.依据问题背景对文法进化算法进行了改进,改进后的方法可以充分利用可测数据,解决了冶炼过程中重要数据无法获得的问题.将该方法应用于Q345B钢种Mn,Si元素收得率的预报,实验结果表明使用本方法可以有效提高模型的预报精度.

Element Yield Prediction in Ladle Furnace Based on Feature Construction

Element yield prediction is a key and difficult problem of ladle furnace(LF) alloying.For improving its precision,main factors of element yield were determined by the mechanism analysis method.Because these factors cannot be readily available in production,factors were obtained indirectly by the feature construction method based on grammatical evolution(GE).Finally,the element yield prediction model was established using constructed features as inputs.Original GE algorithm was improved on the basis of the background issue.The improved method made full use of the existing features,and solved the problem that important data cannot be detected in the smelting process.The proposed method had been applied to element yield prediction of Mn and Si element of Q345B.Experimental results showed that the prediction accuracy could be improved effectively by utilizing the proposed method.