支持向量机在高炉铁水温度预测中的应用

铁水温度是高炉冶炼过程的关键参数，是影响高炉稳定顺行及节能降耗的重要指标。以高炉炉内热状态的重要指示剂-铁水温度为研究对象，在综合利用K-means 聚类和支持向量机方法的各自优势和互补情况下，提出一种基于K-means 聚类的支持向量机预测铁水温度的方法，该方法首先将训练样本数据分为m 类，建立m 个支持向量机回归预测模型，同时采用粒子群算法优化模型参数; 其次建立m 个判别函数，判别待预测样本数据属于哪一类;最后将待预测样本数据代入相应类的回归模型中进行预测。相比标准支持向量机预测，得到了较高的预测精度。

Application of Support Vector Machine(SVM) in Prediction of Molten Iron Temperature in Blast Furnace

As a key parameter in blast furnace smelting process，the temperature of molten iron is of importance for smooth operation of blast furnace and the energy consumption． This paper studies on the important indicator for heat state of the blast furnace，namely molten iron temperature． By taking advantages of both method of K-means clustering and support vector machine( SVM) ，a K-means clustering － based SVM model is proposed for predicting the temperature of molten iron． Firstly，the training sample data are divided into m classes and m SVM regression prediction models are established accordingly． At the same time，a particle swarm optimization algorithm is utilized to optimize the model parameters． Then，m discriminant functions are established to recognize which class the sample data belongs to． Finally，the sample data are put into the corresponding class of regression model to predict temperature． Compared to the standard SVM － based prediction method，the proposed method predict the molten iron temperature with a higher accuracy．