高炉冶炼过程中炉温是影响技术经济指标的关键参数，保持合理的炉温是高炉稳定顺行的关键因素。采用某炼铁厂在线采集的数据，通过核主元分析对建模数据进行预处理，根据相关系数选定模型参数，确定参数对炉温的滞后时间，基于支持向量机建立了高炉向凉、向热预测诊断模型。通过实例验证，该模型具有很高的精度。
Furnace temperature in iron making process was key parameter, which directly affected the primary technique and economics index. One of the key factors for smooth running of blast furnace was to maintain a reasonable temperature. Based on data collected from some ironmaking plant, the model parameters were adopted according to correlation coefficients. Considering the lag time of furnace temperature, status diagnosis system in blast furnaces was proposed based on support vector machines (SVM). In order to obtain precise finite element model, the modeling data were preprocessed by the kernel principal component analysis (KPCA).
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