Modeling blast furnace productivity using support vector machines
Modeling blast furnace productivity using support vector machines
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DOI：
10.1007/s00170-010-2786-0
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发表时间：
2011-02
期刊：
影响因子：
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通讯作者：
Abhijit Ghosh;Sujit K. Majumdar
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文献类型：
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作者：
Abhijit Ghosh;Sujit K. Majumdar
利用支持向量机（SVM）对现代高炉生产率进行建模，该模型采用最小误差、最大边际的分类函数，以生产率等级（低/高）的二进制设置为基础;采用基于最小风险和最小调整风险的生产率真实的值的等级回归函数。支持向量机的训练与大量的数据点，每个数据点包括一组21个关键输入参数的高炉，相应的生产率值观察，和生产率类（低/高）归因。在支持向量机的训练过程中，要求将关键输入参数向量通过径向基核函数映射到高维特征空间，并利用二次优化方法找到了具有良好泛化性能的最优SVM-RBF分类函数及其超参数设置。SVM-RBF分类函数可用于预测临界输入参数的任何给定设置下的生产率类别（低/高）。还针对低生产率和高生产率类别开发了特定类别的SVM-RBF回归模型，这些模型可用于预测任何给定关键输入参数设置的生产率的真实的值。对适合高生产率的SVM-RBF回归模型进行约束非线性优化处理，以找到最大生产率的关键输入参数的最佳设置。关键参数的优化设置可作为高炉获得高利用率的目标设置。
Productivity of a modern generation blast furnace was modeled with the help of a leading supervised learning tool viz. Support Vector Machines in the form of (1) minimum error, maximum margin classification function in binary setting of productivity classes (low/high) and (2) the class-specific regression functions for real values of productivity based on epsilon sensitive loss function and minimum regulated risk. The SVMs were trained with large number data-points each of which consisted of a setting of 21 critical input parameters of blast furnace, corresponding productivity value observed, and the productivity class (low/high) attributed. During the training session of the SVMs, the vectors of critical input parameters were required to be mapped into high-dimensional feature space via Radial basis kernel as function and the optimum SVM-RBF classifying function with chosen setting of its hyperparameters that had good generalization property was found using quadratic optimization. The SVM-RBF classifying function could be used to predict the class of productivity (low/high) for any given setting of the critical input parameters. Class-specific SVM-RBF regression models were also developed for both low as well as high-productivity classes and these models could be used to predict real value of productivity for any given setting of the critical input parameters. The SVM-RBF regression model fitted to the high-productivity class was subjected to constrained nonlinear optimization treatment to find the optimum setting of the critical input parameters that gave maximum productivity. The optimum setting of the critical parameters could be used as the target setting obtaining high productivity in the blast furnace.