A nonlinear support vector machine (SVM) uses engineered features to classify the quality of currently combusting coal as it is fired in an operating electric utility generator. The SVM classification result selects a unique neural network regression model to predict NOx emission rate. A two-part exhaustive grid-search and 5-fold cross-validation routine identifies the radial basis kernel as optimal for the SVM, achieving a classification accuracy of greater than 66%. The accuracy of the modified neural network structure improves on the original structure by 40%. This work contributes 1) evidence of feature engineering to enhance raw features in a complex industrial process and to provide otherwise unavailable data, 2) the formulation of a novel hybrid machine learning approach combining SVMs and neural networks with differing objectives harmoniously, and 3) a demonstrated improvement in neural network NOx emission rate prediction accuracy at a live operating electric utility generator due to SVM classification.

非线性支持向量机（SVM）使用工程特征对当前正在燃烧的煤在可运行的电力发电机中燃烧的质量进行分类。SVM分类结果选择一个独特的神经网络回归模型来预测NOx排放率。由两部分组成的详尽的网格搜索和5倍交叉验证例程将径向基核确定为SVM的最佳选择，从而实现了大于66％的分类精度。改进的神经网络结构的精度比原始结构提高了40％。这项工作有助于1）功能工程的证据，以增强复杂工业流程中的原始功能并提供其他方式无法获得的数据，