On-line classification of coal combustion quality using nonlinear SVM for improved neural network NOx emission rate prediction

Abstract

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.