Classification of solid fuels with machine learning

Abstract

In energy applications, fuels are processed in various ways according to their types. Experiments conducted with non-optimal processing procedures cause waste of the materials and may lead to inaccurate conclusions. Moreover, classifying the fuel material becomes harder when the material is already processed or collected from an environment that makes it difficult to discern. Thus, with the constantly developing and diversifying energy applications, the need for a framework that can classify solid fuels is increased. In this study, we used machine learning based approach to classify fuels with the use of proximate analysis results, i.e., fixed carbon, volatile matter and ash contents. We collected a data set from the literature and group them into four classes, i.e., coals, woods, agricultural residue and manufactured biomass. Then, K-nearest neighbor, support vector machine and random forest machine learning classifiers are employed to develop prediction models. Furthermore, hierarchical classification approach is taken to combine each classifier's advantages with integration of expert opinion to create a complete and highly accurate classifier framework which can classify an unknown fuel into one of the four categories. K-fold cross validation is used to evaluate classifiers' performances in unbiased manner. With hierarchical classifier, % 96 and %92 classification accuracy is achieved for training and testing phases, respectively. Source code of the proposed hierarchical classifier framework is also provided.