Application of a supervised learning machine for accurate prognostication of higher heating values of solid wastes

One of the efficient and reasonable choices for solid waste disposal is waste combustion leading to the generation of a renewable source of energy. In present work, a statistical machine learning technique, namely, least-square support vector machine was created to compute the higher heating value in relation to elemental compositions. The used data sets which include 100 data points, was divided into the two parts of training and testing, respectively, for creating a model and examining the model reliability. In conclusion, it is perceived that the proposed approach is the most accurate numerical scheme as compared with commonly used literature correlations.