Makine Öğrenmesi ile Kısa ve Elemental Analiz Kullanarak Katı Yakıtların Üst Isı Değerinin Tahmin Edilmesi

Abstract

Prediction of higher heating value (HHV) using proximity and ultimate analysis is an important procedure for understanding the characteristic attribute of a fuel. Researches put effort to come up with equations to explain the relationship between the HHV value and those analyses. But conducted methods usually included only simple statistical analysis, thus they were partially effective to use in a practical manner. In this paper we approach this prediction problem from the machine learning perspective, we employ four machine learning methods, i.e. linear regression, polynomial regression, decision tree regression and support vector regression to predict HHV using proximity and ultimate analysis of different type of materials. Data set used is collected from literature and is categorized, where the resulting categories are used as features to be fed to the machine learning models to create prediction models as accurate as possible. Performances of the proposed methods are evaluated with k-fold cross-validation technique and each method’s pros and cons are discussed for both prediction accuracy and computational complexity.