Robust intelligent topology for estimation of heat capacity of biochar pyrolysis residues

Abstract

Biochar is recognized as a promising spectrum for thermal science applications considering environmental protection. In this study, different artificial intelligence (AI) techniques are employed to determine the heat capacity of biochar derived from pyrolysis of different biomass sources as a function of pyrolysis temperature, post-treatments process, and temperature. Relatively substantial experimental measurements are employed for adjusting the hyper-parameters of the considered AI methodologies. Accurate measurement using seven statistical criteria confirmed that the least-squares support vector machine (LS-SVM) with the Gaussian-type kernel is the best paradigm for the considered task. Statistical analysis shows an excellent capacity of the developed LS-SVM approach for estimation of heat capacity of biochar, i.e., mean square error (MSE) of 6.82, absolute average relative deviation (AARD) of 0.15%, and regression coefficient (R2) of one. Furthermore, the LS-SVM method accuracy is more than 700 times better than the developed empirical correlation in literature.