Open AccessDetermination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches
Author(s) -
Karimi Mohsen,
Khosravi Marzieh,
Fathollahi Reza,
Khandakar Amith,
Vaferi Behzad
Publication year - 2022
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.1155
Subject(s) - cellulosic ethanol , support vector machine , heat capacity , ranking (information retrieval) , mathematics , machine learning , estimator , statistics , artificial intelligence , econometrics , computer science , engineering , thermodynamics , cellulose , physics , chemical engineering
Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and ranking analyses over 819 artificial intelligence models from seven different categories confirmed that the least‐squares support vector regression (LSSVR) with the Gaussian kernel function is the best estimator. This model is validated using 700 laboratory heat capacities of four cellulosic biomass samples in wide temperature ranges (absolute average relative deviation = 0.32%, mean square errors = 1.88 × 10 −3 , and R 2 = 0.1). The data validity investigation approved that only one out of 700 experimental data is an outlier. The LSSVR model considers the effect of the cellulosic samples' crystallinity, temperature, and sulfur and ash content on their heat capacity. The overall prediction accuracy of the LSSVR is more than 62% better than the achieved accuracy using the empirical correlation.