Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

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 leastsquares
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&minus3,
and R2 = 0.999991). 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.