Thermo-kinetics study of microalgal biomass in oxidative torrefaction followed by machine learning regression and classification approaches

Abstract

Solid biofuels derived from microalgae represent a low-cost, high-volume bioproduct opportunity with excellent CO2 biofixation capability, contributing significantly to the attainment of net zero. Oxidative torrefaction also offers a more economical pretreatment to upgrade its solid fuel properties. This study investigates the thermo-kinetics aspect of oxidative torrefaction of microalgae with varying O2 concentrations using thermogravimetric and differential thermal analysis (TGA-DTA). Isoconversional kinetic modeling is applied to mass-loss data and shows average activation energies of 172.57, 174.68, 199.42, and 209.03 kJ‧mol−1 at 0, 3, 12, and 21 % O2 concentrations, respectively. The effect of O2, especially at 12 vol%, significantly reduces the calculated activation energy at lower conversion. DTA also reveals lower heat flow values under oxidative than inert torrefaction. Thermo-kinetics data are then utilized to conduct machine learning approaches. Regression via artificial neural networks shows that the prediction of conversion and heat flow values are predominantly dictated by the temperature, followed by heating rate and O2 concentration. Finally, classification using the k-nearest neighbors algorithm highlights the effects of factors at specific ranges of conversion and heat flow responses. The O2 concentration is significant only at early conversion (X<0.1) and contributes to generally lower heat flow values.