Use of neural network to analyze the kinetics of CO2 absorption in Li4SiO4/MgO composites from TG experimental data

Abstract

In this work, it was studied the kinetics of CO2 absorption process in the composite Li4SiO4/MgO at 400, 500, 600 and 700 °C. The composite Li4SiO4/MgO, prepared from a natural available mineral serpentinite Mg3Si2O5(OH)4 showed absorption capacity of ca. 24 wt.%. Based on the isothermal TG data, the CO2 absorption kinetic was initially investigated using geometrical contraction models, diffusion models and nucleation models, which are commonly used in the literature. All of these models showed poor description of the experimental data. On the other hand, when the kinetic process is investigated by the neural network-based model, combining all these kinetic models, an excellent data fitting was observed with the identification of the most important mechanism combination for each temperature. For instance, at 400 °C, the nucleation process presented a more important contribution, at 500 °C, the nucleation and diffusion processes were similarly important whereas at temperatures higher than 600 °C, diffusion processes become more important.