Artificial neural network model for the evaluation of chemical kinetics in thermally induced solid-state reaction

Abstract

The artificial intelligence technique is utilized to improve evaluation of thermally induced solid-state reaction kinetics. A general regression neural network (GRNN) model was applied to directly determine the kinetic triplets, i.e., activation energy, pre-exponential factor, and mechanism model. The effect of number of heating rate on prediction performance of the GRNN model was assessed based on the estimation indictors. The obtained kinetic triplets based on the triple heating rates were considered to be accepted. The prediction ability of the GRNN model was very robust at more than three heating rates. The relative errors for kinetic parameters derived from five heating rates were within ± 4%, and the cognition rates for mechanism models were up to 99.6%. The developed GRNN model was successfully applied in the high-temperature synthesis of Li4Ti5O12/C composites. It is expected that the model also could be extended to estimate the kinetics of other solid-state reactions.