Prediction of thermal stability of materials by modified kinetic and model selection approaches based on limited amount of experimental points

•Method for the prediction of the thermal stability using discontinuously collected sparse data was proposed.•Modified kinetic and model selection approaches were used in kinetic computations.•Data were simulated by the combination of two kinetic models without limitation of the number of the reaction stages.•Model selection procedure was based on Akaike and Bayesian information criteria.•Proposed methods of kinetic and model selection were verified by simulation of generated data with known, arbitrarily chosen kinetic parameters. The experimental data collected in the discontinuous mode are often used for the computation of reaction kinetics and, further, for the simulation of the thermal stability of materials. However, the kinetic calculations based on limited amount of sparse points require specific criteria allowing correct choice of the best kinetic model. We present the modified kinetic computations allowing considering one, two or even more reaction stages by applying unlimited amount of combinations of different kinetic models for the best description of the reaction course. The kinetic parameters are calculated using the truncated Šesták-Berggren (SB) approach and further verified by using the Akaike and Bayesian information criteria (AIC and BIC, respectively). The proposed method of kinetic and model selection for elaboration of sparse points were checked by the simulation of generated points with known, arbitrarily chosen kinetic parameters containing some scatter. The verified procedure was applied for the prediction of the thermal stability of energetic (propellant) and biological (vaccine) materials characterized by approximately 30 experimental data points.