Biomass Hydrothermal Carbonization: Markov-Chain Monte Carlo Data Analysis and Modeling - Gallifuoco, Alberto; Papa, Alessandro Antonio; Taglieri, Luca (Frontiers in Chemical Engineering, 2021)
This paper introduces Bayesian statistical methods for studying the kinetics of biomass hydrothermal carbonization. Two simple, specially developed computer programs implement Markov-chain Monte Carlo methods to illustrate the potential of these techniques, long since established in other areas of chemical engineering. A range of experimental data, both from this study and the literature, confirm the success of modeling. The first program carries out Bayesian parameter estimation and performs better than the traditional deterministic methods (R^2 as high as 0.9998) also detecting global and local minima of the parameter space. The second program uses the Gillespie’s algorithm for the statistical simulation of the reactions occurring in the hydrothermal carbonization. Basic kinetic models, abiding by the acquired experimental evidence of the literature, test the program. Previsions compare properly with experiments. The proposed approach could support well the other techniques for studying hydrothermal conversions.