Abstract
The so-called “model-free” or isoconversional way of kinetic modeling was examined. In this field, the available evaluation methods do not aim at an optimal fit for the experimental data. In the present work, the functions of the corresponding kinetic equation were approximated by simple versatile formulas, the number of the unknown parameters was kept on reasonably low levels, and the evaluation aimed at the best fit for the experiments by the least-squares method. Considerations and methods were tested on 85 thermogravimetric (TGA) experiments, which had been published earlier with different types of kinetic modeling. The experiments belonged to 16 biomass samples including woody biomass, agricultural residues, and industrial wastes. The temperature programs comprised constant heating rates, stepwise heating, constant reaction rate heating, isothermal temperature programs, and a modulated temperature program. The evaluations were based on four to nine experiments for each sample. The best fit was search...
Highlights
The kinetics of biomass pyrolysis is usually based on models that are built of equations dα/dt = A exp(−E/RT)f (α) where α is a reacted fraction, E is the apparent activation energy, A is the pre-exponential factor, and f(α) is an appropriate function
More than one such equation is needed when the model reflects the complexity of biomass pyrolysis reactions
Experiments belonging to the 16 biomass samples have been evaluated in different ways
Summary
During my work in various teams for 30 years, we realized that the simultaneous evaluation of linear and nonlinear temperature programs increases the information of the series of TGA experiments,[11−17] and kinetic models with 12−13 adjustable parameters were sufficient for fitting such series of experiments by the least-squares method for pyrolysis of a wide variation of biomass samples.[13,14,16,17] (Models assuming pseudo-components and parallel reactions were employed in these works.) the number of parameters could be decreased further by assuming partly common kinetic parameters for different biomass samples.[14−17] Here, questions arise: If 13 parameters are enough for a good fit, why should anyone determine 19 × 2 or 39 × 2 parameters? In other areas of sciences, the usual way of the evaluation is to find the best fit between the predicted and observed data by the least-squares method The use of this approach for isoconversional evaluations is the main goal in the present work
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