Thermogravimetric analysis and kinetic modeling of the pyrolysis of different biomass types by means of model-fitting, model-free and network modeling approaches

Abstract

Abstract This work aims at comparing the ability of 7 modeling approaches to simulate the pyrolysis kinetics of spruce wood, wheat straw, swine manure, miscanthus and switchgrass. Measurements were taken using a thermogravimetric analyzer (TGA) with 4 heating rates comprised between 5 and 30 K min −1 . The obtained results were processed using 3 isoconversional methods (Kissinger–Akahira–Sunose (KAS), Ozawa–Flynn–Wall (OFW) and Friedman), 1-step and 3-step Kissinger models, as well as an advanced fitting method recently proposed by Bondarchuk et al. [1] (Molecules 28:424, 2023, https://doi.org/10.3390/molecules28010424 ). Seventeen reaction models were considered to derive rate constant parameters, which were used to simulate the variation of the fuel conversion degree $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> as a function of the temperature $$T$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> . To complement this benchmarking analysis of the modeling approaches commonly used to simulate biomass pyrolysis, a network model, the bio-CPD (chemical percolation devolatilization), was additionally considered. The suitability of each model was assessed by computing the root-mean-square deviation between simulated and measured $$\alpha =f(T)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mo>=</mml:mo> <mml:mi>f</mml:mi> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> profiles. As highlights, the model-free methods were found to accurately reproduce experimental results. The agreement between simulated and measured data was found to be higher with the Friedman model, followed by the KAS, FWO, 3-step, and 1-step Kissinger models. As for the bio-CPD, it failed to predict measured data as well as the above-listed models. To conclude, although it was less efficient than the Friedman, KAS or OFW models, the fitting approach from Bondarchuk et al. [1] (Molecules 28:424, 2023, https://doi.org/10.3390/molecules28010424 ) still led to satisfactory results, while having the advantage of not requiring the selection of a reaction model a priori.

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