The pyrolysis reactions of hardwood and softwood were investigated using evolved gas analysis and mass spectrometry (EGA-MS) and by treating the experimental data with isoconversional methods to obtain kinetic information. Mass spectrometric detection allowed the identification of the pyrolysis products to be performed and component-specific thermograms were obtained by the extraction of appropriate m/z values without the need of peak-fitting. Finally, isoconversional methods, both an integral and a differential method, were used on compound-specific thermograms to calculate apparent activation energies of the carbohydrate and lignin fractions separately. The results showed that the two isoconversional methods provide comparable results, and that there are significant differences between the activation energies of the holocellulose and lignin fractions. This work shows that EGA-MS can provide reliable kinetic data for multi-component samples without the need of chemical pre-treatments or signal deconvolution.

Evolved gas analysis-mass spectrometry and isoconversional methods for the estimation of component-specific kinetic data in wood pyrolysis

Nardella F.;Mattonai M.;Ribechini E.
2020-01-01

Abstract

The pyrolysis reactions of hardwood and softwood were investigated using evolved gas analysis and mass spectrometry (EGA-MS) and by treating the experimental data with isoconversional methods to obtain kinetic information. Mass spectrometric detection allowed the identification of the pyrolysis products to be performed and component-specific thermograms were obtained by the extraction of appropriate m/z values without the need of peak-fitting. Finally, isoconversional methods, both an integral and a differential method, were used on compound-specific thermograms to calculate apparent activation energies of the carbohydrate and lignin fractions separately. The results showed that the two isoconversional methods provide comparable results, and that there are significant differences between the activation energies of the holocellulose and lignin fractions. This work shows that EGA-MS can provide reliable kinetic data for multi-component samples without the need of chemical pre-treatments or signal deconvolution.
2020
Nardella, F.; Mattonai, M.; Ribechini, E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1028754
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