Kinetic modelling of biomass fast devolatilization using Py-MS: Model-free and model-based approaches

Abstract

Feasibility using quantitative data obtained from a micropyrolyzer coupled to an evolved gas analysis technique, mass spectrometry, for kinetics determination has been long demonstrated. This paper describes for the first time how to obtain intrinsic kinetics for fast devolatilization of biomass and its lignocellulosic fractions (e.i., holocellulose and lignins). The main challenge with online detection is assessing the time lag related to transport phenomena between the reactor and the detector. To do this, an experimental method was developed to derive the real-time biomass devolatilization profile; this provided 'corrected' datasets. Preliminary kinetic parameters were obtained from the differential isoconversional Friedman method combined with real-time sample temperature history. Not considering the effect of thermal lag and the delay in detecting pyrolysis products by the MS leads to a certain level of inaccuracies. Isoconversional activation energy (Eα) dependencies obtained in the absence of heat and mass transfer limitations for biomass and its components highly varied with conversion, confirming the multi-step nature of the fast pyrolysis process. After demonstrating the modeling limitations of a constant activation energy model (CAEM), both isoconversional functions were parametrized to propose a variable activation energy model (VAEM) and used as initial inputs for the distributed activation energy model.