A Logistic Approach for Kinetics of Isothermal Pyrolysis of Cellulose

Abstract

A kinetic model is proposed to fit isothermal thermogravimetric data obtained from cellulose in an inert atmosphere at different temperatures. The method used here to evaluate the model involves two steps: (1) fitting of single time-derivative thermogravimetric curves (DTG) obtained at different temperatures versus time, and (2) fitting of the rate parameter values obtained at different temperatures versus temperature. The first step makes use of derivative of logistic functions. For the second step, the dependence of the rate factor on temperature is evaluated. That separation of the curve fitting from the analysis of the rate factor resulted to be very flexible since it proved to work for previous crystallization studies and now for thermal degradation of cellulose.

Removal of Inorganic Impurities in the Fast Pyrolysis Bio-oil Using Sorbents at Ambient Temperature

“…The boiling points and degree of volatility both suggest that residual water, acids, ketones, or furans are converted/ evaporated early on at around 150 °C, while larger and more complex structures are converted at around 550 °C. TGA under inert N 2 conditions (Figure 2b) confirms the evaporation of volatile compounds at 150 °C, but no peak at 550 °C was observed: this could be due to that anhydrosugars (such as levoglucosan) form char 39 or the secondary reactions of lignin-derived components (reported at 425−570 °C) 40 that were combusted in the presence of oxygen. 39 FPBO is composed of hundreds of compounds with varying volatility, polarity, and molecular weights: typical compositions vary, depending on the pyrolysis method and feedstock used.…”

The Complexity of Lignin Thermal Degradation in the Isothermal Context

“…In general, these isothermal TG curves seem to follow asymptotic trends, which can be reproduced by generalized logistic functions. In line with previous works, fitting on DTG curves is preferred to that on TG curves in order to better reproduce the mass loss rate [36][37][38]. Thus, time-derivative generalized logistic functions were used to fit the segments of DTG curves obtained under isothermal conditions.…”

“…Three types of experiments were done: The experimentally obtained isothermal time derivative TG (DTG) curves were fitted by a time derivative logistic function. This function is suitable for accurately representing degradation curves of cellulose and other polymers accurately [36][37][38].…”

Determination of Kinetic and Thermodynamic Parameters of Biomass Gasification with TG-FTIR and Regression Model Fitting

“…Identifying the kinetic parameters for overlapping TGA steps remains complex and typically requires non-linear Energies 2024, 17, 1875 2 of 15 optimization techniques. Consequently, alternative approaches such as machine learning techniques and fitting non-linear process models have been suggested to describe thermal degradation [5]. Additionally, the identification of the kinetic parameters for additively overlapping TGA steps is a complex problem and, to the authors' current knowledge, is possible only through non-linear optimization techniques [6].…”

“…On the other hand, to model the temporal evolution of the thermal degradation process, the use of logistic regression-based methods has become commonplace as they can circumvent the use of numerical derivatives for kinetic parameter estimation and generally fit the TGA data to a great extent due to their asymptoticity. Varying approaches have been applied to the use of logistic regression models in TGA, with some being used to estimate the kinetic parameters of Arrhenius kinetic models describing the degradation, while others are used to directly estimate mass changes in the investigated material as a function of time or temperature [5]. In the latter case, logistic mixture models have been proposed to estimate thermal degradation as a time series process when multiple overlapping degradation steps may be present [6,9].…”