Pyrolysis kinetics of wildland vegetation using model-fitting methods☆
Introduction
Prescribed burning in the southern United States is an important tool used by land managers to accomplish several objectives such as wildlife habitat management, hazardous fuel reduction, maintenance of critical military training areas, control of insects and disease, improved grazing, maintenance of fire-dependent species and plant communities and management of competing vegetation [1]. In order to refine fire application to achieve desired fire characteristics and reduce undesirable fire characteristics, an improved understanding of the main processes related to pyrolysis and ignition is needed for the heterogenous fuel beds of live and dead vegetation [2].
The pyrolysis of major biomass components such as cellulose, hemicellulose and lignin has been studied in detail [[3], [4], [5], [6], [7], [8]]. However, the study of these components is not sufficient to understand the pyrolysis behavior of plant species during wildland fires [9]. Plant species are a combination of these biopolymers, and the presence of each component affects the pyrolysis of others. While there are general descriptions of the compositional makeup of plants for combustion and smoke emissions purposes, it has long been recognized that plants differ chemically and physically in many ways that contribute to differing combustion behavior [[10], [11], [12], [13], [14]]. Kinetic models are needed to describe the pyrolysis behavior of a plant which contains different components.
As biomass is heated, moisture is first released at temperatures below 150 °C. At low heating rates, two distinct peaks in the pyrolysis rate are often observed at 250–350 °C and 300–450 °C, which are attributed to the degradation of cellulose and hemicellulose. A broad peak is also often modeled ranging from 200 to 600 °C that is thought to represent a combination of pyrolysis of cellulose, hemicellulose, and lignin (see for example [4,7,15]).
Thermogravimetric Analyzer (TGA) is the most common tool used by many researchers to study the pyrolysis kinetics of woody biomass. Thermogravimetric/differential thermogravimetry (TG/DTG) experiments can be either isothermal or non-isothermal. Non-isothermal methods are most commonly used for pyrolysis, since pyrolysis always starts during the heating period [[16], [17], [18], [19]]. At a constant heating rate, time and temperature are related through:T = β t+ T0where t is time, β is the heating rate, T is the temperature, and T0 is the initial temperature.
Global devolatilization models are generally used to describe biomass pyrolysis due to the complexity of the solid-phase reactions. There are three types of global devolatilization kinetic models: one-step models, two-competing-step models, and distributed activation energy (DAE) models. In each model, the number of kinetic pathways that reaction can take determines the number of steps. These models have been used to study pyrolysis kinetics for a variety of biomass fuels [4,20,21].
The slow and fast pyrolysis characteristics of live and dead vegetation native to the southern United States and the analysis of pyrolysis products and the effect of temperature and heating rates on the distribution of pyrolysis products have been measured in detail [[22], [23], [24], [25], [26], [27]]. In addition, the kinetics of pyrolysis using iso-conversional methods and the dependence of activation energy on conversion were explored, and kinetic parameters as a function of conversion were reported [28]. However, since several current physical models of wildland fire use a single kinetic parameter obtained from global kinetic models, a study was performed to find single kinetic parameters for selected wildland plants based on these global kinetic models.
In this work, the kinetics of pyrolysis of 14 plant species native to the southern United States using global kinetic models were studied using simple model forms, including a simple one-step model as well as single- and multiple-reaction DAE models.
Section snippets
Material
Amini et al. [22,28] studied the slow-heating pyrolysis of 14 different plant species native to the southeastern United States forests. These plant species were selected to represent the range of live wildland fuels that commonly occur in wildland fires in this region which accounts for approximately 2/3 of the area (4.7 million ha) treated with prescribed fire in the U.S. annually [29]. The plants were grown in nurseries in the region and shipped overnight to the fire laboratory at Brigham
Kinetic modeling
Biomass pyrolysis depends on the conversion, residual mass, heating rate, and temperature. The mass loss and derivative mass loss data were fitted simultaneously at heating rates of 10, 20, and 30 °C min−1 to find kinetic parameters.
Model evaluating methods
To compare the predicted values from model fitting methods and experimental data obtained from slow pyrolysis of all live and dead plant species in TGA, the root mean square error (RMSE) and mean absolute error (MAE) values were calculated as follows:where Pi is the predicted value from the model, Ei is the experimental data point, and n is the number of employed experimental data points [43].
Statistical analysis
For statistical analysis, the ANOVA (Analysis of Variance) data analysis tool in Microsoft Excel 2017 was used [44]. All the results of this study are the average of three replications, and the error bars represent the ±90 % confidence intervals for three experiments. The 90 % confidence intervals (α = 0.1) using the t-value table are calculated as follows:where x̄ is the average value of the replications, s is standard deviation, n is the number of replications, t(α,n-1) is the
Completion temperature
The completion temperature has been extensively used by many researchers to characterize pyrolysis and combustion properties of solid fuels [5,45,46]. The temperature where the normalized rate of decomposition decreases consistently to less than 1% min−1 is defined as the completion temperature [5]. The pyrolysis completion temperature was determined for both the live and dead plant species at all three heating rates of 10, 20, and 30 °C min−1; the results for the live samples are presented in
Discussion
The three models used above to find the kinetic parameters of slow pyrolysis reaction of all plant species were the simple one-step model, the single-reaction DAE model, and the multiple-reaction DAE model. The goal was to fit the TGA and DTG data at multiple heating rates in order to provide a model that possibly may be extrapolated to the heating conditions in fires (approximately 100 °C s−1). Fig. 10 shows the comparison of these three models vs. the DTG data for live little bluestem grass
Conclusion
In this study, previously-reported slow-heating pyrolysis data of 14 plant species were analyzed. Pyrolysis completion temperatures, which may be related to combustion rate, were found for all plant species. Each plant species had a different completion temperature, differing by as much as 100 °C. Increasing heating rate increased the completion temperature.
Three different models (the single-reaction first-order model and the single and multiple reaction DAE models) were used to fit the TGA
Author statement
The roles of each author are stated below:
Elham Amini: Conceptualization, Methodology, Investigation, Software, Data Curation, Writing - Original Draft; Mohammad-Saeed Safdari: Writing - Review & Editing; Nathan Johnson: Investigation, Data Curation; David R. Weise: Writing - Review & Editing, Funding acquisition; Thomas H. Fletcher: Conceptualization, Methodology, Software, Data Curation, Writing - Review & Editing, Project administration
Declaration of Competing Interest
The authors report no declarations of interest.
Acknowledgements
This research was funded by DOD/EPA/DOE Strategic Environmental Research and Development Program ProjectRC-2640, funded through contract 16-JV-11272167-024, administered by the USDA Forest Service PSW Research Station.
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This manuscript was prepared, in part, by a U.S. Government employee on official time, is not subject to copyright and is in the public domain.