Modeling and analysis of bench-scale pyrolysis of lignocellulosic biomass based on merge thickness
Introduction
Biomass is expected to be a major source of sustainable energy in the future (Park et al., 2010). Wood, as a representative of lignocellulosic biomass, whose mechanisms and pyrolysis rates are important for energy utilization (Chen et al., 2016, Chen et al., 2018) and designing wood stoves, furnaces, and boilers (Bryden et al., 2002). Especially, the size, namely, the thickness of wood plays a very important role in the pyrolysis and combustion behaviors (Ashori and Sheshmani, 2010, Dang and Nguyen, 2008). For example, furnaces and boilers are designed for sawdust, woodchips, chunkwood, or logs (Bryden et al., 2002). Kung (1972) focused on the wood pyrolysis model based on thickness from 0.02 cm to 1 cm. Wesson et al., 1971, Mikkola and Wichman, 1989 emphasized the piloted ignition time were closely related with thickness by the experimental data of various wood species. The combustible solid is classified by the thickness as ‘thermal thin’ and ‘thermal thick’ based on the significant temperature gradient under thermal wave condition. Babrauskas (2003) even summarized an empirical formula of wood thermal thickness based on density and external heat flux. Therefore, the effect of thickness cannot be ignored on the pyrolysis behaviors.
Based on the previous results of fire propagation apparatus or cone calorimeter, it is found that the number of peaks of mass loss rate is also related with the thickness (Ding et al., 2015). As the thickness decreases, the two peaks merge to one at a certain thickness (defined as “merge thickness” in this paper), which has not been paid more attention in the previous studies. Then the aim of our current study is to explore the exact value of merge thickness during the bench-scale pyrolysis of lignocellulosic biomass, especially by the numerical simulation using a modified version of FireFOAM (Ding et al., 2018, Wang et al., 2011) within the OpenFOAM toolbox. A new method based on the second derivative of simulated mass loss results is put forward to deal with the merge thickness by the Gpyro pyrolysis model (A Generalized Pyrolysis Model for Combustible Solids) (Lautenberger and Fernandez-Pello, 2009) coupled with the optimized chemical reaction parameters, moisture and changed volume.
Section snippets
Thermogravimetric measurements
Beech wood (Fagus sylvatica), was chosen as sample in our study (Ding et al., 2016a, Ding et al., 2017). A TA Instrument SDT Q600 thermal analyzer was applied in the pyrolysis process at various heating rates (5–80 K/min). A powdery sample with was evenly distributed in an Alumina cup without a lid with a purge flow of 100 mL/min pure nitrogen during the whole process. The detailed thermogravimetric measurements had been introduced by Ding et al. (2016b).
Fire propagation apparatus
A fire propagation apparatus (FPA) was
Results and discussion
Figure 1 shows the experimental results of mass loss rate at different thicknesses. When the sample surface is exposed to the original radiation heat flux, the thermal wave spreads downwards with the drying and pyrolysis process until the appearance of the first peak of mass loss rate. Due to the produced char layer, the spread of thermal wave slows down and the mass loss rate starts to decrease. However, when the thermal wave spreads to the bottom of sample, the heat gradually accumulates
Conclusions
To predict the bench-scale pyrolysis of lignocellulosic biomass at merge thickness, the Gpyro pyrolysis model is coupled with the optimized chemical reaction parameters, moisture and changed volume in FireFOAM. When the two peaks start to merge, the thermal wave spread is significantly accelerating the pyrolysis process. A new method based on the second derivative of simulated mass loss to compute the merge thickness is put forward. Eventually, the predicted equation of merge thickness at
Acknowledgements
The authors would like to acknowledge financial support sponsored by National Key Research and Development Program of China (No. 2016YFE0113400), National Natural Foundation of China (No. 51706212), Opening Fund of State Key Laboratory of Fire Science (SKLFS) (Nos. HZ2018-KF06, HZ2017-KF08), Natural Science Foundation of Hubei Province of China (No. 2018CFB352) and Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUG170672).
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