(446a) Mechanistic Modeling of Fast Pyrolysis of Hemicellulose | AIChE

(446a) Mechanistic Modeling of Fast Pyrolysis of Hemicellulose

Authors 

Zhou, X. - Presenter, Northwestern University
Li, W., ExxonMobil Research and Engineering Company
Mabon, R., ExxonMobil Research and Engineering Company
Broadbelt, L. J., Northwestern University
Hemicellulose is one of the major components of lignocellulosic biomass and has potential for the production of renewable drop-in transportation fuels and multiple commodity chemicals.[1] Previously, however, the successful modeling of fast pyrolysis of hemicellulose was hampered by lumped kinetic models, which failed to describe the heterogeneous structure of hemicellulose and cannot predict the bio-oil composition. In this work, a structural model for hemicellulose extracted from corn stover that captured the experimentally measured properties was proposed, and a mechanistic model for hemicellulose pyrolysis was constructed based on the reaction family approach that we used for cellulose pyrolysis[2]. The model described the decomposition of hemicellulose chains, reactions of intermediates, and formation of a range of low molecular weight compounds at the mechanistic level. Rate constants were specified for each elementary reaction with Arrhenius format. Overall, more than 500 reactions of 144 species were included in the mechanistic model for fast pyrolysis of hemicellulose. The mechanistic model was able to closely match experimental yields of all the major products (with yield ≥ 1 wt%) from hemicellulose pyrolysis at 500 °C reported by Shanks and coworkers[3]at Iowa State University. The model predicted about 4 s residence time for complete thermo-conversion of hemicellulose. Model results showed that both degree of polymerization (DP) and polydispersity index (PDI) of hemicellulose had an insignificant effect on the pyrolysis products distribution. The mechanistic model of extracted hemicellulose is extendable to fast pyrolysis of native hemicellulose, which will be also discussed in this work.

[1] X. Zhou, W. Li, R. Mabon, L. J. Broadbelt, Energy Technol. 2016, 5, 52 –79.

[2] a) R. Vinu, L. J. Broadbelt, Energy Environ. Sci. 2012, 5, 9808-9826; b) X. Zhou, M. W. Nolte, H. B. Mayes, B. H. Shanks, L. J. Broadbelt, Ind. Eng. Chem. Res. 2014, 53, 13274–13289; c) X. Zhou, M. W. Nolte, H. B. Mayes, B. H. Shanks, L. J. Broadbelt, AlChE J. 2016, 62, 766–777.

[3] J. Zhang, Y. S. Choi, C. G. Yoo, T. H. Kim, R. C. Brown, B. H. Shanks, ACS Sustainable Chem. Eng. 2015, 3, 293-301.