Bibliographic Details
| Title: |
Catalytic Pyrolysis of Raw and Thermally Treated Cellulose Using Different Acidic Zeolites. |
| Authors: |
Srinivasan, Vaishnavi1, Adhikari, Sushil1 sza0016@auburn.edu, Chattanathan, Shyamsundar1, Tu, Maobing2, Park, Sunkyu3 |
| Source: |
BioEnergy Research. Sep2014, Vol. 7 Issue 3, p867-875. 9p. |
| Subjects: |
Pyrolysis, Cellulose, Biomass, Catalysis, Aromatic compounds |
| Abstract: |
Fast pyrolysis of biomass using zeolite catalyst has shown to be effective in improving aromatic production. This study focuses on aromatic production through catalytic pyrolysis of major biomass constituent i.e., cellulose. Furthermore, cellulose was torrefied to understand torrefaction's effect on pyrolysis products. The influence of SiO/AlO ratios of zeolite (ZSM-5) catalyst on aromatic production during pyrolysis of raw and torrefied cellulose was investigated. Results showed that the catalyst acidity played a pivotal role in eliminating anhydro sugars and other oxygenated compounds while producing more aromatics. The maximum aromatic yield (~25 wt%) was obtained when ZSM-5 with the highest acidity (SiO/AlO = 30) was used, while the lowest yield (7.99 wt%) was obtained when the least acidic catalyst was used (SiO/AlO = 280) for raw cellulose pyrolysis. Torrefaction process showed to have positive effect on the aromatic production from pyrolysis. There were no aromatics produced from pyrolysis of raw cellulose in the absence of catalyst, whereas significant amount of aromatic compounds were produced from both catalytic and noncatalytic pyrolyses of torrefied cellulose. The aromatic hydrocarbons produced from catalytic pyrolysis of torrefied cellulose were 5 % more than those produced from raw cellulose at the highest temperature and catalyst acidity (SiO/AlO = 30). [ABSTRACT FROM AUTHOR] |
|
Copyright of BioEnergy Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |