Improved simulation of lignocellulosic biomass pyrolysis plant using chemical kinetics in Aspen Plus® and comparison with experiments
Issued Date
2023-02-01
Resource Type
ISSN
11100168
Scopus ID
2-s2.0-85136741353
Journal Title
Alexandria Engineering Journal
Volume
63
Start Page
199
End Page
209
Rights Holder(s)
SCOPUS
Bibliographic Citation
Alexandria Engineering Journal Vol.63 (2023) , 199-209
Suggested Citation
Jaroenkhasemmeesuk C., Tippayawong N., Shimpalee S., Ingham D.B., Pourkashanian M. Improved simulation of lignocellulosic biomass pyrolysis plant using chemical kinetics in Aspen Plus® and comparison with experiments. Alexandria Engineering Journal Vol.63 (2023) , 199-209. 209. doi:10.1016/j.aej.2022.07.060 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/82836
Title
Improved simulation of lignocellulosic biomass pyrolysis plant using chemical kinetics in Aspen Plus® and comparison with experiments
Author's Affiliation
Other Contributor(s)
Abstract
The successful use (for performance predictions, in previous work) of process modeling and simulation of fast pyrolysis plants has made these techniques imperative in the design and operation of the pyrolysis plant itself. The current work proposes an expansion of pyrolysis simulation in the Aspen Plus® model for lignocellulosic biomass, which is based on the kinetic reaction mechanisms of different materials under varied operational settings. This simulation allows the yield prediction for both slow pyrolysis at 350–450 °C, with residence times in the range 30–50 s, and fast pyrolysis at 450–600 °C, 1–5 s residence time. The comparison between simulation and experimental results was performed, focusing on the product yields and components. The results revealed a significant correlation between the two sets of results, not only for slow pyrolysis but also for quick pyrolysis processes of biomass, with less than 8% difference error when compared to the pilot plant and earlier experimental work. The simulation model was proven to be suitable for predicting pyrolysis yields and products within the common temperature and residence time ranges, and it was discovered to be ideal for predicting the outcomes of fast pyrolysis products within the specific scope of operation. The results from the model showed a high level of reliability in estimating the composition of different compounds obtainable, not only from slow pyrolysis but also from fast pyrolysis, with a 0.99 Pearson's correlation coefficient.