Publication: Process modelling and simulation of fast pyrolysis plant of lignocellulosic biomass using improved chemical kinetics in Aspen Plus®
Issued Date
2020-01-01
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ISSN
22839216
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2-s2.0-85082744127
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Mahidol University
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SCOPUS
Bibliographic Citation
Chemical Engineering Transactions. Vol.78, (2020), 73-78
Suggested Citation
Chawannat Jaroenkhasemmeesuk, Nakorn Tippayawong, Derek B. Ingham, Mohammed Pourkashanian Process modelling and simulation of fast pyrolysis plant of lignocellulosic biomass using improved chemical kinetics in Aspen Plus®. Chemical Engineering Transactions. Vol.78, (2020), 73-78. doi:10.3303/CET2078013 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/54511
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Title
Process modelling and simulation of fast pyrolysis plant of lignocellulosic biomass using improved chemical kinetics in Aspen Plus®
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Abstract
Copyright © 2020, AIDIC Servizi S.r.l. The successful operation of biomass pyrolysis plant on an industrial scale would showcase and promote the possibility of practical decarbonizing energy projects. This work presents a comprehensive Aspen Plus® modeling work of fast pyrolysis processes for lignocellulosic biomass based on kinetic reaction mechanisms. The simulation uses mass and energy balance calculations to forecast the product yields and composition depending on different sets of operating conditions (temperature, residence time) and feedstock composition includes conventional components, nonconventional components, and solids component of lignocellulosic biomass. The reaction kinetic models are developed from the principle of biomass pyrolysis using data available from the literature. The product yield from a biomass pyrolysis pilot plant is used to demonstrate the validation of the model. The results show a high correlation of the results for both slow and fast pyrolysis processes compared with those from the pilot plant and the previous works. The simulation model is found to be able to correctly predict fast pyrolysis products’ yields within the typical range of operation considered (high reaction temperatures with low residence times). In conclusion, the model proved to be suitable for predicting fast pyrolysis reactions for lignocellulosic biomass feedstock and can be used for estimating the trend of pyrolysis products without the need for experimental data with t-test of differential of product yield’s trend at 95 % confidence interval as 0.00327. This fast pyrolysis model offers rapid assessment for energy projects associated with the transition towards low-carbon development in Asia.