Multi-scale Modelling for Cellulosic Biomass Mixture During Enzymatic Hydrolysis

Renewable energy or biofuel from lignocellulosic biomass is an alternative way to replace the depleting fossil fuels. The production cost can be reduced by increasing the concentration of biomass particles. However, lignocellulosic biomass is a suspension of natural fibres, and processing at high so...

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Bibliographic Details
Main Authors: Norazaliza, Jamil, Wang, Qi
Format: Conference or Workshop Item
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/19693/1/IEEE%20Japan.pdf
http://umpir.ump.edu.my/id/eprint/19693/
https://conferencealerts.com/show-event?id=173657
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Summary:Renewable energy or biofuel from lignocellulosic biomass is an alternative way to replace the depleting fossil fuels. The production cost can be reduced by increasing the concentration of biomass particles. However, lignocellulosic biomass is a suspension of natural fibres, and processing at high solid concentration is a challenging task. Thus, understanding the factors that affect the rheology of biomass suspension is crucial in order to maximize the production at a minimum cost. Our aim was to develop a multiscale modelling for enzymatic hydrolysis of cellulose by combining three scales: the macroscopic flow field, the mesoscopic particle orientation, and the microscopic reactive kinetics. The governing equations for the flow field, particle stress, kinetic equations, and particle orientation were coupled and were simultaneously solved using a finite element method based software, COMSOL. Essentially, clear connections were made between microscopic, mesoscopic, and macroscopic properties of biomass slurries undergoing enzymatic hydrolysis. One of the main results was the apparent viscosity and the yield stress increased with the increase in solid concentration. The results from the simulation model agreed qualitatively with the experimental findings. This approach has enables us to obtain better predictive capabilities, hence increasing our understanding on the behaviour of biomass suspension.