Norazaliza, Mohd Jamil (2015) Mathematical modeling and simulation of biofuel production from lignocellulosic biomass. PhD thesis, University of South Carolina (Contributors, UNSPECIFIED: UNSPECIFIED).
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Abstract
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 fibers, and processing at high solid concentration is a challenging task because it will affect the mixing quality between enzyme and cellulose particles and the generation of sugars. Thus, understanding factors that affect the rheology of biomass suspension is crucial in order to maximize the production at a minimum cost. Our aim is to develop a solution strategy for the modeling and simulation of high solid concentration of biomass suspension during enzymatic hydrolysis. Also, we intend to develop a multi-scale model for enzymatic hydrolysis that captures the reaction kinetics of cellulose chains in PBE form, cellulose rod orientation and interaction, as well as hydrodynamics and plasticity of the biomass mixture. We extended and improved the established kinetic model proposed by Griggs et al. [Griggs et al., 2012a]. We built the reduced order models by ignoring significantly small terms and determined the approximate solutions by employing asymptotic analysis method. Liquid crystal theory was adopted to study the cellulose fibers. The complete model was solved using DAE-QMOM technique in finite-element software package, COMSOL. Essentially, we made a clear connection between microscopic, mesoscopic, and macroscopic properties of biomass slurries undergoing enzymatic hydrolysis. The results show that the quality of mixing within a reactor is crucial in optimizing the hydrolysis product. Also, the biomass suspension shows non-Newtonian behaviors such as shear thinning, yield stress, and normal stress difference, which is in agreement with experimental results. The extended model improved the predictive capabilities, hence increased our understanding on the behavior of biomass suspension.
| Item Type: | Thesis (PhD) |
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| Additional Information: | Thesis (Doctor of Philosophy (Mathematics)) -- University of South Carolina -- 2015 |
| Uncontrolled Keywords: | Renewable energy; biofuel; lignocellulosic biomass |
| Subjects: | Q Science > QA Mathematics T Technology > T Technology (General) |
| Depositing User: | Ms. Nurezzatul Akmal Salleh |
| Date Deposited: | 27 Jun 2016 01:36 |
| Last Modified: | 17 Nov 2021 02:22 |
| URI: | http://umpir.ump.edu.my/id/eprint/13533 |
| Download Statistic: | View Download Statistics |
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