Modeling, optimization, and life cycle assessment of bioethanol production

Shadbahr, Jalil (2017) Modeling, optimization, and life cycle assessment of bioethanol production. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Second generation bioethanol produced primarily from lignocellulosic biomass resources has attracted great attention over the past two decades due to its numerous advantages such as: (i) potential to reduce environmental impacts in comparison to fossil fuels (ii) capability to mix with gasoline and use in vehicles without modifications in regular engines, and (iii) not competing with food resources that are being used in first generation ethanol. Simultaneous Saccharification and Fermentation (SSF) approach was proffered for this research over the Separate Hydrolysis and Fermentation (SHF) method to mitigate the inhibition impacts of hydrolysis products and reduce the capital costs of process. SSF process was experimentally studied in a batch media at various levels of enzyme loading and sugars concentration to investigate the interactive influences of sugars concentration and enzyme loading on the final ethanol yield and concentration. Results indicate that cellulase inhibition by cellobiose and glucose is remarkable when enzyme loading is increased from intermediate to high level, particularly at high initial sugars concentrations. The acquired experimental data from batch SSF reactions were consequently applied to determine five major kinetic parameters (k1, k2, Keq, λ, and μ) of kinetic models which incorporate the synergistic effects of supplementing β-glucosidase with cellulase on cellulose conversion and end-product inhibitions. The accuracy and reliability of the derived kinetic parameters were then verified by the good agreement between experimental results and the simulation concentration profiles of sugars and ethanol using tuned parameters under different reaction conditions. Multi-objective optimization of the SSF process based on mechanistic kinetic and reaction models was carried out in this study to further improve the SSF performance by simultaneously maximizing the ethanol yield/concentration and minimizing enzyme loading. Controlled elitist genetic algorithm, a variant of NSGA II, was used for bi-objective optimization of three case studies with a varied combination of objectives and constraints. The optimized objectives in each case were validated by experiments at the corresponding operating parameters. Comparing the results with non-optimized experiments proved that optimization is capable of improving the objectives. Lower environmental impact is an important criterion when selecting the best technology for lignocellulose to bioethanol conversion. In this study, the influence of pretreatment process design on the environmental performance of the chained ethanol production process was evaluated by life cycle analysis. Resulting substrates by two pretreatment designs led to significant differences in final ethanol concentration. The amount of produced ethanol as the functional unit in the life cycle analysis of a bioethanol production plant will significantly affect the environmental performance of the system. LCA was performed in small scale (pretreatment unit) and large scale (bioethanol plant) for both scenarios and results confirmed that pretreatment process leading to higher final ethanol concentration helps to mitigate the environmental impacts of the whole production process in most environmental impact categories.

Item Type: Thesis (Doctoral (PhD))
Item ID: 12836
Additional Information: Includes bibliographical references.
Keywords: Multi Objective Optimization, Bioethanol, Kinetic Modeling, Life Cycle Assessment, Lignocellulosic Biomass, Simultaneous Saccharification and Fermentation
Department(s): Engineering and Applied Science, Faculty of
Date: July 2017
Date Type: Submission
Library of Congress Subject Heading: Ethanol as fuel; Biomass--conversion; Sugarcane--Biotechnology.

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