Exploring Biomass Quality within the Biomass-to-Biofuel Supply Chain Using Principal Component Analysis
With a focused effort to replace petroleum-based fuels and chemicals with a more sustainable source of energy, research in biofuel has become a topic of interest. The goal of this thesis is to first establish an important gap in the literature through an in-depth review of current biomass-to-biofuel research, followed by a statistical analysis of biomass preprocessing data. The data obtained from pre-processing two types of biomass, with varying moisture content, are used to develop conclusions on how variables such as throughput, grinding energy, and logistics costs are related to the aforementioned factors, as well as to each other. Test data from Idaho National Laboratory was obtained and Principal Component Analysis (PCA) was used to investigate the correlation between selected factors and the overall performance of the system. The following factors were used for this study: corn stover versus switchgrass and incoming moisture percentage from each stage of grinding. The principal components were analyzed, and it was found that principal component 1, which was a value derived from measured throughput, measured energy consumption, and preprocessing cost, was the source of the most significant variance. Results from PCA also indicate that the type of biomass had a significant effect on the system's performance; ideal setup, which returned the largest throughput while minimizing energy consumption, was found to be preprocessing switchgrass with a moisture content of 10%.