Semester

Summer

Date of Graduation

2009

Document Type

Thesis

Degree Type

MS

College

Davis College of Agriculture, Natural Resources and Design

Department

Wood Science and Technology

Committee Chair

Ben Dawson-Andoh.

Abstract

The heterogeneous property of biomass (wood) affects its potential of being converted into any form of fuel in different ways (both positive and negative effects). Therefore in other to efficiently utilized biomass as a raw material for conversion into any form of clean alternative fuel to displace some of the fossil fuel we consume in the United State on a commercial scale basis, a quick, robust, non destructive on/in/at-line method of characterizing the physical and chemical properties of biomass that are relevant to the bio-refinery industry is imperative.;This study discusses the potential of using near infrared spectroscopy (NIRS) and fluorescence spectroscopy (FS) coupled with multivariate data analysis (MVDA) as a robust and rapid process analytical technology (PAT) to characterize the physical and chemical properties of two potential biomass feedstock (yellow-poplar and northern red oak) in its solid state. This study is aimed at rapidly detecting the properties of potential biomass feedstock to be used in the bio-refinery online before any conversion process is begun. This will reduce cost of manufacturing bio-fuels, provide real time results of biomass characteristics reduce waste and produce a much consistent product. The potential utilization of fluorescence spectrometer which is much cheaper, rapid and sensitive spectrometer with equal model performance as the NIR spectrometer models will reduce the cost of PAT even further.;Generally, the results of this study showed that both NIR and FS can be used as rapid PAT method to characterize the physical and chemical properties of northern red oak and yellow-poplar with moderate to high prediction performance. The NIR prediction models generally exhibited slightly higher prediction model performance as compared to similar models of the same response variable developed with the fluorescence spectra data.

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