Date of Award

Spring 2018

Degree Type

Publishable Paper

Degree Name

Master of Science (MS)

Department

Geophysical Engineering

Committee Chair

Xiaobing Zhou

First Advisor

Martha Apple

Second Advisor

Marvin Speece

Third Advisor

Gary Wyss

Abstract

Biofuel from microalgae is a very promising renewable energy resource. Growth of microalgae depends on ambient temperature, appropriate nutrients in water, and light condition for photosynthesis. As microalgae grow, the depth of light penetration decreases and the growing conditions at depth deteriorate. Monitoring of microalgae concentration during their growing phase is imperative to ensure efficiency in biomass production. Conventionally, cell concentration (number of cells per unit volume) of microalgae solution is estimated by taking images of samples under microscope and then counted and estimated using the Metallized Hemacytometer Hausser Bright-Linewe (MHHBL) method developed by Hausser Scientific. This method of measuring cell concentration of microalgal solution is time consuming and can be performed only in the laboratory. The objectives of this study are to develop algorithms that can quickly estimate the cell concentration of three different species of microalgae, A. cylindrica cylindrica (A. cylindrica), Nannochloropsis gaditana (N. gaditana), and PW-95 (Neospongiococcum sp.) through measurement of hyperspectral reflectance and the subsequently derived extinction coefficient. These algae species are candidates for biofuel due to their relative high level of lipid content. We used an Analytical Spectral Devices (ASD) hyperspectral radiometer (350 ~ 1050 nm) with the spectral resolution of 1 nanometer to measure the hyperspectral reflectance of each sample for which the cell concentration was estimated using the MHHBL method. A multi-layer radiative transfer model was developed to derive the hyperspectral extinction coefficient (EC). For reflectance-based algorithm development, regression analyses between multiple reflectance-based indices with band positions optimized and cell concentration were performed and assessed. For EC-based algorithm development, regression analyses between multiple EC-based indices with band positions optimized using EC data and cell concentration were performed and assessed. These indices include Single Band Model (SBM), Normalized Difference Chlorophyll Index (NDCI), Band Ratio (BR), Three Band Model (TBM), and Spectral Shape (SS). Regression results show that the reflectance-based Band Ratio (BR) algorithm and the EC-based Spectral Shape (SS) index in Near Infrared (NIR) band show the best results for all the three microalgae species with > 0.990, MRE < 5%, and RMSE < 5%, especially for A. cylindrica and N. gaditana, with > 0.999, MRE < 2% and RMSE < 1%. These relationships can be used to quickly estimate microalgae cell concentration from hyperspectral measurements that can be carried out quickly and easily in either lab or in field.

Comments

A publishable paper to fulfill degree requirements for a Master of Science in Geophysical Engineering

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