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Title: Microalgal growth and lipid production : trends, multiple regression models, and validation in a photobioreactor
Author: Guha Roy, Aimee
ISNI:       0000 0004 5353 1112
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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Algae are a promising new source of oil for biodiesel. They are aquatic organisms that do not require cropland, and they can produce many useful side-products for bioenergy, aquaculture, and nutraceutical production. To be cost-effective, algae need high and reliable oil productivities; however, there is still a great deal to learn about the effects of culturing conditions on algae growth rates and lipid production. These culturing conditions include light intensity, gas flow, use of CO2, and culture volume. An extensive database of published research on algae growth rates and lipid contents under a wide variety of environmental conditions was prepared. By graphing data from 116 publications on 132 microalgae species, several key trends were identified relating to culturing parameters and algae biomass and lipid production. In addition, data from 131 publications on 128 microalgae species were graphed to look at presence of flagella, nutrient limitation, lipid productivity, and productivity tradeoffs. Moreover, cell size information was gathered for 146 species. The interactions between culture variables are complex, so it is difficult to quantify the degree to which each culture variable affects algae growth rates and lipid production. Therefore, several multivariate analyses were performed to generate a set of general and simple predictive models to assess specific growth rates, maximum lipid contents, and volumetric lipid productivities. These models were used to determine which culture parameters were significant predictors of algae growth rates and lipid production, and the contribution of each environmental parameter was quantified. In addition to models for algae in general, genera specific models were prepared for Chlorella, Isochrysis, Nannochloropsis, Phaeodactylum, and Tetraselmis. These models show high predictive capabilities, and they greatly extend the range of species-specific multiple regression models available. Furthermore, one Tetraselmis model was validated using Tetraselmis impellucida growth experiments in a large novel photobioreactor.
Supervisor: Field, Robert; Cui, Zhangfeng Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Biology ; Engineering & allied sciences ; Algae ; Biofuel ; Biodiesel ; Photobioreactor ; Energy ; Mathematical Model ; Tetraselmis ; Specific Growth Rate ; Maximum Lipid Content