Date of Award:


Document Type:


Degree Name:

Master of Science (MS)


Mechanical and Aerospace Engineering

Committee Chair(s)

Byard D. Wood, Jason C. Quinn


Byard D. Wood


Jason C. Quinn


Rees R. Fullmer


Growing demand for energy worldwide has increased interest in the production of renewable fuels, with microalgae representing a promising feedstock. The large-scale feasibility of microalgae based biofuels has previously been evaluated through technoeconomic and environmental impact assessments, with limited work performed on resource requirements. This study presents the use of a modular engineering system process model, founded on literature, to evaluate the nutrient (nitrogen and phosphorus) and carbon dioxide resource demand of five large-scale microalgae to biofuels production systems. The baseline scenario, representative of a near-term large-scale production system includes process models for growth, dewater, lipid extraction, anaerobic digestion, and biofuel conversion. Optimistic and conservative process scenarios are simulated to represent practical best and worst case system performance to bound the total resource demand of large-scale production. Baseline modeling results combined with current US nutrient availability from fertilizer and wastewater are used to perform a scalability assessment. Results show nutrient requirements represent a major barrier to the development of microalgae based biofuels to meet the US Department of Energy 2030 renewable fuel goal of 30% of transportation fuel, or 60 billion gallons per year. Specifically, results from the baseline and optimistic fuel production systems show wastewater sources can provide sufficient nutrients to produce 3.8 billion gallons and 13 billion gallons of fuel per year, corresponding to 6% and 22% of the DOE goal, respectively. High resource demand necessitates nutrient recovery from the lipid-extracted algae, thus limiting its use as a value-added co-product. Discussion focuses on system scalability, comparison of results to previous resource assessments, and model sensitivity of nutrient and carbon dioxide resource requirements to system parameter inputs.