Global Evaluation of Microalgae Productivity Coupled with Scalability Assessment

Document Type

Presentation

Publication Date

4-10-2014

Faculty Mentor

Jason Quinn

Abstract

The evaluation of microalgae based biofuel production systems through lifecycle, technoeconomic, and resource assessments have based growth models on the extrapolation of laboratory-scale data due to the immaturity of the technology. This type of scaling leads to large uncertainty in the results due to the inaccurate modeling of the current near-term productivity potential which typically serves as the functional unit. This study integrates a large-scale validated outdoor microalgae growth model that utilizes 21 species and reactor specific inputs to accurately account for biological effects such as nutrient uptake, respiration, and temperature with hourly historical meteorological data from around the world to determine the current global productivity potential. A global map of the microalgae lipid and biomass productivity has been generated based on the results of annual simulations at 4,388 global locations spread over the 7 continents. Maximum annual average yields between 24-27 m3ˆha-1ˆyr-1 are found in Australia, Brazil, Colombia, Egypt, Ethiopia, India, Kenya, and Saudi Arabia with the monthly variability (minimum and maximum) yields of these locations ranging between 14 and 33 m3ˆha-1ˆyr-1. A scalability assessment which leverages geographic information systems data to evaluate geographically realized microalgae productivity, energy consumption, and land availability has been performed highlighting the promising potential of microalgae based biofuels compared to traditional terrestrial feedstocks. Results show many regions can meet their energy requirements through microalgae production without land resource restriction. Discussion focuses on sensitivity of monthly variability in lipid production compared to annual average yields, biomass productivity potential, effects of temperature on lipid production, and a comparison of results to previous published modeling assumptions.

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