In the current literature, the life cycle, technoeconomic, and resource assessments of microalgae-based biofuel production systems have relied on growth models extrapolated from laboratory-scale data, leading to a large uncertainty in results. This type of simplistic growth modeling overestimates productivity potential and fails to incorporate biological effects, geographical location, or cultivation architecture. This study uses a large-scale, validated, outdoor photobioreactor microalgae growth model based on 21 reactor- and species-specific inputs to model the growth of Nannochloropsis. This model accurately accounts for biological effects such as nutrient uptake, respiration, and temperature and uses hourly historical meteorological data to determine the current global productivity potential. Global maps of the current near-term microalgae lipid and biomass productivity were generated based on the results of annual simulations at 4,388 global locations. Maximum annual average lipid yields between 24 and 27 m3·ha−1·y−1, corresponding to biomass yields of 13 to 15 g·m−2·d−1, are possible in Australia, Brazil, Colombia, Egypt, Ethiopia, India, Kenya, and Saudi Arabia. The microalgae lipid productivity results of this study were integrated with geography-specific fuel consumption and land availability data to perform a scalability assessment. Results highlight the promising potential of microalgae-based biofuels compared with traditional terrestrial feedstocks. When water, nutrients, and CO2 are not limiting, many regions can potentially meet significant fractions of their transportation fuel requirements through microalgae production, without land resource restriction. Discussion focuses on sensitivity of monthly variability in lipid production compared with annual average yields, effects of temperature on productivity, and a comparison of results with previous published modeling assumptions.
Moody J.W., McGinty C.M., Quinn J.C. (2014). Global evaluation of biofuel potential from microalgae. Proceedings of the National Academy of Sciences, 111:8691-8696. 10.1073/pnas.1321652111.