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DOE OSTI · 1865706

Improving Microalgal Biomass Productivity Using Weather-Forecast-Informed Operations

Gao, Song (ORCID:0000000272104445)·Yan, Hongxiang·Beirne, Nathan·Wigmosta, Mark·Huesemann, Michael
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Abstract

The operation of microalgal cultivation systems, such as culture dilution associated with harvests, affects biomass productivity. However, the constantly changing incident light and ambient temperature in the outdoor environment make it difficult to determine the operational parameters that result in optimal biomass growth. To address this problem, we present a pond operation optimization tool that predicts biomass growth based on future weather conditions to identify the optimal dilution rate that maximizes biomass productivity. The concept was tested by comparing the biomass productivities of three dilution scenarios: standard batch cultivation (no dilution), fixed-rate dilution (harvest 60% of the culture every three days), and weather-forecast-informed dilution. In the weather-forecast-informed case, the culture was diluted daily, and the dilution ratio was optimized by the operation optimization tool according to the future 24 h weather condition. The results show that the weather-forecast-informed dilution improved the biomass productivity by 47% over the standard batch cultivation and 20% over the fixed-rate dilution case. These results demonstrate that the pond operation optimization tool could help pond operators to make decisions that maximize biomass growth in the field under ever-changing weather conditions.

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59 BASIC BIOLOGICAL SCIENCESBiomass productivityDilution rateMicroalgaeWeather forecast-informedphotobioreactor

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Gao, Song (ORCID:0000000272104445), Yan, Hongxiang, Beirne, Nathan, Wigmosta, Mark, Huesemann, Michael. 2022-04-29. Improving Microalgal Biomass Productivity Using Weather-Forecast-Informed Operations. https://doi.org/10.3390/cells11091498

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