Machine Learning-Informed and Synthetic Biology-Enabled Semi-Continuous Algal Cultivation to Unleash Renewable Fuel Productivity: Article No. 541

Bin Long, Bart Fisher, Yining Zeng, Zoe Amerigian, Qiang Li, Henry Bryant, Man Li, Susie Dai, Joshua Yuan

Research output: Contribution to journalArticlepeer-review

44 Scopus Citations

Abstract

Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning to inform the design of a semi-continuous algal cultivation (SAC) to sustain optimal cell growth and minimize mutual shading. An aggregation-based sedimentation (ABS) strategy is then designed to achieve low-cost biomass harvesting and economical SAC. The ABS is achieved by engineering a fast-growing strain, Synechococcus elongatus UTEX 2973, to produce limonene, which increases cyanobacterial cell surface hydrophobicity and enables efficient cell aggregation and sedimentation. SAC unleashes cyanobacterial growth potential with 0.1 g/L/hour biomass productivity and 0.2 mg/L/hour limonene productivity over a sustained period in photobioreactors. Scaling-up the SAC with an outdoor pond system achieves a biomass yield of 43.3 g/m2/day, bringing the minimum biomass selling price down to approximately $281 per ton.
Original languageAmerican English
Number of pages11
JournalNature Communications
Volume13
DOIs
StatePublished - 2022

NREL Publication Number

  • NREL/JA-2800-81465

Keywords

  • algal biofuel
  • bioimaging
  • cell growth
  • machine learning
  • mutual shading
  • semi-continuous algal cultivation

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