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Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity

Author

Listed:
  • Bin Long

    (Texas A&M University)

  • Bart Fischer

    (Texas A&M University)

  • Yining Zeng

    (National Renewable Energy Laboratory)

  • Zoe Amerigian

    (Texas A&M University)

  • Qiang Li

    (Texas A&M University)

  • Henry Bryant

    (Texas A&M University)

  • Man Li

    (Texas A&M University
    Texas A&M University)

  • Susie Y. Dai

    (Texas A&M University
    Texas A&M University)

  • Joshua S. Yuan

    (Texas A&M University
    Texas A&M University)

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.

Suggested Citation

  • Bin Long & Bart Fischer & Yining Zeng & Zoe Amerigian & Qiang Li & Henry Bryant & Man Li & Susie Y. Dai & Joshua S. Yuan, 2022. "Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-021-27665-y
    DOI: 10.1038/s41467-021-27665-y
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    Cited by:

    1. Singh, Saurabh & Morya, Raj & Jaiswal, Durgesh Kumar & Keerthana, S. & Kim, Sang-Hyoun & Manimekalai, R. & PrudĂȘncio de Araujo Pereira, Arthur & Verma, Jay Prakash, 2024. "Innovations and advances in enzymatic deconstruction of biomass and their sustainability analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    2. Despotovic, Miroslav & Glatschke, Matthias, 2024. "Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy," SocArXiv 6qmhf, Center for Open Science.
    3. Grira, Soumaya & Abu Khalifeh, Hadil & Alkhedher, Mohammad & Ramadan, Mohamad, 2023. "The conventional microalgal biofuel production process and the alternative milking pathway: A review," Energy, Elsevier, vol. 277(C).

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