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Novel machine learning unlocks high lipid productivity and resolves trade-offs in algal biofuel production

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  • Lopez, Amanda M.
  • Savage, Sean
  • Zhou, Zhi

Abstract

Algal biofuel is promising but challenged by balancing biomass productivity (BP) and lipid content (LC) to achieve high lipid productivity (LP). The aim of this study was to develop a novel machine learning (ML) approach to model complex nonlinear relationships between cultivation conditions and LP in Chlorella species. We created a database of 38 cultivation variables from over 300 algal growth scenarios (12,000+ data points) from peer-reviewed studies. These data were used to train the first ML random forest model to maximize LP. The newly developed RF-ML model was able to predict high LP (>0.018 g L−1 d−1) with an accuracy of 95.2 %. This approach offers a novel solution to a critical challenge and leads to a substantial reduction (by <96.7 %) in the broad operational ranges of algal cultivation conditions for improved LP, holding great promise for potential cost savings in algal cultivation. Our partial dependence plots (PDPs) identified exogenous iron and phosphorus as key drivers of high LP, challenging the longstanding focus on nitrogen starvation as the dominant lipid trigger strategy in the literature. This framework, at the core of artificial intelligence (AI), offers a promising avenue to boost algae lipid production for biodiesel through efficient, data-driven cultivation.

Suggested Citation

  • Lopez, Amanda M. & Savage, Sean & Zhou, Zhi, 2026. "Novel machine learning unlocks high lipid productivity and resolves trade-offs in algal biofuel production," Renewable Energy, Elsevier, vol. 256(PA).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pa:s0960148125015654
    DOI: 10.1016/j.renene.2025.123901
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    1. Mladen Bošnjaković & Robert Santa & Antonija Vučić & Zoran Crnac, 2025. "Analysis of Biodiesel from Algae Using the SWOT-AHP Method: Strategic Insights for a Green Energy Future," Clean Technol., MDPI, vol. 7(3), pages 1-25, August.

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