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Artificial Intelligence-Powered Algal Biodiesel: The Future of Biofuel Production Through Data-Driven Biotechnology
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DOI：10.1002/bit.70305.png)
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Abstract
En 中文
Microalgae are promising feedstocks for sustainable biodiesel production, although their industrial use is currently limited by their biological complexity and high production costs. Recently, artificial intelligence (AI) technologies have been identified as promising, game-changing tools that could optimize algal biodiesel processes by leveraging data-driven biology. The purpose of this Review is to provide an integrated summary of recent developments in machine learning, deep learning, and hybrid models of microalgal biodiesel, analyzing AI frameworks that integrate biological and physical information with microalgal biodiesel process models to improve productivity, biological stability, and sustainability. It is essential to critically analyze the challenges of data quality and explainability in machine learning models, and to explain their use in techno-economic analysis of microalgal biodiesel. In the final part, directions for developing an independent microalgal biorefinery system using artificial intelligence tools are outlined to accelerate the lab-to-production transition of climate-relevant biofuels.
Keywords:
algal biodiesel
artificial intelligence
deep learning
hybrid models
machine learning
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