Machine learning for algal biofuels: a critical review and perspective for the future

Abstract

In this work, machine learning (ML) applications in microalgal biofuel production are reviewed. First, the basic steps of algal biofuel production are summarized followed by a bibliometric analysis to demonstrate the major research trends in the field. Also, the major challenges related to the commercialization of technology are identified. Then, ML applications for various steps in the value chain are reviewed and analyzed systematically. Finally, a future perspective on the contribution of ML in the field is provided. Our analysis indicates that ML applications should focus on screening and selecting suitable strains, preferably together with some other value-added products, requiring close collaborations among the researchers in the field to construct an extensive microalgal strain database. Optimization of cultivation conditions appears to be another area where ML can be helpful. Although most published ML works on cultivation are not usually suitable to extract generalizable knowledge (due to the nonstandard nature of strains, wastewater, and irradiation), standard testing and methodologies related to reporting protocols should also be built through collaboration to build comparable and generalizable ML models.