Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects

This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML can optimize various stages, including identification, classification, cultivation, harvesting, drying, and conversion to biofuels. This review also highlights the integration of ML with technologies such as the Internet of Things (IoT) for real-time monitoring and management of bioprocesses. It discusses the adaptability and flexibility of ML in the context of microalgae biotechnology, focusing on diverse algorithms such as Artificial Neural Networks, Support Vector Machines, Decision Trees, and Random Forests, while emphasizing the importance of data collection and preparation. Additionally, current ML applications in microalgae biofuel production are reviewed, including strain selection, growth optimization, system monitoring, and lipid extraction.