Overcoming challenges in microalgal bioprocessing through data-driven and computational approaches

Microalgae holds significant potential for sustainable food production, presenting challenges and opportunities. This review presents the key obstacles in microalgal bioprocessing, including cultivation methods optimisation, inconsistent biomass yields, high production costs, and stability of valuable compounds during processing. The gap in sustainable aspects of microalgae bioprocessing for food production is highlighted. Integrating omics technologies with microalgal bioprocessing can enhance the understanding of the cultivation process and improve microalgal yield and quality. When combined with advanced computational tools such as machine learning, digital twin technology, and process optimisation techniques, these approaches can significantly accelerate the adoption of circular economy principles in sustainable food production. Open databases and user-friendly software tools can lower learning barriers, encouraging broader microalgae research and development. Through interdisciplinary collaboration and innovation, microalgae can emerge as key players in sustainable food production, driving innovation and efficiency. • Bioprocessing challenges include cultivation, costs, yield, and stability. • Multi-omics data are crucial to enhance microalgae bioprocessing. • Machine learning algorithms can analyse data to enable predictive modelling. • Process optimisation with Digital Twin (DT) accelerates a circular bioeconomy. • Future DTs should incorporate multi-omics, machine learning, and sustainability tools.