AI-Driven Algae Biorefineries: A New Era for Sustainable Bioeconomy

• Published in: Current pollution reports Vol. 11; no. 1; p. 21
• Main Authors: Abdullah, Mohammed, Malik, Hafiza Aroosa, Ali, Abiha, Boopathy, Ramaraj, Vo, Phong H. N., Danaee, Soroosh, Ralph, Peter, Malik, Sana
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 11.04.2025; Springer Nature B.V
• Subjects: Algae; Algorithms; Aquatic Pollution; Artificial intelligence; Atmospheric Protection/Air Quality Control/Air Pollution; bioeconomics; Biomass; Biorefineries; biorefining; Biotechnology; carbon; Climate change; Climate change mitigation; Commercialization; Dewatering; Earth and Environmental Science; Economics; Efficiency; Emissions; Environment; Environmental Law/Policy/Ecojustice; Enzymes; Ethics; Global temperatures; humans; Industrial Pollution Prevention; labor; Learning algorithms; Machine learning; Monitoring; Monitoring/Environmental Analysis; Optimization; phenotype; Pollution; prediction; Prediction models; Refining; Review; risk; Spectrum analysis; Sustainability; temperature; Topical Collection on Biology and Pollution; Waste Water Technology; Water Management; Water Pollution Control; Conventional cultivation; Automation; Algae biorefinery; Artificial intelligence; Circular bioeconomy; Machine learning
• ISSN: 2198-6592; 2198-6592
• DOI: 10.1007/s40726-025-00352-y

Purpose of Review The urgent need for a transition toward a sustainable and greener future is underscored by the projected risk of a global temperature increase of up to 3 °C above pre-industrial levels, driven by rising carbon emissions. Algae biorefineries offer a promising solution to these challenges. However, the high production and downstream processing costs continue to hinder successful commercialization. This review provides a comprehensive overview of several AI-driven innovations: phenotypic screening for strain improvement, monitoring of environmental parameters, machine learning for optimizing dewatering efficiency, predictive modeling for algae growth and yield, and AI-controlled drying systems for biomass preservation. Recent Findings Integrating machine learning algorithms and predictive modeling can transform algae cultivation by automating the process with continuous monitoring and feedback systems, significantly reducing labor costs while enhancing process economics and efficiency. Accurate prediction of optimal harvesting times can further decrease harvesting costs, address key scalability issues, and facilitate the broader commercialization of algae for diverse biotechnological applications. Summary In the future, smart biorefineries that integrate artificial intelligence into algae production facilities will be pivotal in enhancing process efficiency and economics within circular and sustainable frameworks. While AI continues to impact various fields to ease human effort, ethical considerations must remain central to its use, especially as this sector grows rapidly. Graphical Abstract Highlights • Algae biorefineries present a viable solution to mitigate climate change challenges. • Machine learning and predictive modeling offer transformative potential for algae farming. • AI can reduce costs, solve scalability issues, and accelerate algae commercialization. • Integrating AI into algae production will create smart biorefineries, but ethical considerations must remain a priority as the sector grows.