AI-Driven Algae Biorefineries: A New Era for Sustainable Bioeconomy
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Machine learning algorithms analyse historical data to predict changes in microalgae populations and their metabolism, facilitating adaptive bioreactor control strategies that automatically adjust environmental parameters in response to changing cultivation conditions.
“Machine learning (ML) algorithms in particular analyse historical data to predict changes in microalgae populations and their metabolism. This facilitates the implementation of adaptive bioreactor control strategies that automatically adjust environmental parameters in response to changing cultivation conditions [247].”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [247]
Advanced analytical algorithms and AI used in IoT systems enable modelling of microalgae growth dynamics and prediction of optimal cultivation conditions.
“Advanced analytical algorithms and artificial intelligence (AI) used in IoT systems enable the modelling of microalgae growth dynamics and the prediction of optimal cultivation conditions.”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [247]
AI-enabled control systems have been deployed to regulate CO2 input and light regimes, improving consistency and yield in semi-automated cultivation facilities.
“AI-enabled control systems have been deployed to regulate CO2 input and light regimes, improving consistency and yield in semi-automated cultivation facilities [64].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [64]
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References 160 · Cited by 24
- Authors
- Abdullah, M., Malik, H. A., Ali, A., Boopathy, R., Vo, P. H. N., Danaee, S., Ralph, P., & Malik, S.
- Affiliations
- Government College University, Faisalabad (PK)
- Zhengzhou University (CN)
- Nicholls State University (US)
- University of Technology Sydney (AU)
- Corresponding author
- Ralph, Peter
- Malik, Sana
- Venue
- Current Pollution Reports
- Published
- 2025-04-11
- Volume
- 11
- Issue
- 1
- ISSN
- 2198-6592 (ISSN-L)
- OpenAlex
- W4409319450
- Cited by
- 24
- References
- 160
- Citations by year
- 2026: 12 · 2025: 12
- FWCI
- 3.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Current Pollution Reports (publishedVersion)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Bioeconomy and Sustainability Development
- Keywords
- algae biorefinery, artificial intelligence, machine learning, phenotypic screening, strain improvement, dewatering efficiency, predictive modeling, drying systems, process automation, bioeconomy, circular economy, AI ethics
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