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Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review
Abstract
The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.
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In the citing paper
“Studies indicate that these methods significantly improve the efficiency of biodiesel production by precisely regulating the nitrogen and phosphorus content in the culture medium [243].”
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 [243]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [15]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [15]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [15]
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Metadata
- Authors
- Oruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K. K., Gadhamshetty, V., & Bhattacharyya, D.
- Affiliations
- Indian Institute of Technology Hyderabad (IN)
- Khalifa University of Science and Technology (AE)
- King Mongkut's University of Technology North Bangkok (TH)
- Umeå University (SE)
- South Dakota School of Mines and Technology (US)
- Corresponding author
- Bhattacharyya, Debraj
- ORCID
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 876
- Pages
- 162797
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W4323923505
- Cited by
- 211
- References
- 95
- Citations by year
- 2026: 56 · 2025: 83 · 2024: 49 · 2023: 23
- FWCI
- 3.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The Science of The Total Environment (publishedVersion)
- PubMed (publishedVersion)
- Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) (acceptedVersion)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Physical Sciences › Environmental Science › Waste Management and Disposal › Wastewater Treatment and Reuse
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Keywords
- microalgae-based wastewater treatment, microalgae biorefinery, artificial intelligence, machine learning, nutrient removal, resource recovery, microalgae cultivation optimization, biomass productivity, artificial neural networks, random forest, support vector machine, genetic algorithm, decision tree, Internet of Things, circular economy, biofuels, biomaterials
- MeSH terms
- Machine Learning, Artificial Intelligence, Biotechnology, Waardenburg Syndrome, Water Purification, Biomass, Biofuels, Microalgae
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