Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses
Open accessAbstract
This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.
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Models based on genetic algorithms enable calibration of mathematical models describing microalgae growth and allow accurate prediction of cell proliferation rates under different cultivation conditions.
“Models based on genetic algorithms (GA) enable the calibration of mathematical models describing microalgae growth and allow accurate prediction of cell proliferation rates under different cultivation conditions [242].”
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 [242]
Section 3.1.2 says genetic algorithms have been used to optimize parameters for microalgae growth, and Section 2 says kinetic models can predict microalgae growth. Neither passage establishes that GA-based models calibrate those mathematical models or accurately predict cell proliferation rates across cultivation conditions; Section 5.2 describes accurate predictions for other outcomes or model types. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAn integrated AI-driven cultivation framework offers potential to reduce nutrient and energy consumption.
“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 [17]
Section 4 (page not provided) reports that AI-based monitoring of cultivation conditions, including nutrient concentrations, enhanced energy efficiency. It does not say that nutrient consumption was reduced; monitoring nutrient levels is not evidence of reduced use. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAn integrated AI-driven cultivation framework offers potential to improve biomass yield by 30-50% for large-scale production.
“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 [17]
The Introduction and Section 5.2 say AI/ML can optimize microalgae cultivation and improve biomass production. They do not support the claimed 30–50% yield increase for large-scale production: Section 4 reports a 20% output increase with IoT integration, while Section 5.2 reports a 90% biomass-yield increase in a specific optimization study. No page numbers are available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe use of artificial intelligence and adaptive prediction can enable precise management of the cultivation process, customised to a specific strain and environmental conditions.
“The use of artificial intelligence and adaptive prediction can enable precise management of the cultivation process, customised to a specific strain and environmental conditions [242].”
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 [242]
An integrated AI-driven cultivation framework offers potential to enhance process control accuracy for large-scale production.
“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 [17]
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Wu, Y., Shan, L., Zhao, W., & Lu, X. (2025) · Marine Drugs · doi:10.3390/md23050184 · Cited by 59 · see citing works
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The power of microalgae: Cutting-edge applications and groundbreaking innovations
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Shahhoseyni, S., Rahmani, M., & Sivaram, A. (2026) · Algal Research · doi:10.1016/j.algal.2025.104503 · Cited by 3 · see citing works
- Authors
- Imamoglu, E.
- Affiliations
- Department of Bioengineering, Faculty of Engineering, Ege University, Izmir 35100, Turkey
- Venue
- Bioengineering
- Publisher
- MDPI AG
- Published
- 2024-11-13
- Volume
- 11
- Issue
- 11
- Pages
- 1143
- ISSN
- 2306-5354 (ISSN-L)
- PMC
- PMC11592280
- OpenAlex
- W4404322128
- Cited by
- 143
- References
- 119
- Citations by year
- 2026: 84 · 2025: 58 · 2024: 1
- FWCI
- 2.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioengineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, machine learning, real-time monitoring, species identification, growth condition optimization, microalgae harvesting, support vector machine, genetic algorithm, random forest, artificial neural networks, deep learning, overfitting, data availability, process control
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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