ReviewBioengineering (Basel, Switzerland)2024
Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.
Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Beyond conventional imaging: curvelet-AI convergence for non-invasive microalgal lipid quantification and biofuel optimization.Bioprocess and biosystems engineering · 2026Review
- Research Progress on Bioinspired Superhydrophobic Photothermal Anti-/Deicing Coatings.Biomimetics (Basel, Switzerland) · 2026Review
- Precision Microalgae: A New Conceptual Framework for Bioengineering Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Microalgae-mediated removal and biodegradation of indoor volatile organic compounds: mechanisms, performance metrics, and engineering constraints.Biodegradation · 2026Review
- Advancing microalgae biomass cultivation for an integrated sustainable wastewater treatment and resource recovery.iScience · 2026Review
- High-Purity Phycocyanin Production from Cyanobacteria Using a Biorefinery Approach: Life Cycle Assessment and Comparative Process Benchmarking.Microorganisms · 2026Article
- Integrating Biomimetic Reasoning Into Early-Stage Design Thinking for Sustainable Textile Development.Biomimetics (Basel, Switzerland) · 2026Article
- Microalgae-Driven Circular Agriculture: System Integration, Nutrient Recovery, and AI-Assisted Optimization.Microorganisms · 2026Review
- Selenium biofortification: integrating one health and sustainability.Journal of the science of food and agriculture · 2026Review
- Smart-driven bioengineering techniques for enhancing microalgal biohydrogen production.Biotechnology for biofuels and bioproducts · 2026Review
- VerdaML: an explainable ensemble machine learning framework with AI-driven interactive decision support system for biomass yield optimisation and Nitrogen-Stress Risk Classification in verrucodesmus verrucosus photobioreactors.Frontiers in bioengineering and biotechnology · 2026Article
- Biology-aware scaling of microalgal biofuels: bioprocess constraints and data-centric digital twins.Frontiers in microbiology · 2026Review
- Artificial Intelligence in Biofuels: Progress, Trends, and Directions.Chemistry & biodiversity · 2026Review
- Development and validation of a machine learning model for predicting high-risk distant metastatic recurrence in differentiated thyroid cancer.Frontiers in medicine · 2026Article
- Harnessing microalgae for bioproducts: innovations in synthetic biology.World journal of microbiology & biotechnology · 2025Review
- Microalgae: revolutionizing skin repair and enhancement.Biotechnology reports (Amsterdam, Netherlands) · 2025Review
- Review
- Influence of Light Regimes on Production of Beneficial Pigments and Nutrients by Microalgae for Functional Plant-Based Foods.Foods (Basel, Switzerland) · 2025Review
- Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality.Marine drugs · 2025Review
- Advancing Gel Systems with Natural Extracts: Antioxidant, Antimicrobial Applications, and Sustainable Innovations.Gels (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.