ReviewChemistry & biodiversity2026
Artificial Intelligence in Biofuels: Progress, Trends, and Directions.
Review in Chemistry & biodiversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Process modeling and optimization of biochar production: contemporary and emerging techniques for environmental engineering applications.Biodegradation · 2026Review
- Smart nanobiocatalysts for waste-to-biofuel conversion: integrating Nano-Bio interfaces and AI-driven design.Frontiers in bioengineering and biotechnology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biofuels have the potential to improve the sustainability of transportation fuels. Production systems for biofuels are complex, and data-driven artificial intelligence (AI) modeling offers advanced capabilities for prediction, optimization, and quality control. However, large-scale applications of AI in biofuels production remain in their early stages compared to laboratory research. This article discusses how AI-based modeling is used for various bioconversion technologies in the effort to improve their efficiency, dependability, and management of biofuels. Furthermore, the applications of AI technology in various types of biofuels, including biodiesel, bioethanol, biobutanol, biomethanol, biohydrogen, biogas, and algal biofuels, are critically discussed. The benefits and drawbacks of applying AI-based modeling to manage, optimize, control, monitor, and predict biofuels yields are comprehensively investigated. Additionally, the use of fuzzy logic, genetic algorithms (GAs), artificial neural networks (ANNs), expert systems (ES), adaptive neuro-fuzzy inference system (ANFIS), hybrid AI techniques, and other AI-based methods to increase biofuels production yield and quality, as well as end-user system performance, are thoroughly reviewed. Finally, a concise summary of the present state of research milestones is provided, and most recent state-of-the-art studies are discussed. The technology readiness level analysis indicates that integrating AI with established technologies is the most effective commercial approach.
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Identifiers
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.