Academic Journal
A Review on Machine Learning and Bioinformatics to Study Biofouling in Marine Renewable Energy Devices: Modeling, Performance Prediction, and Maintenance Planning.
| Τίτλος: | A Review on Machine Learning and Bioinformatics to Study Biofouling in Marine Renewable Energy Devices: Modeling, Performance Prediction, and Maintenance Planning. |
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| Συγγραφείς: | Hasil, Shah Dad, Zahid, Zahid, Michailides, Constantine, Shi, Wei, Irshad, Feroz |
| Πηγή: | Journal of Marine Science & Engineering; Mar2026, Vol. 14 Issue 6, p549, 23p |
| Θεματικοί όροι: | Machine learning, Bioinformatics, Fouling, Computer simulation, Computerized maintenance management systems, Nucleic acids, Ocean energy resources, Digital twin |
| Περίληψη: | Marine renewable energy (MRE) systems operate in harsh marine environments where long-term exposure to seawater leads to biofouling, resulting in increased surface roughness, hydrodynamic drag, added mass, structural loading, sensor degradation, and reduced energy production. Despite its significant operational and economic impact, biofouling management in MRE devices has traditionally relied on manual inspections and empirical growth models, which offer limited predictive capability. This review provides a structured, data-centric synthesis of recent advances in machine learning (ML) and bioinformatics approaches for biofouling modeling, performance prediction, and maintenance planning in offshore wind turbines, tidal turbines, and wave energy converters. The study systematically examines key fouling locations and associated engineering impacts, and analyzes the major data streams used for predictive modeling, including SCADA and condition-monitoring time series, metocean variables, inspection imagery, laboratory and field experiments, and environmental DNA (eDNA) sequencing outputs. We compare modeling strategies ranging from physics-based simulations to classical ML, deep learning, computer vision, and hybrid physics-informed frameworks, and discuss how biological indicators such as microbial community profiles and eDNA-derived taxa abundances can be integrated as predictive features. The review further outlines emerging digital twin architectures for fouling-aware performance forecasting and maintenance decision support. Finally, we identify key challenges including data scarcity, cross-site generalization, validation practices, and uncertainty quantification, and propose future research directions toward integrated, proactive biofouling management systems in marine renewable energy infrastructure. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Marine Science & Engineering is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20771312&ISBN=&volume=14&issue=6&date=20260315&spage=549&pages=549-571&title=Journal of Marine Science & Engineering&atitle=A%20Review%20on%20Machine%20Learning%20and%20Bioinformatics%20to%20Study%20Biofouling%20in%20Marine%20Renewable%20Energy%20Devices%3A%20Modeling%2C%20Performance%20Prediction%2C%20and%20Maintenance%20Planning.&aulast=Hasil%2C%20Shah%20Dad&id=DOI:10.3390/jmse14060549 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A Review on Machine Learning and Bioinformatics to Study Biofouling in Marine Renewable Energy Devices: Modeling, Performance Prediction, and Maintenance Planning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hasil%2C+Shah+Dad%22">Hasil, Shah Dad</searchLink><br /><searchLink fieldCode="AR" term="%22Zahid%2C+Zahid%22">Zahid, Zahid</searchLink><br /><searchLink fieldCode="AR" term="%22Michailides%2C+Constantine%22">Michailides, Constantine</searchLink><br /><searchLink fieldCode="AR" term="%22Shi%2C+Wei%22">Shi, Wei</searchLink><br /><searchLink fieldCode="AR" term="%22Irshad%2C+Feroz%22">Irshad, Feroz</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Marine Science & Engineering; Mar2026, Vol. 14 Issue 6, p549, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Fouling%22">Fouling</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Computerized+maintenance+management+systems%22">Computerized maintenance management systems</searchLink><br /><searchLink fieldCode="DE" term="%22Nucleic+acids%22">Nucleic acids</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+energy+resources%22">Ocean energy resources</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Marine renewable energy (MRE) systems operate in harsh marine environments where long-term exposure to seawater leads to biofouling, resulting in increased surface roughness, hydrodynamic drag, added mass, structural loading, sensor degradation, and reduced energy production. Despite its significant operational and economic impact, biofouling management in MRE devices has traditionally relied on manual inspections and empirical growth models, which offer limited predictive capability. This review provides a structured, data-centric synthesis of recent advances in machine learning (ML) and bioinformatics approaches for biofouling modeling, performance prediction, and maintenance planning in offshore wind turbines, tidal turbines, and wave energy converters. The study systematically examines key fouling locations and associated engineering impacts, and analyzes the major data streams used for predictive modeling, including SCADA and condition-monitoring time series, metocean variables, inspection imagery, laboratory and field experiments, and environmental DNA (eDNA) sequencing outputs. We compare modeling strategies ranging from physics-based simulations to classical ML, deep learning, computer vision, and hybrid physics-informed frameworks, and discuss how biological indicators such as microbial community profiles and eDNA-derived taxa abundances can be integrated as predictive features. The review further outlines emerging digital twin architectures for fouling-aware performance forecasting and maintenance decision support. Finally, we identify key challenges including data scarcity, cross-site generalization, validation practices, and uncertainty quantification, and propose future research directions toward integrated, proactive biofouling management systems in marine renewable energy infrastructure. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Marine Science & Engineering is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/jmse14060549 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 549 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Bioinformatics Type: general – SubjectFull: Fouling Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Computerized maintenance management systems Type: general – SubjectFull: Nucleic acids Type: general – SubjectFull: Ocean energy resources Type: general – SubjectFull: Digital twin Type: general Titles: – TitleFull: A Review on Machine Learning and Bioinformatics to Study Biofouling in Marine Renewable Energy Devices: Modeling, Performance Prediction, and Maintenance Planning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hasil, Shah Dad – PersonEntity: Name: NameFull: Zahid, Zahid – PersonEntity: Name: NameFull: Michailides, Constantine – PersonEntity: Name: NameFull: Shi, Wei – PersonEntity: Name: NameFull: Irshad, Feroz IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20771312 Numbering: – Type: volume Value: 14 – Type: issue Value: 6 Titles: – TitleFull: Journal of Marine Science & Engineering Type: main |
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