Academic Journal
Recent review on deep learning-based Dempster–Shafer decision-making framework for chemical process monitoring.
| Τίτλος: | Recent review on deep learning-based Dempster–Shafer decision-making framework for chemical process monitoring. |
|---|---|
| Συγγραφείς: | Ewuzie, Remigius Nnadozie, Gunasekaran, Shivaneswar, Ahmad, Zainal, Md Nor, Norazwan |
| Πηγή: | Reviews in Chemical Engineering; Jul2026, Vol. 42 Issue 5, p381-404, 24p |
| Θεματικοί όροι: | Deep learning, Dempster-Shafer theory, Chemical process control, Uncertainty (Information theory), Fault diagnosis, Multiple criteria decision making, Sensor networks |
| Περίληψη: | Chemical process monitoring is vital for ensuring safe, efficient, and reliable operations in the chemical and process industries. However, practical implementation remains challenging due to complex nonlinear dynamics, high-dimensional sensor networks, and inherent uncertainty in industrial environments. Conventional monitoring and decision-making methods often struggle to manage these complexities effectively. This review provides a comprehensive overview of integrated deep learning (DL)–Dempster–Shafer theory (DST) frameworks for chemical process monitoring. DL techniques enable automatic extraction of nonlinear and temporal features from high-dimensional process data, while DST offers a principled mechanism for uncertainty representation, multi-source evidence fusion, and conflict resolution. The review summarizes core components of process monitoring, including sensor-based systems, fault detection and diagnosis, and intelligent decision-making strategies. It then examines recent DL–DST integration approaches, demonstrating improvements in diagnostic robustness, confidence-aware decision-making, and reliability under noisy and uncertain conditions. Benchmark studies, including the Tennessee Eastman process, highlight the advantages of DL–DST frameworks over standalone DL models. Industrial applicability is further analyzed, addressing challenges such as real-time deployment, process drift adaptation, high-dimensional data processing, and the absence of standardized integration guidelines. Future research directions are outlined to support scalable, interpretable, and industrially deployable monitoring solutions. [ABSTRACT FROM AUTHOR] |
| Copyright of Reviews in Chemical Engineering is the property of De Gruyter 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 |
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| Header | DbId: edb DbLabel: Complementary Index An: 195070835 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.76818847656 |
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| Items | – Name: Title Label: Title Group: Ti Data: Recent review on deep learning-based Dempster–Shafer decision-making framework for chemical process monitoring. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ewuzie%2C+Remigius+Nnadozie%22">Ewuzie, Remigius Nnadozie</searchLink><br /><searchLink fieldCode="AR" term="%22Gunasekaran%2C+Shivaneswar%22">Gunasekaran, Shivaneswar</searchLink><br /><searchLink fieldCode="AR" term="%22Ahmad%2C+Zainal%22">Ahmad, Zainal</searchLink><br /><searchLink fieldCode="AR" term="%22Md+Nor%2C+Norazwan%22">Md Nor, Norazwan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Reviews in Chemical Engineering; Jul2026, Vol. 42 Issue 5, p381-404, 24p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Dempster-Shafer+theory%22">Dempster-Shafer theory</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+process+control%22">Chemical process control</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Chemical process monitoring is vital for ensuring safe, efficient, and reliable operations in the chemical and process industries. However, practical implementation remains challenging due to complex nonlinear dynamics, high-dimensional sensor networks, and inherent uncertainty in industrial environments. Conventional monitoring and decision-making methods often struggle to manage these complexities effectively. This review provides a comprehensive overview of integrated deep learning (DL)–Dempster–Shafer theory (DST) frameworks for chemical process monitoring. DL techniques enable automatic extraction of nonlinear and temporal features from high-dimensional process data, while DST offers a principled mechanism for uncertainty representation, multi-source evidence fusion, and conflict resolution. The review summarizes core components of process monitoring, including sensor-based systems, fault detection and diagnosis, and intelligent decision-making strategies. It then examines recent DL–DST integration approaches, demonstrating improvements in diagnostic robustness, confidence-aware decision-making, and reliability under noisy and uncertain conditions. Benchmark studies, including the Tennessee Eastman process, highlight the advantages of DL–DST frameworks over standalone DL models. Industrial applicability is further analyzed, addressing challenges such as real-time deployment, process drift adaptation, high-dimensional data processing, and the absence of standardized integration guidelines. Future research directions are outlined to support scalable, interpretable, and industrially deployable monitoring solutions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Reviews in Chemical Engineering is the property of De Gruyter 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: 10.1515/revce-2025-0047 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 381 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Dempster-Shafer theory Type: general – SubjectFull: Chemical process control Type: general – SubjectFull: Uncertainty (Information theory) Type: general – SubjectFull: Fault diagnosis Type: general – SubjectFull: Multiple criteria decision making Type: general – SubjectFull: Sensor networks Type: general Titles: – TitleFull: Recent review on deep learning-based Dempster–Shafer decision-making framework for chemical process monitoring. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ewuzie, Remigius Nnadozie – PersonEntity: Name: NameFull: Gunasekaran, Shivaneswar – PersonEntity: Name: NameFull: Ahmad, Zainal – PersonEntity: Name: NameFull: Md Nor, Norazwan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01678299 Numbering: – Type: volume Value: 42 – Type: issue Value: 5 Titles: – TitleFull: Reviews in Chemical Engineering Type: main |
| ResultId | 1 |