Nuclear Reactor Transient Diagnostics Using Classification and AutoML.
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| Title: | Nuclear Reactor Transient Diagnostics Using Classification and AutoML. |
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| Authors: | Mena, Pedro1 (AUTHOR) menapedr@isu.edu, Borrelli, R. A.2,3 (AUTHOR), Kerby, Leslie1,2 (AUTHOR) |
| Source: | Nuclear Technology. Feb 2022, Vol. 208 Issue 2, p232-245. 14p. |
| Abstract: | Artificial intelligence is becoming a larger part of operations for many industries. One industry where this is occurring rapidly is the nuclear industry. Researchers from around the world are looking to implement this technology in various areas of the nuclear industry. This paper explores the use of machine learning to diagnose problems. This project makes use of synthetic data collected from a Generic Pressurized Water Reactor (GPWR) simulator on whether a reactor is operating normally or experiencing one of four different transient events. A dataset was created consisting of over 30 000 reactor operational states. The data were explored and wrangled using Python and the Pandas package, using a variety of methods. Once ready, the data were randomly shuffled, with half the data being used for training and the other half being used for testing. Six different machine learning models were created using scikit-learn and the AutoML package Tree-based Pipeline Optimization Tool (TPOT). These models were created using six data scaling methods along with six feature reduction/selection methods. These models were validated using accuracy, precision, recall, and F1 score. The accuracy of the individual transients was also calculated. All six of the models had validation scores above 95%, with the decision tree and logistic regression models performing the best. These results are promising for the possible future use of machine learning in reactor diagnostics. [ABSTRACT FROM AUTHOR] |
| Copyright of Nuclear Technology is the property of Taylor & Francis Ltd 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 154691143 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Nuclear Reactor Transient Diagnostics Using Classification and AutoML. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mena%2C+Pedro%22">Mena, Pedro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> menapedr@isu.edu</i><br /><searchLink fieldCode="AR" term="%22Borrelli%2C+R%2E+A%2E%22">Borrelli, R. A.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kerby%2C+Leslie%22">Kerby, Leslie</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nuclear+Technology%22">Nuclear Technology</searchLink>. Feb 2022, Vol. 208 Issue 2, p232-245. 14p. – Name: Abstract Label: Abstract Group: Ab Data: Artificial intelligence is becoming a larger part of operations for many industries. One industry where this is occurring rapidly is the nuclear industry. Researchers from around the world are looking to implement this technology in various areas of the nuclear industry. This paper explores the use of machine learning to diagnose problems. This project makes use of synthetic data collected from a Generic Pressurized Water Reactor (GPWR) simulator on whether a reactor is operating normally or experiencing one of four different transient events. A dataset was created consisting of over 30 000 reactor operational states. The data were explored and wrangled using Python and the Pandas package, using a variety of methods. Once ready, the data were randomly shuffled, with half the data being used for training and the other half being used for testing. Six different machine learning models were created using scikit-learn and the AutoML package Tree-based Pipeline Optimization Tool (TPOT). These models were created using six data scaling methods along with six feature reduction/selection methods. These models were validated using accuracy, precision, recall, and F1 score. The accuracy of the individual transients was also calculated. All six of the models had validation scores above 95%, with the decision tree and logistic regression models performing the best. These results are promising for the possible future use of machine learning in reactor diagnostics. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nuclear Technology is the property of Taylor & Francis Ltd 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=154691143 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00295450.2021.1905470 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 232 Titles: – TitleFull: Nuclear Reactor Transient Diagnostics Using Classification and AutoML. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mena, Pedro – PersonEntity: Name: NameFull: Borrelli, R. A. – PersonEntity: Name: NameFull: Kerby, Leslie IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb 2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00295450 Numbering: – Type: volume Value: 208 – Type: issue Value: 2 Titles: – TitleFull: Nuclear Technology Type: main |
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