Hybrid Feature Generation and Selection with a Focus on Novel Genetic-Based Generated Feature Method for Modeling Products in the Sulfur Recovery Unit.
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| Title: | Hybrid Feature Generation and Selection with a Focus on Novel Genetic-Based Generated Feature Method for Modeling Products in the Sulfur Recovery Unit. |
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| Authors: | Moayedi, Farshad1 (AUTHOR) farshad.moayedi@ut.ac.ir, Abolghasemi, Hossein1 (AUTHOR), Shokri, Saeid2 (AUTHOR), Ganji, Hamid3 (AUTHOR), Hamedi, Amir Hossein1 (AUTHOR) |
| Source: | Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Jul2023, Vol. 48 Issue 7, p9023-9034. 12p. |
| Subject Terms: | *Feature selection, *Product recovery, *Artificial intelligence, *Chemical industry |
| Abstract: | Nowadays, varied challenges in modeling variables in chemical industries have led to the implementation of designing intelligent systems to tackle these issues. In the sulfur recovery unit, online controlling of pollutants in different streams, which are extremely significant in the environmental context, is an arduous task and classic methods have numerous limitations. This paper aims to develop a reliable method in the field of feature engineering, meaning genetic-based generated features (GBGF); furthermore, it studies four feature selection methods and represents some innovations in the performance of the ant colony method (BACO), providing a powerful technique, the hybrid feature generation and selection (HF-G&S), for the prediction of different variables. Using the HF-G&S method, compared to the classic methods in feature generation and selection, the accuracy of the models increases significantly. The main reason for this achievement is introducing the GBGF technique and the simultaneous construction of compelling features, identifying essential features and repeating this cycle. Ultimately, by implementing this technique, the prediction precision of the SVR model for the H2S and SO2 concentration in the output flow reached over 98 and 97 percent, respectively. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 164420491 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid Feature Generation and Selection with a Focus on Novel Genetic-Based Generated Feature Method for Modeling Products in the Sulfur Recovery Unit. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moayedi%2C+Farshad%22">Moayedi, Farshad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> farshad.moayedi@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Abolghasemi%2C+Hossein%22">Abolghasemi, Hossein</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shokri%2C+Saeid%22">Shokri, Saeid</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ganji%2C+Hamid%22">Ganji, Hamid</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hamedi%2C+Amir+Hossein%22">Hamedi, Amir Hossein</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Arabian+Journal+for+Science+%26+Engineering+%28Springer+Science+%26+Business+Media+B%2EV%2E+%29%22">Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )</searchLink>. Jul2023, Vol. 48 Issue 7, p9023-9034. 12p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br />*<searchLink fieldCode="DE" term="%22Product+recovery%22">Product recovery</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Chemical+industry%22">Chemical industry</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Nowadays, varied challenges in modeling variables in chemical industries have led to the implementation of designing intelligent systems to tackle these issues. In the sulfur recovery unit, online controlling of pollutants in different streams, which are extremely significant in the environmental context, is an arduous task and classic methods have numerous limitations. This paper aims to develop a reliable method in the field of feature engineering, meaning genetic-based generated features (GBGF); furthermore, it studies four feature selection methods and represents some innovations in the performance of the ant colony method (BACO), providing a powerful technique, the hybrid feature generation and selection (HF-G&S), for the prediction of different variables. Using the HF-G&S method, compared to the classic methods in feature generation and selection, the accuracy of the models increases significantly. The main reason for this achievement is introducing the GBGF technique and the simultaneous construction of compelling features, identifying essential features and repeating this cycle. Ultimately, by implementing this technique, the prediction precision of the SVR model for the H2S and SO2 concentration in the output flow reached over 98 and 97 percent, respectively. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=164420491 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s13369-023-07609-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 9023 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Product recovery Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Chemical industry Type: general Titles: – TitleFull: Hybrid Feature Generation and Selection with a Focus on Novel Genetic-Based Generated Feature Method for Modeling Products in the Sulfur Recovery Unit. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moayedi, Farshad – PersonEntity: Name: NameFull: Abolghasemi, Hossein – PersonEntity: Name: NameFull: Shokri, Saeid – PersonEntity: Name: NameFull: Ganji, Hamid – PersonEntity: Name: NameFull: Hamedi, Amir Hossein IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 2193567X Numbering: – Type: volume Value: 48 – Type: issue Value: 7 Titles: – TitleFull: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) Type: main |
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