Evaluation of environmentally hazardous waste tires and waste engine oils in a diesel engine: An extensive ANN modeling and experimental investigation.
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| Title: | Evaluation of environmentally hazardous waste tires and waste engine oils in a diesel engine: An extensive ANN modeling and experimental investigation. |
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| Authors: | Yıldız, Abdulkerim1 (AUTHOR), Aydın, Selman2 (AUTHOR) selman.aydin@batman.edu.tr |
| Source: | Environmental Progress & Sustainable Energy. May/Jun2026, Vol. 45 Issue 3, p1-15. 15p. |
| Subjects: | Waste tires, Artificial neural networks, Petroleum waste, Diesel motors, Air pollutants, Sulfuration, Energy consumption, Distillation |
| Abstract: | The aim of this study is to recycle waste plastics and waste mineral oils, which threaten the environment and human health, into energy. Diesel‐like fuel was produced from waste minerals and tire oils by pyrolytic distillation. Among these produced fuels, the sulfur rate in DL‐MF (Diesel‐like mineral fuel) is within the limits of DF, and in DL‐TF (Diesel‐like tire fuel), due to the high sulfur rate, the sulfur removal process is carried out by applying 10% perlite, 10% CaO, and 10% zeolite catalysts. Then, these two fuels were modeled with ANN based on the experimental results obtained from running an IC engine with a blend of DF in different proportions at a constant rpm and different loads. The performance, combustion, and emission values were examined, and in the combustion parameter, calculations with model application for MGT, CHR, NHRR, RPR, and CP for each ANN prediction could be made. In ANN modeling, the network was trained with 10 neurons using feed‐forward experimental data. The best R2 estimation of CGP followed values: 0.99998 with DLTF30‐5 bar Bmep and 0.99989 with DLMF30‐2.5 bar Bmep. It has been found that the parameters in the experimental data match well with the robust values predicted by the ANN determined in the network test data. [ABSTRACT FROM AUTHOR] |
| Copyright of Environmental Progress & Sustainable Energy is the property of Wiley-Blackwell 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: 194163759 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluation of environmentally hazardous waste tires and waste engine oils in a diesel engine: An extensive ANN modeling and experimental investigation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yıldız%2C+Abdulkerim%22">Yıldız, Abdulkerim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Aydın%2C+Selman%22">Aydın, Selman</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> selman.aydin@batman.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Progress+%26+Sustainable+Energy%22">Environmental Progress & Sustainable Energy</searchLink>. May/Jun2026, Vol. 45 Issue 3, p1-15. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Waste+tires%22">Waste tires</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Petroleum+waste%22">Petroleum waste</searchLink><br /><searchLink fieldCode="DE" term="%22Diesel+motors%22">Diesel motors</searchLink><br /><searchLink fieldCode="DE" term="%22Air+pollutants%22">Air pollutants</searchLink><br /><searchLink fieldCode="DE" term="%22Sulfuration%22">Sulfuration</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Distillation%22">Distillation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The aim of this study is to recycle waste plastics and waste mineral oils, which threaten the environment and human health, into energy. Diesel‐like fuel was produced from waste minerals and tire oils by pyrolytic distillation. Among these produced fuels, the sulfur rate in DL‐MF (Diesel‐like mineral fuel) is within the limits of DF, and in DL‐TF (Diesel‐like tire fuel), due to the high sulfur rate, the sulfur removal process is carried out by applying 10% perlite, 10% CaO, and 10% zeolite catalysts. Then, these two fuels were modeled with ANN based on the experimental results obtained from running an IC engine with a blend of DF in different proportions at a constant rpm and different loads. The performance, combustion, and emission values were examined, and in the combustion parameter, calculations with model application for MGT, CHR, NHRR, RPR, and CP for each ANN prediction could be made. In ANN modeling, the network was trained with 10 neurons using feed‐forward experimental data. The best R2 estimation of CGP followed values: 0.99998 with DLTF30‐5 bar Bmep and 0.99989 with DLMF30‐2.5 bar Bmep. It has been found that the parameters in the experimental data match well with the robust values predicted by the ANN determined in the network test data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Progress & Sustainable Energy is the property of Wiley-Blackwell 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: 10.1002/ep.70115 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Waste tires Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Petroleum waste Type: general – SubjectFull: Diesel motors Type: general – SubjectFull: Air pollutants Type: general – SubjectFull: Sulfuration Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Distillation Type: general Titles: – TitleFull: Evaluation of environmentally hazardous waste tires and waste engine oils in a diesel engine: An extensive ANN modeling and experimental investigation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yıldız, Abdulkerim – PersonEntity: Name: NameFull: Aydın, Selman IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May/Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19447442 Numbering: – Type: volume Value: 45 – Type: issue Value: 3 Titles: – TitleFull: Environmental Progress & Sustainable Energy Type: main |
| ResultId | 1 |
