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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.
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]
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Database: Engineering Source
Description
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]
ISSN:19447442
DOI:10.1002/ep.70115