Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/2586
Title: Prediction of gas product yield from packaging waste pyrolysis: support vector and Gaussian process regression models
Authors: Yapıcı, Ece
Akgün, Haluk
Özkan, Korhan
Günkaya, Zerrin
Özkan, Aysun
Banar, Maryam
Keywords: C
LDPE
Gaussian process regression
LDPE
Pyrolysis
Support vector regression
Catalytic Pyrolysis
Density Polyethylene
Power-Generation
Plastic Wastes
Neural-Network
H-Beta
Recovery
Degradation
Aluminum
Ldpe
Issue Date: 2022
Publisher: Springer
Abstract: The pyrolysis process enables the transformation of plastic waste into products such as oil, solid residue, and gas at temperatures of around 300-900 degrees C by thermal decomposition. Conversion of such waste into valuable products depends on various factors, such as raw material composition, temperature, heating rate, residence time, and catalyst. From this point of view, in this study, predictions of gas product yield based on different pyrolysis conditions including waste types (LDPE-C/LDPE), temperature (400-600-800 degrees C), heating rate (5-10-20 degrees C/min), type of catalyst (zeolite-clay-sludge) and amount of catalyst (5%, 10%, 15%, by weight) were carried out with support from the vector regression (SVR) and the Gaussian process (GPR) models using the results of experimental studies performed under various conditions. Different kernel functions were used for SVR (Linear, Quadratic, Cubic, Gaussian) and GPR (Squared Exponential, Matern 5/2, Exponential, Rational Quadratic). The Gaussian Kernel Function presented a good prediction performance (89% R-2 and 0.0011 RMSE) for SVR while the Exponential Kernel Function was the most appropriate for GPR (93% R-2 and 0.0011 RMSE). On the other hand, the deviations in the SVR model with linear Kernel change over a wide range of 0.25-80.85%, and the GPR model with exponential kernel show deviations close to each other in the range of 0.06-3.91%. The present study provides new information for future studies by understanding the pyrolysis process of plastic waste and predicting product yield.
URI: https://doi.org/10.1007/s13762-022-04013-1
https://hdl.handle.net/20.500.13087/2586
ISSN: 1735-1472
1735-2630
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

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