Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12920
Title: Applied machine learning for prediction of waste plastic pyrolysis towards valuable fuel and chemicals production
Authors: Cheng, Yi
Ekici, Ecrin
Yıldız, Güray
Yang, Yang
Coward, Brad
Wang, Jiawei
Yıldız, Güray
Keywords: Decision tree
Machine learning
Pyrolysis
Ultimate analysis
Waste plastics
Elastomers
Issue Date: Jan-2023
Publisher: Elsevier
Abstract: Pyrolysis is a suitable conversion technology to address the severe ecological and environmental hurdles caused by waste plastics' ineffective pre- and/or post-user management and massive landfilling. By using machine learning (ML) algorithms, the present study developed models for predicting the products of continuous and non-catalytically processes for the pyrolysis of waste plastics. Along with different input datasets, four algorithms, including decision tree (DT), artificial neuron network (ANN), support vector machine (SVM), and Gaussian process (GP), were compared to select input variables for the most accurate models. Among these algorithms, the DT model exhibited generalisable and satisfactory accuracy (R2 > 0.99) with training data. The dataset with the elemental composition of waste plastics achieved better accuracy than that with the plastic-type for predicting liquid yields. These observations allow the predictions by the data from ultimate analysis when inaccessible to the plastic-type data in unknown plastic wastes. Besides, the combination of ultimate analysis input and the DT model also achieved excellent accuracy in liquid and gas composition predictions. © 2023 The Authors
URI: https://doi.org/10.1016/j.jaap.2023.105857
https://hdl.handle.net/11147/12920
ISSN: 0165-2370
Appears in Collections:Energy Systems Engineering / Enerji Sistemleri Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File SizeFormat 
1-s2.0-S0165237023000013-main.pdf3.85 MBAdobe PDFView/Open
Show full item record



CORE Recommender

SCOPUSTM   
Citations

7
checked on Feb 16, 2024

WEB OF SCIENCETM
Citations

6
checked on Feb 3, 2024

Page view(s)

98
checked on Feb 26, 2024

Download(s)

56
checked on Feb 26, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.