Applied machine learning for prediction of waste plastic pyrolysis towards valuable fuel and chemicals production

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.

Publication DOI: https://doi.org/10.1016/j.jaap.2023.105857
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Chemical Engineering & Applied Chemistry
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Energy and Bioproducts Research Institute (EBRI)
College of Engineering & Physical Sciences > Aston Institute of Materials Research (AIMR)
College of Engineering & Physical Sciences > Aston Advanced Materials
Funding Information: The work was supported by an Institutional Links grant (No. 527641843), under the Turkey partnership. The grant is funded by the UK Department for Business, Energy, and Industrial Strategy together with the Scientific and Technological Research Council of
Additional Information: Funding Information: The work was supported by an Institutional Links grant (No. 527641843), under the Turkey partnership. The grant is funded by the UK Department for Business, Energy, and Industrial Strategy together with the Scientific and Technological Research Council of Turkey (TÜBİTAK; ˙Project no. 119N302) and delivered by the British Council. The author Yi Cheng and Jiawei Wang would like to acknowledge the Marie Skłodowska Curie Actions Fellowships by The European Research Executive Agency (H2020-MSCA-IF-2020, no. 101025906). The author Jiawei Wang would also like to acknowledge the support from Guangdong Science and Technology Program, No. 2021A0505030008. Copyright © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Uncontrolled Keywords: Decision tree,Machine learning,Pyrolysis,Ultimate analysis,Waste plastics,Analytical Chemistry,Fuel Technology
Publication ISSN: 1873-250X
Last Modified: 23 May 2024 07:18
Date Deposited: 30 Jan 2023 16:39
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 0013?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2023-01-05
Published Online Date: 2023-01-05
Accepted Date: 2023-01-02
Authors: Cheng, Yi
Ekici, Ecrin
Yildiz, Güray
Yang, Yang (ORCID Profile 0000-0003-2075-3803)
Coward, Brad
Wang, Jiawei (ORCID Profile 0000-0001-5690-9107)

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