Pyrolysis behaviour of ultrafiltration polymer composite membranes (PSF/ PET): Kinetic, thermodynamic, prediction modelling using artificial neural network and volatile product analysis

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dc.contributor.author Yousef, Samy
dc.contributor.author Eimontas, Justas
dc.contributor.author Striugas, Nerijus
dc.contributor.author Mohamed, Alaa
dc.contributor.author Abdelnaby, Mohammed Ali
dc.date.accessioned 2024-05-02T05:52:45Z
dc.date.available 2024-05-02T05:52:45Z
dc.date.issued 2024-04
dc.identifier.other https://doi.org/10.1016/j.fuel.2024.131779
dc.identifier.uri http://repository.msa.edu.eg/xmlui/handle/123456789/5953
dc.description.abstract This study aims to explore the feasibility of managing ultrafiltration polymer composite membranes (UPCM) waste and converting it into valuable chemicals and energy products using a pyrolysis process. The thermal decomposition experiments were performed on polysulfone (PSF)/polyethylene terephthalate (PET) membranes using thermogravimetric analysis (TG). The vapors generated during the thermochemical process were analyzed under different heating rate conditions using TG-FTIR and GC/MS. In addition, the kinetic and thermodynamic parameters of the pyrolysis process were determined using conventional modeling methods and artificial neural network (ANN) method. The results demonstrated that the PSF/PET feedstock exhibits ahigh volatile matter content (77 % wt.%), which can be completely decomposed up to 600 °C by 79 wt%. While TG-FTIR analysis showed that the released vapors contained aromatic groups and benzoic acid (89.21 wt% at 15˚C/min) as the main GC/MS compound. Moreover, the kinetic analysis demonstrated complete decomposition of the membranes at a lower activation energy (151 kJ/mol). Meanwhile, the ANN model exhibited high performance in predicting the degradation stages of PSF/PET membranes under unknown heating conditions. This approach shows potential for modeling the thermal decomposition of ultrafiltration composite membranes more broadly. en_US
dc.description.uri https://www.scimagojr.com/journalsearch.php?q=16313&tip=sid&clean=0
dc.language.iso en en_US
dc.publisher Elsevier B.V en_US
dc.relation.ispartofseries Fuel;Volume 3691 August 2024 Article number 131779
dc.subject Artificial neural network; PSF/PET membranes; Pyrolysis; Pyrolysis kinetic behaviour; Ultrafiltration polymer composite memberes en_US
dc.title Pyrolysis behaviour of ultrafiltration polymer composite membranes (PSF/ PET): Kinetic, thermodynamic, prediction modelling using artificial neural network and volatile product analysis en_US
dc.type Article en_US
dc.identifier.doi https://doi.org/10.1016/j.fuel.2024.131779
dc.Affiliation October University for modern sciences and Arts MSA


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