Pyrolysis behaviour of ultrafiltration polymer composite membranes (PSF/ PET): Kinetic, thermodynamic, prediction modelling using artificial neural network and volatile product analysis
dc.Affiliation | October University for modern sciences and Arts MSA | |
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.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.identifier.doi | https://doi.org/10.1016/j.fuel.2024.131779 | |
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.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 |
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