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 |
|