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A comprehensive study of facemasks pyrolysis using Py-GC/MS kinetic analysis and ANN modeling
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Document Title
A comprehensive study of facemasks pyrolysis using Py-GC/MS kinetic analysis and ANN modeling
Author
Idris I.A. Nisamaneenate J. Atong D. Sricharoenchaikul V.
Affiliations
Department of Environmental Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand; National Metal and Materials Technology Center (MTEC) National Science and Technology Development Agency (NSTDA) Thailand Science Park Pathum Thani12120 Thailand; Energy Research Institute Chulalongkorn University Bangkok 10330 Thailand
Type
Article
Source Title
Arabian Journal of Chemistry
ISSN
18785352
Year
2024
Volume
17
Issue
3
Open Access
All Open Access Gold
Publisher
Elsevier B.V.
DOI
10.1016/j.arabjc.2024.105605
Abstract
The thermo-kinetics of pyrolysis and product distribution of facemasks as a blend of filter layers were explored in this study. Pyrolysis products were studied using Py-GC/MS at 550 ?C for 30 s resulting in predominantly aliphatic hydrocarbons (82.6%): alkanes (34.5%) and alkenes (48.1%). Notable gaseous products identified include propene 2-methyl pentane and 2 3-dimethyl-1-pentene while the dominant species among the cycloalkanes were 1 2 3 5-tetraisopropylcyclohexane and 1 3 5-trimethylcyclohexane. Furthermore we developed a chemical reaction mechanism to describe the main products formed during pyrolysis. Besides the activation energy was predicted using model-free methods namely FR (214.2 kJ/mol) KAS (200.5 kJ/mol) and FWO (200.6 kJ/mol) as a function of conversion. The Coats � Redfern (CR) model-fitting method revealed that the pyrolysis reaction mechanism within the temperature range of 400 � 550 ?C (pyrolysis active zone) belonged to one-dimensional diffusion and contracting cylinder model. The reliability of these results was further affirmed using the Criado method showing agreement between the experimental and theoretical master plots. The thermodynamic parameters for facemask degradation indicated an endothermic process (?H = 205.5 kJ/mol ?G = 182.4 kJ/mol and ?S = 0.03 kJ/mol?K). To predict weight loss during facemask pyrolysis we developed an artificial neural network (ANN) model that considered heating rate and temperature as inputs. The most efficient model structure involved an ANN with 2 input layers (2 neurons each) 2 hidden layers (each with 10 neurons) and an output layer (1 neuron). This study is crucial for advancing our understanding of the theoretical aspects of polymeric waste pyrolysis. ? 2024 The Author(s)
Keyword
Diffusion | kinetics | Mechanisms | Py-GC/MS | Pyrolysis | Surgical facemask
License
CC BY-NC-ND
Rights
Authors
Publication Source
WOS