Pyrolytic characteristics of fine materials from municipal solid waste using TG-FTIR, Py-GC/MS, and deep learning approach: Kinetics, thermodynamics, and gaseous products distribution

Fine materials (FM) from municipal solid waste (MSW) classification require disposal, and pyrolysis is a feasible method for the treatments. Hence, the behavior, kinetics, and products of FM pyrolysis were investigated in this study. A deep learning algorithm was firstly employed to predict and veri...

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Bibliographic Details
Published in:Chemosphere (Oxford) 2022-04, Vol.293, p.133533-133533, Article 133533
Main Authors: Lin, Kunsen, Tian, Lu, Zhao, Youcai, Zhao, Chunlong, Zhang, Meilan, Zhou, Tao
Format: Article
Language:English
Subjects:
activation energy
algorithms
carbon dioxide
Deep Learning
Fine materials
Gas Chromatography-Mass Spectrometry
Gases
Kinetics
mass spectrometry
municipal solid waste
Products
Pyrolysis
Solid Waste
species
Spectroscopy, Fourier Transform Infrared
temperature
Thermodynamics
Thermogravimetry
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Summary:Fine materials (FM) from municipal solid waste (MSW) classification require disposal, and pyrolysis is a feasible method for the treatments. Hence, the behavior, kinetics, and products of FM pyrolysis were investigated in this study. A deep learning algorithm was firstly employed to predict and verify the TG data during the process of FM pyrolysis. The results showed that FM pyrolysis could be divided into drying (
ISSN:0045-6535
1879-1298
DOI:10.1016/j.chemosphere.2022.133533