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Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction.
Polymers (Basel). 2020 Apr 12; 12(4)P

Abstract

Pyrolysis of waste low-density polyethylene (LDPE) is considered to be a highly efficient, promising treatment method. This work aims to investigate the kinetics of LDPE pyrolysis using three model-free methods (Friedman, Flynn-Wall-Qzawa (FWO), and Kissinger-Akahira-Sunose (KAS)), two model-fitting methods (Arrhenius and Coats-Redfern), as well as to develop, for the first time, a highly efficient artificial neural network (ANN) model to predict the kinetic parameters of LDPE pyrolysis. Thermogravimetric (TG) and derivative thermogravimetric (DTG) thermograms at 5, 10, 20 and 40 K min-1 showed only a single pyrolysis zone, implying a single reaction. The values of the kinetic parameters (E and A) of LDPE pyrolysis have been calculated at different conversions by three model-free methods and the average values of the obtained activation energies are in good agreement and ranging between 193 and 195 kJ mol-1. In addition, these kinetic parameters at different heating rates have been calculated using Arrhenius and Coats-Redfern methods. Moreover, a feed-forward ANN with backpropagation model, with 10 neurons in two hidden layers and logsig-logsig transfer functions, has been employed to predict the thermogravimetric analysis (TGA) kinetic data. Results showed good agreement between the ANN-predicted and experimental data (R > 0.9999). Then, the selected network topology was tested for extra new input data with a highly efficient performance.

Authors+Show Affiliations

Department of Chemical Engineering, King Faisal University, Al-Ahsa 31982, P.O. Box 380, Saudi Arabia.Department of Chemical Engineering, King Faisal University, Al-Ahsa 31982, P.O. Box 380, Saudi Arabia.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32290595

Citation

Dubdub, Ibrahim, and Mohammed Al-Yaari. "Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction." Polymers, vol. 12, no. 4, 2020.
Dubdub I, Al-Yaari M. Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction. Polymers (Basel). 2020;12(4).
Dubdub, I., & Al-Yaari, M. (2020). Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction. Polymers, 12(4). https://doi.org/10.3390/polym12040891
Dubdub I, Al-Yaari M. Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction. Polymers (Basel). 2020 Apr 12;12(4) PubMed PMID: 32290595.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction. AU - Dubdub,Ibrahim, AU - Al-Yaari,Mohammed, Y1 - 2020/04/12/ PY - 2020/03/07/received PY - 2020/04/05/revised PY - 2020/04/08/accepted PY - 2020/4/16/entrez PY - 2020/4/16/pubmed PY - 2020/4/16/medline KW - activation energy KW - artificial neural networks (ANN) KW - kinetics KW - low density polyethylene (LDPE) KW - pyrolysis KW - thermogravimetric analysis (TGA) JF - Polymers JO - Polymers (Basel) VL - 12 IS - 4 N2 - Pyrolysis of waste low-density polyethylene (LDPE) is considered to be a highly efficient, promising treatment method. This work aims to investigate the kinetics of LDPE pyrolysis using three model-free methods (Friedman, Flynn-Wall-Qzawa (FWO), and Kissinger-Akahira-Sunose (KAS)), two model-fitting methods (Arrhenius and Coats-Redfern), as well as to develop, for the first time, a highly efficient artificial neural network (ANN) model to predict the kinetic parameters of LDPE pyrolysis. Thermogravimetric (TG) and derivative thermogravimetric (DTG) thermograms at 5, 10, 20 and 40 K min-1 showed only a single pyrolysis zone, implying a single reaction. The values of the kinetic parameters (E and A) of LDPE pyrolysis have been calculated at different conversions by three model-free methods and the average values of the obtained activation energies are in good agreement and ranging between 193 and 195 kJ mol-1. In addition, these kinetic parameters at different heating rates have been calculated using Arrhenius and Coats-Redfern methods. Moreover, a feed-forward ANN with backpropagation model, with 10 neurons in two hidden layers and logsig-logsig transfer functions, has been employed to predict the thermogravimetric analysis (TGA) kinetic data. Results showed good agreement between the ANN-predicted and experimental data (R > 0.9999). Then, the selected network topology was tested for extra new input data with a highly efficient performance. SN - 2073-4360 UR - https://www.unboundmedicine.com/medline/citation/32290595/Pyrolysis_of_Low_Density_Polyethylene:_Kinetic_Study_Using_TGA_Data_and_ANN_Prediction_ L2 - https://www.mdpi.com/resolver?pii=polym12040891 DB - PRIME DP - Unbound Medicine ER -
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