Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction

被引:81
|
作者
Dubdub, Ibrahim [1 ]
Al-Yaari, Mohammed [1 ]
机构
[1] King Faisal Univ, Dept Chem Engn, POB 380, Al Hasa 31982, Saudi Arabia
关键词
pyrolysis; low density polyethylene (LDPE); kinetics; activation energy; thermogravimetric analysis (TGA); artificial neural networks (ANN); ARTIFICIAL NEURAL-NETWORK; THERMAL-DEGRADATION KINETICS; SEWAGE-SLUDGE; BEHAVIORS; WASTE; POLYPROPYLENE; POLYSTYRENE; COMBUSTION; PARAMETERS; CRACKING;
D O I
10.3390/polym12040891
中图分类号
O63 [高分子化学(高聚物)];
学科分类号
070305 ; 080501 ; 081704 ;
摘要
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.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Comprehensive Kinetic Study of PET Pyrolysis Using TGA
    Alhulaybi, Zaid
    Dubdub, Ibrahim
    POLYMERS, 2023, 15 (14)
  • [2] Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks
    Yin, Xiaoxiao
    Tao, Junyu
    Chen, Guanyi
    Yao, Xilei
    Luan, Pengpeng
    Cheng, Zhanjun
    Li, Ning
    Zhou, Zhongyue
    Yan, Beibei
    FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING, 2023, 17 (01)
  • [3] Kinetic study of high density polyethylene (HDPE) pyrolysis
    Al-Salem, S. M.
    Lettieri, P.
    CHEMICAL ENGINEERING RESEARCH & DESIGN, 2010, 88 (12A) : 1599 - 1606
  • [4] A thermo-kinetic study on co-pyrolysis of oil shale and polyethylene terephthalate using TGA/FT-IR
    Ozsin, Gamzenur
    Kilic, Murat
    Apaydin-Varol, Esin
    Putun, Ayse Eren
    Putun, Ersan
    KOREAN JOURNAL OF CHEMICAL ENGINEERING, 2020, 37 (11) : 1888 - 1898
  • [5] Kinetic Study of Low Density Polyethylene Using Thermogravimetric Analysis, Part 2: Isothermal Study
    Kple, Melhyas
    Girods, Pierre
    Fagla, Benoit
    Anjorin, Malahimi
    Ziegler-Devin, Isabelle
    Rogaume, Yann
    WASTE AND BIOMASS VALORIZATION, 2017, 8 (03) : 707 - 719
  • [6] Investigation and prediction of co-pyrolysis between oily sludge and high-density polyethylene via in-situ DRIFTS, TGA, and artificial neural network
    Ai, Zejian
    Zhang, Weijin
    Yang, Lihong
    Chen, Hong
    Xu, Zhengyong
    Leng, Lijian
    Li, Hailong
    JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS, 2022, 166
  • [7] Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks
    Naqvi, Salman Raza
    Tariq, Rumaisa
    Hameed, Zeeshan
    Ali, Imtiaz
    Taqvi, Syed A.
    Naqvi, Muhammad
    Niazi, M. B. K.
    Noor, Tayyaba
    Farooq, Wasif
    FUEL, 2018, 233 : 529 - 538
  • [8] Kinetic study on the pyrolysis of low-density polyethylene (LDPE) waste using kaolin as catalyst
    Erawati, Emi
    Hamid
    Martenda, Danik
    26TH REGIONAL SYMPOSIUM ON CHEMICAL ENGINEERING (RSCE 2019), 2020, 778
  • [9] A modified DAEM: To study the bioenergy potential of invasive Staghorn Sumac through pyrolysis, ANN, TGA, kinetic modeling, FTIR and GC-MS analysis
    Ahmad, Muhammad Sajjad
    Liu, Hui
    Alhumade, Hesham
    Tahir, Muddasar Hussain
    Cakman, Gulce
    Yildiz, Agah
    Ceylan, Selim
    Elkamel, Ali
    Shen, Boxiong
    ENERGY CONVERSION AND MANAGEMENT, 2020, 221 (221)
  • [10] Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks
    Xiaoxiao Yin
    Junyu Tao
    Guanyi Chen
    Xilei Yao
    Pengpeng Luan
    Zhanjun Cheng
    Ning Li
    Zhongyue Zhou
    Beibei Yan
    Frontiers of Environmental Science & Engineering, 2023, 17