TY  - JOUR
AV  - none
TI  - Synergistic effects of catalytic co-pyrolysis of corn cob and HDPE waste mixtures using weight average global process model
SP  - 948
N1  - cited By 61
SN  - 09601481
PB  - Elsevier Ltd
EP  - 963
KW  - Activation energy; Animals; Catalysts; High density polyethylenes; Neural networks; Pyrolysis; Thermogravimetric analysis
KW  -  Catalytic pyrolysis; Egg-shell catalysts; Global Process Model; High density polyethylene(HDPE); Joint optimization; Reaction mechanism; Synergistic effect; Temperature range
KW  -  Binary mixtures
KW  -  artificial neural network; catalysis; catalyst; degradation; eggshell; heating; maize; optimization; pyrolysis; synergism; thermogravimetry
KW  -  Gallus gallus
ID  - scholars14897
N2  - Synergistic effects and kinetic parameters for binary mixtures of corn cob and high-density polyethylene (HDPE) in co-pyrolysis with the presence of renewable chicken and duck eggshell catalyst are evaluated using thermogravimetric analysis (TGA) approach at various heating rates (10â??200 K/min) in temperature range of 323â??1173 K. Weight average global process model based on two-stage kinetic scheme are employed in this study. The reaction mechanisms involved in the co-pyrolysis process are 1-D diffusion for second stage of thermal degradation and 3-D diffusion for third stage of the thermal degradation. The difference in the experimental and estimated values of the catalytic corn cob and HDPE mixtures in terms of weight loss indicates the existence of the synergistic effects during the pyrolysis process. The values of the activation energy for pure corn cob, pure HDPE, binary mixtures of corn cob and HDPE are reported in the range of 43.61â??83.03, 412.32â??510.72, and 28.98â??93.18 kJ/mol, respectively. Meanwhile, the activation energy for catalytic pyrolysis process are in the range of 28.98â??113.17 and 23.65â??119.50 kJ/mol respectively in the presence of chicken and duck eggshells as catalyst. Additionally, artificial neural network (ANN) and joint optimization modelling are also utilized to validate and optimize the results from the TGA. © 2021 Elsevier Ltd
Y1  - 2021///
VL  - 170
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100908247&doi=10.1016%2fj.renene.2021.02.053&partnerID=40&md5=0ad8aeed84630ff165e8372065ca51d0
A1  - Liew, J.X.
A1  - Loy, A.C.M.
A1  - Chin, B.L.F.
A1  - AlNouss, A.
A1  - Shahbaz, M.
A1  - Al-Ansari, T.
A1  - Govindan, R.
A1  - Chai, Y.H.
JF  - Renewable Energy
ER  -