%I Elsevier Ltd
%V 10
%A T.L. Yap
%A A.C.M. Loy
%A B.L.F. Chin
%A J.Y. Lim
%A H. Alhamzi
%A Y.H. Chai
%A C.L. Yiin
%A K.W. Cheah
%A M.X.J. Wee
%A M.K. Lam
%A Z.A. Jawad
%A S. Yusup
%A S.S.M. Lock
%T Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses
%K Binary mixtures; Catalysts; Genetic algorithms; High density polyethylenes; Kinetic parameters; Lime; Neural networks; Pyrolysis; Thermogravimetric analysis; Zeolites, Artificial neuron networks; Bi-functional; Catalytic pyrolysis; Chlorella vulgaris; Copyrolysis; Empirical model; Kinetic analysis; Kinetics parameter; Microalga chlorellum vulgari; ]+ catalyst, Microalgae
%X The catalytic pyrolysis of Chlorella vulgaris, high-density polyethylene (Pure HDPE) and, their binary mixtures were conducted to analyse the kinetic and thermodynamic performances from 10 to 100 K/min. The kinetic parameters were computed by substituting the experimental and ANN predicted data into these iso-conversional equations and plotting linear plots. Among all the iso-conversional models, Flynn-Wall-Ozawa (FWO) model gave the best prediction for kinetic parameters with the lowest deviation error (2.28-12.76). The bifunctional HZSM-5/LS catalysts were found out to be the best catalysts among HZSM-5 zeolite, natural limestone (LS), and bifunctional HZSM-5/LS catalyst in co-pyrolysis of binary mixture of Chlorella vulgaris and HDPE, in which the Ea of the whole system was reduced from range 144.93-225.84 kJ/mol (without catalysts) to 75.37-76.90 kJ/mol. With the aid of artificial neuron network and genetic algorithm, an empirical model with a mean absolute percentage error (MAPE) of 51.59 was developed for tri-solid state degradation system. The developed empirical model is comparable to the thermogravimetry analysis (TGA) experimental values alongside the other empirical model proposed in literature © 2022 Elsevier Ltd.
%L scholars16714
%J Journal of Environmental Chemical Engineering
%O cited By 25
%N 3
%R 10.1016/j.jece.2022.107391
%D 2022