eprintid: 16714
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/67/14
datestamp: 2023-12-19 03:23:14
lastmod: 2023-12-19 03:23:14
status_changed: 2023-12-19 03:06:45
type: article
metadata_visibility: show
creators_name: Yap, T.L.
creators_name: Loy, A.C.M.
creators_name: Chin, B.L.F.
creators_name: Lim, J.Y.
creators_name: Alhamzi, H.
creators_name: Chai, Y.H.
creators_name: Yiin, C.L.
creators_name: Cheah, K.W.
creators_name: Wee, M.X.J.
creators_name: Lam, M.K.
creators_name: Jawad, Z.A.
creators_name: Yusup, S.
creators_name: Lock, S.S.M.
title: Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses
ispublished: pub
keywords: 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
note: cited By 25
abstract: 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.
date: 2022
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125755055&doi=10.1016%2fj.jece.2022.107391&partnerID=40&md5=a80dbff94aba18ebee678cde67296944
id_number: 10.1016/j.jece.2022.107391
full_text_status: none
publication: Journal of Environmental Chemical Engineering
volume: 10
number: 3
refereed: TRUE
issn: 22133437
citation:   Yap, T.L. and Loy, A.C.M. and Chin, B.L.F. and Lim, J.Y. and Alhamzi, H. and Chai, Y.H. and Yiin, C.L. and Cheah, K.W. and Wee, M.X.J. and Lam, M.K. and Jawad, Z.A. and Yusup, S. and Lock, S.S.M.  (2022) Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses.  Journal of Environmental Chemical Engineering, 10 (3).   ISSN 22133437