eprintid: 12745
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/27/45
datestamp: 2023-11-10 03:27:18
lastmod: 2023-11-10 03:27:18
status_changed: 2023-11-10 01:49:25
type: article
metadata_visibility: show
creators_name: Bong, J.T.
creators_name: Loy, A.C.M.
creators_name: Chin, B.L.F.
creators_name: Lam, M.K.
creators_name: Tang, D.K.H.
creators_name: Lim, H.Y.
creators_name: Chai, Y.H.
creators_name: Yusup, S.
title: Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst
ispublished: pub
keywords: Activation energy; Algae; Catalysts; Heating rate; Kinetics; Microorganisms; Neural networks; Oilseeds; Polymer blends; Pyrolysis, Activation energies (Ea); Artificial neural network approach; Catalytic pyrolysis; Chlorella vulgaris; Degradation behaviours; Flynn-Wall-Ozawa; Kinetic and thermodynamic parameters; Pyrolysis process, Binary mixtures, activation energy; artificial neural network; ash; catalyst; crop residue; green alga; heating; microalga; pyrolysis; reaction kinetics; thermodynamics, Arachis hypogaea; Chlorella vulgaris
note: cited By 64
abstract: The catalytic pyrolysis of pure microalgae (M), peanut shell wastes (PS) and their binary mixtures were analysed by introducing the microalgae ash (MA) as a catalyst. The pyrolysis processes were conducted at different heating rates from 10 K/min-100 K/min to observe their thermal degradation behaviour. Additionally, Artificial Neural Network (ANN) was applied by feeding the heating rates and temperatures to predict the weight loss of the samples. The kinetic and thermodynamic parameters were also determined through three different iso-conversional kinetic models: Friedman (FR), Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO). Based on the kinetic results, FWO model achieved the lowest deviation between the activation energies (Ea) from the experimental which aligned with the ANN predicted results. The finding also shows that the activation energy (Ea) of the catalytic pyrolysis of binary mixtures was lower than the pure M and PS (Experimental: 142.56 kJ/mol; ANN forecast: 131.37 kJ/mol). © 2020 Elsevier Ltd
date: 2020
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087697379&doi=10.1016%2fj.energy.2020.118289&partnerID=40&md5=fcf1b892dbc7e22e8ad7748a6fa11ca0
id_number: 10.1016/j.energy.2020.118289
full_text_status: none
publication: Energy
volume: 207
refereed: TRUE
issn: 03605442
citation:   Bong, J.T. and Loy, A.C.M. and Chin, B.L.F. and Lam, M.K. and Tang, D.K.H. and Lim, H.Y. and Chai, Y.H. and Yusup, S.  (2020) Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst.  Energy, 207.   ISSN 03605442