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Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst

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 behavi...

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Main Authors: Bong, J.T., Loy, A.C.M., Chin, B.L.F., Lam, M.K., Tang, D.K.H., Lim, H.Y., Chai, Y.H., Yusup, S.
Format: Article
Published: Elsevier Ltd 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087697379&doi=10.1016%2fj.energy.2020.118289&partnerID=40&md5=fcf1b892dbc7e22e8ad7748a6fa11ca0
http://eprints.utp.edu.my/29974/
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spelling my.utp.eprints.299742022-03-25T03:17:13Z Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst Bong, J.T. Loy, A.C.M. Chin, B.L.F. Lam, M.K. Tang, D.K.H. Lim, H.Y. Chai, Y.H. Yusup, S. 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 Elsevier Ltd 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087697379&doi=10.1016%2fj.energy.2020.118289&partnerID=40&md5=fcf1b892dbc7e22e8ad7748a6fa11ca0 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 . http://eprints.utp.edu.my/29974/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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
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author Bong, J.T.
Loy, A.C.M.
Chin, B.L.F.
Lam, M.K.
Tang, D.K.H.
Lim, H.Y.
Chai, Y.H.
Yusup, S.
spellingShingle Bong, J.T.
Loy, A.C.M.
Chin, B.L.F.
Lam, M.K.
Tang, D.K.H.
Lim, H.Y.
Chai, Y.H.
Yusup, S.
Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst
author_facet Bong, J.T.
Loy, A.C.M.
Chin, B.L.F.
Lam, M.K.
Tang, D.K.H.
Lim, H.Y.
Chai, Y.H.
Yusup, S.
author_sort Bong, J.T.
title Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst
title_short Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst
title_full Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst
title_fullStr Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst
title_full_unstemmed Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst
title_sort artificial neural network approach for co-pyrolysis of chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst
publisher Elsevier Ltd
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087697379&doi=10.1016%2fj.energy.2020.118289&partnerID=40&md5=fcf1b892dbc7e22e8ad7748a6fa11ca0
http://eprints.utp.edu.my/29974/
_version_ 1738657041890672640
score 13.214268

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