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Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming

This study investigates the applicability of the Leven–Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3...

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Main Authors: Ayodele, Bamidele V., Siti Indati, Mustapa, Alsaffar, May Ali, Cheng, C. K.
Format: Article
Language:English
Published: MDPI 2019
Subjects:
TP Chemical technology
Online Access:http://umpir.ump.edu.my/id/eprint/26852/1/Artificial%20intelligence%20modelling%20approach%20for%20the%20prediction%20of%20CO.pdf
http://umpir.ump.edu.my/id/eprint/26852/
https://doi.org/10.3390/catal9090738
https://doi.org/10.3390/catal9090738
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spelling my.ump.umpir.268522020-03-19T07:02:03Z http://umpir.ump.edu.my/id/eprint/26852/ Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming Ayodele, Bamidele V. Siti Indati, Mustapa Alsaffar, May Ali Cheng, C. K. TP Chemical technology This study investigates the applicability of the Leven–Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH4 partial pressure, CO2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven–Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H2 production were strongly correlated with the observed values. MDPI 2019-08-31 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/26852/1/Artificial%20intelligence%20modelling%20approach%20for%20the%20prediction%20of%20CO.pdf Ayodele, Bamidele V. and Siti Indati, Mustapa and Alsaffar, May Ali and Cheng, C. K. (2019) Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming. Catalysts, 9 (9). pp. 1-20. ISSN 2073-4344 https://doi.org/10.3390/catal9090738 https://doi.org/10.3390/catal9090738
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TP Chemical technology
spellingShingle TP Chemical technology
Ayodele, Bamidele V.
Siti Indati, Mustapa
Alsaffar, May Ali
Cheng, C. K.
Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming
description This study investigates the applicability of the Leven–Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH4 partial pressure, CO2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven–Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H2 production were strongly correlated with the observed values.
format Article
author Ayodele, Bamidele V.
Siti Indati, Mustapa
Alsaffar, May Ali
Cheng, C. K.
author_facet Ayodele, Bamidele V.
Siti Indati, Mustapa
Alsaffar, May Ali
Cheng, C. K.
author_sort Ayodele, Bamidele V.
title Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming
title_short Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming
title_full Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming
title_fullStr Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming
title_full_unstemmed Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming
title_sort artificial intelligence modelling approach for the prediction of co-rich hydrogen production rate from methane dry reforming
publisher MDPI
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/26852/1/Artificial%20intelligence%20modelling%20approach%20for%20the%20prediction%20of%20CO.pdf
http://umpir.ump.edu.my/id/eprint/26852/
https://doi.org/10.3390/catal9090738
https://doi.org/10.3390/catal9090738
_version_ 1662754743955488768
score 13.211869

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