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/ http://umpir.ump.edu.my/id/eprint/26852/ http://umpir.ump.edu.my/id/eprint/26852/ http://umpir.ump.edu.my/id/eprint/26852/1/Artificial%20intelligence%20modelling%20approach%20for%20the%20prediction%20of%20CO.pdf

id	ump-26852
recordtype	eprints
spelling	ump-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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
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/ http://umpir.ump.edu.my/id/eprint/26852/ http://umpir.ump.edu.my/id/eprint/26852/ http://umpir.ump.edu.my/id/eprint/26852/1/Artificial%20intelligence%20modelling%20approach%20for%20the%20prediction%20of%20CO.pdf
first_indexed	2023-09-18T22:42:04Z
last_indexed	2023-09-18T22:42:04Z
_version_	1777416981080702976

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

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