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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2023
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| author | Ayodele B.V. Mustapa S.I. Alsaffar M.A. Cheng C.K. |
| author2 | 56862160400 |
| author_facet | 56862160400 Ayodele B.V. Mustapa S.I. Alsaffar M.A. Cheng C.K. |
| author_sort | Ayodele B.V. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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. � 2019 by the authors. Licensee MDPI, Basel, Switzerland. |
| format | Article |
| id | my.uniten.dspace-24476 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | dspace |
| spelling | my.uniten.dspace-244762023-05-29T15:23:50Z Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming Ayodele B.V. Mustapa S.I. Alsaffar M.A. Cheng C.K. 56862160400 36651549700 57210601717 57204938666 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. � 2019 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T07:23:50Z 2023-05-29T07:23:50Z 2019 Article 10.3390/catal9090738 2-s2.0-85073318624 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073318624&doi=10.3390%2fcatal9090738&partnerID=40&md5=7c677ecca4468d058410e5fa830afd11 https://irepository.uniten.edu.my/handle/123456789/24476 9 9 738 All Open Access, Gold MDPI Scopus |
| spellingShingle | Ayodele B.V. Mustapa S.I. Alsaffar M.A. Cheng C.K. Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming |
| title | 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_short | 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 |
| url_provider | http://dspace.uniten.edu.my/ |