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

Simple item page
dc.citedby16
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorAlsaffar M.A.en_US
dc.contributor.authorCheng C.K.en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid36651549700en_US
dc.contributor.authorid57210601717en_US
dc.contributor.authorid57204938666en_US
dc.date.accessioned2023-05-29T07:23:50Z
dc.date.available2023-05-29T07:23:50Z
dc.date.issued2019
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo738
dc.identifier.doi10.3390/catal9090738
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85073318624
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073318624&doi=10.3390%2fcatal9090738&partnerID=40&md5=7c677ecca4468d058410e5fa830afd11
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24476
dc.identifier.volume9
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleCatalysts
dc.titleArtificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reformingen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS