2020
DOI: 10.1088/1757-899x/991/1/012078
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Elucidating the non-linear effect of process parameters on hydrogen production by catalytic methane reforming: an artificial intelligence approach

Abstract: This study focuses on the non-linear effect of gas hourly space velocity (GHSV), oxygen (O2) concentration in the feed, the reaction temperature, and the CH4/CO2 ratio on hydrogen production by catalytic methane dry reforming using artificial neural networks (ANN). Ten different ANN models were configured by varying the hidden neurons from 1 to 10. The various ANN model architecture was tested using 30 datasets. The ANN model with the topology of 4-9-2 resulted in the best performance with the sum of square er… Show more

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Cited by 3 publications
(1 citation statement)
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“…From the results, the artificial neural network can be easily applied to analyze the performance of the entire hydrogen plant to achieve suitable operating conditions, with less time-consuming and high accuracy [7]. Recently, hydrogen production by catalytic dry reforming of methane through the use of artificial neural networks (ANN) has been reported by Alsaffar et al [8]. They focused on the optimization of gas hourly space velocity (GHSV), the oxygen (O 2 ) concentration in the feed, the reaction temperature and the CH 4 /CO 2 ratio.…”
Section: Introductionmentioning
confidence: 99%
“…From the results, the artificial neural network can be easily applied to analyze the performance of the entire hydrogen plant to achieve suitable operating conditions, with less time-consuming and high accuracy [7]. Recently, hydrogen production by catalytic dry reforming of methane through the use of artificial neural networks (ANN) has been reported by Alsaffar et al [8]. They focused on the optimization of gas hourly space velocity (GHSV), the oxygen (O 2 ) concentration in the feed, the reaction temperature and the CH 4 /CO 2 ratio.…”
Section: Introductionmentioning
confidence: 99%