Artificial Neural Networks for Predicting Hydrogen Production in Catalytic Dry Reforming: A Systematic Review
نویسندگان
چکیده
Dry reforming of hydrocarbons, alcohols, and biological compounds is one the most promising effective avenues to increase hydrogen (H2) production. Catalytic dry used facilitate process. The popular catalysts for are Ni-based catalysts. Due their inactivation at high temperatures, these need use metal supports, which have received special attention from researchers in recent years. existence a wide range supports accurate detection higher H2 production, this study, systematic review meta-analysis using ANNs were conducted assess production by various Scopus, Embase, Web Science databases investigated retrieve related articles 1 January 2000 until 20 2021. Forty-seven containing 100 studies included. To determine optimal models three target factors (hydrocarbon conversion, yield, stability test time), artificial neural networks (ANNs) combined with differential evolution (DE) applied. best obtained had an average relative error testing data 0.52% 3.36% stability, 0.03% yield. These small differences between experimental results predictions indicate good generalization capability.
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ژورنال
عنوان ژورنال: Energies
سال: 2021
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en14102894