2020
DOI: 10.1016/j.conengprac.2020.104631
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Experimental implementation of a new control approach using an inverse neural network to on-demand hydrogen production

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Cited by 8 publications
(6 citation statements)
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“…To achieve this, the methodology proposed in [14] is applied. The inverse artificial neural network (ANNi) is a variant of the ANN that has been implemented in several processes to determine the optimal parameters successfully [15]. The inputs of the data base used to train the ANN were constructed based on a DOE, whereas the output, MVMS, was calculated by the means of FEM.…”
Section: Methodsmentioning
confidence: 99%
“…To achieve this, the methodology proposed in [14] is applied. The inverse artificial neural network (ANNi) is a variant of the ANN that has been implemented in several processes to determine the optimal parameters successfully [15]. The inputs of the data base used to train the ANN were constructed based on a DOE, whereas the output, MVMS, was calculated by the means of FEM.…”
Section: Methodsmentioning
confidence: 99%
“…The energy crisis has been worsened by the use of non‐renewable energy sources such as coal, oil, and natural gas. These sources are limited and are being depleted rapidly [18–20] . Additionally, the generation and utilization of non‐renewable energy sources have a notable environmental impact, including pollution of air and water and the emission of greenhouse gases, which cause climate change [21] .…”
Section: Introductionmentioning
confidence: 99%
“…Hydrogen (H 2 ) is viewed as the fuel of the future, and in recent years, there has been a lot of interest in using photocatalytic water splitting to produce hydrogen, as it allows for the use of renewable resources like sunlight and water [33] . The production of photocatalytically hydrogen (H 2 ) has garnered significant attention due to its environmentally friendly nature, simple processing, ease of production and low‐cost procedure [18] . H 2 has the potential to serve as a renewable and green energy source, addressing energy‐related challenges [17] .…”
Section: Introductionmentioning
confidence: 99%
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