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Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Safarian, Sahar
dc.contributor.author Ebrahimi Saryazdi, Seyed Mohammad
dc.contributor.author Unnthorsson, Runar
dc.contributor.author Richter, Christiaan
dc.date.accessioned 2021-05-03T12:12:55Z
dc.date.available 2021-05-03T12:12:55Z
dc.date.issued 2021-05-01
dc.identifier.issn 2311-5637
dc.identifier.uri https://hdl.handle.net/20.500.11815/2564
dc.description.abstract In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogencontent are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen.
dc.description.sponsorship This paper was a part of the project funded by Icelandic Research Fund (IRF), (in Icelandic: Rannsoknasjodur) and the grant number is 196458-051.
dc.format.extent 71
dc.language.iso en
dc.publisher MDPI
dc.relation.ispartofseries Fermentation;7(2)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Biomass gasificatio
dc.subject Artificial neural network
dc.subject Hydrogen production
dc.subject Downdraft
dc.subject Simulation
dc.subject Gaskennd efni
dc.subject Hermilíkön
dc.subject Orkuframleiðsla
dc.subject Lífmassi
dc.title Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach
dc.type info:eu-repo/semantics/article
dcterms.license This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
dc.identifier.journal Fermentation
dc.identifier.doi 10.3390/fermentation7020071
dc.contributor.department Iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild (HÍ)
dc.contributor.department Faculty of Industrial Eng., Mechanical Eng. and Computer Science (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)

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