Air gasification of high-ash sewage sludge for hydrogen production: Experimental, sensitivity and predictive analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10248554" target="_blank" >RIV/61989100:27240/22:10248554 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85121370150&origin=resultslist&sort=plf-f" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85121370150&origin=resultslist&sort=plf-f</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ijhydene.2021.11.192" target="_blank" >10.1016/j.ijhydene.2021.11.192</a>
Alternative languages
Result language
angličtina
Original language name
Air gasification of high-ash sewage sludge for hydrogen production: Experimental, sensitivity and predictive analysis
Original language description
In this work, air gasification of sewage sludge was conducted in a lab-scale bubbling fluidized bed gasifier. Further, the gasification process was modeled using artificial neural networks for the product gas composition with varying temperatures and equivalence ratios. Neural network-based prediction will help to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The gasification efficiency and lower heating values were also established as a function of temperatures and equivalence ratios. The maximum H2 and CO was recorded as 16.26 vol% and 33.55 vol%. Intraileally at ER 0.2 gas composition H2, CO, and CH4 show high concentrations of 20.56 vol%, 45.91 vol%, and 13.32 vol%, respectively. At the same time, CO2 was lower as 20.20 vol% at ER 0.2. Therefore, optimum values are suggested for maximum H2 and CO yield and lower concentration of CO2 at ER 0.25 and temperature of 850 oC. A predictive model based on an Artificial Neural network is also developed to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The network has been trained with different topologies to find the optimal structure for temperature and equivalence ratio. The obtained results showed that the regression coefficients for training, validation, and testing are 0.99999, 0.99998, and 0.99992, respectively, which clearly identifies the training efficiency of the trained model. (C) 2021 Hydrogen Energy Publications LLC
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20704 - Energy and fuels
Result continuities
Project
<a href="/en/project/LTI19002" target="_blank" >LTI19002: The involvement of Czech research organizations in the Energy Research Alliance EERA</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Hydrogen Energy
ISSN
0360-3199
e-ISSN
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Volume of the periodical
2021
Issue of the periodical within the volume
12
Country of publishing house
GB - UNITED KINGDOM
Number of pages
20
Pages from-to
nestrankovano
UT code for WoS article
000889311300016
EID of the result in the Scopus database
2-s2.0-85121370150