prikaz prve stranice dokumenta Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network
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Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network
Cleaner Engineering and Technology, 1 (2020); 100029. https://doi.org/10.1016/j.clet.2020.100029
Cerinski, Damijan; Baleta, Jakov; Mikulčić, Hrvoje; Mikulandrić, Robert; Wang, Jin

Citirajte ovaj rad

Cerinski, D., Baleta, J., Mikulčić, H., Mikulandrić, R. i Wang, J. (2020). Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network. Cleaner Engineering and Technology, 1.. doi: 10.1016/j.clet.2020.100029

Cerinski, Damijan, et al. "Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network." Cleaner Engineering and Technology, vol. 1, 2020. https://doi.org/10.1016/j.clet.2020.100029

Cerinski, Damijan, Jakov Baleta, Hrvoje Mikulčić, Robert Mikulandrić i Jin Wang. "Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network." Cleaner Engineering and Technology 1 (2020). https://doi.org/10.1016/j.clet.2020.100029

Cerinski, D., et al. (2020) 'Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network', Cleaner Engineering and Technology, 1. doi: 10.1016/j.clet.2020.100029

Cerinski D, Baleta J, Mikulčić H, Mikulandrić R, Wang J. Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network. Cleaner Engineering and Technology [Internet]. 12.2020. [pristupljeno 20.06.2024.];1. doi: 10.1016/j.clet.2020.100029

D. Cerinski, J. Baleta, H. Mikulčić, R. Mikulandrić i J. Wang, "Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network", Cleaner Engineering and Technology, vol. 1, Prosinac 2020. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:115:893992. [Citirano: 20.06.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:115:893992
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Podaci o radu
Naslov (engleski)Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network
AutorDamijan Cerinski
AutorJakov Baleta
AutorHrvoje Mikulčić
AutorRobert Mikulandrić
AutorJin Wang
Autorova ustanovaSveučilište u Zagrebu
Fakultet strojarstva i brodogradnje
(Zavod za energetska postrojenja, energetiku i okoliš)
(Katedra za energetska postrojenja i energetiku)
Autorova ustanovaSveučilište u Zagrebu
Metalurški fakultet
(Zavod za mehaničku metalurgiju)
(Laboratorij za toplotehniku i strojarstvo)
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Strojarstvo
Sažetak (engleski)
Biomass gasification enables the transformation of biomass feedstock into syngas suitable for further energy conversions. Mathematical models of gasification are not only valuable tool for the design and optimization of the processes, but could be also employed for online prediction and process control. In this work, the potential of using nonlinear autoregressive networks with exogenous inputs (NARX) for predicting the gasification process when a lower amount of experimental data is available was studied. The analysis of using an open-loop NARX network for an online prediction of the syngas composition in a downdraft gasifier at different data recording frequency was performed. The predicted results showed that by decreasing the data recording frequency, the prediction error increases. Furthermore, the possibility of improving the NARX network at lower data recording frequency was analysed by expanding the training dataset to reduce discrepancies between predicted and measured results. Inclusion of four data sets made neural network more robust and flexible for operating with fewer data points. Practical significance of results can be seen in the application of open-loop network for online prediction and control of the gasification process at lower data recording frequency as a part of a model predictive control module.
Ključne riječi (hrvatski)
Jezikhrvatski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaCleaner Engineering and Technology
Brojčani podacivol. 1, 100029
p-ISSN2666-7908
DOIhttps://doi.org/10.1016/j.clet.2020.100029
URN:NBNurn:nbn:hr:115:893992
Datum objave publikacije2020-12
URL dokumentahttps://linkinghub.elsevier.com/retrieve/pii/S266679082030029X
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje-Nekomercijalno-Bez prerada 4.0 međunarodna (CC BY-NC-ND 4.0)
Datum i vrijeme pohrane2024-05-03 16:38:58