Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach

Serrano García, Daniel; Golpour, Iman; Sánchez Delgado, Sergio

Publication:
Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach

dc.affiliation.dpto	UC3M. Departamento de Ingeniería Térmica y de Fluidos	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Ingeniería de Sistemas Energéticos	es
dc.contributor.author	Serrano García, Daniel
dc.contributor.author	Golpour, Iman
dc.contributor.author	Sánchez Delgado, Sergio
dc.date.accessioned	2021-04-26T07:40:56Z
dc.date.available	2022-04-15T23:00:04Z
dc.date.issued	2020-04-15
dc.description.abstract	The effect of different bed materials was included a as new input into an artificial neural network model to predict the gas composition (CO2, CO, CH4 and H2) and gas yield of a biomass gasification process in a bubbling fluidized bed. Feed and cascade forward back propagation networks with one and two hidden layers and with Levenberg-Marquardt and Bayesian Regulation learning algorithms were employed for the training of the networks. A high number of network topologies were simulated to determine the best configuration. It was observed that the developed models are able to predict the CO2, CO, CH4, H2 and gas yield with good accuracy (R2 > 0.94 and MSE < 1.7 × 10−3). The results obtained indicate that this approach is a powerful tool to help in the efficient design, operation and control of bubbling fluidized bed gasifiers working with different operating conditions, including the effect of the bed material.	en
dc.format.extent	6
dc.identifier.bibliographicCitation	Serrano, D., Golpour, I. & Sánchez-Delgado, S. (2020). Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach. Fuel, 266, 117021.	en
dc.identifier.doi	https://doi.org/10.1016/j.fuel.2020.117021
dc.identifier.issn	0016-2361
dc.identifier.publicationfirstpage	1
dc.identifier.publicationissue	117021
dc.identifier.publicationlastpage	6
dc.identifier.publicationtitle	Fuel	en
dc.identifier.publicationvolume	266
dc.identifier.uri	https://hdl.handle.net/10016/32475
dc.identifier.uxxi	AR/0000025675
dc.language.iso	eng
dc.publisher	Elsevier	en
dc.rights	© 2020 Elsevier Ltd.
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.eciencia	Energías Renovables	es
dc.subject.other	Artificial neural network	en
dc.subject.other	Bed material	en
dc.subject.other	Bubbling fluidized bed	en
dc.subject.other	Gasification	en
dc.title	Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach	en
dc.type	research article	*
dc.type.hasVersion	AM	*
dspace.entity.type	Publication