Modelling of the performance of industrial HDS reactors using a hybrid neural network approach

Bellos, GD; Kallinikos, LE; Gounaris, CE; Papayannakos, NG

dc.contributor.author	Bellos, GD	en
dc.contributor.author	Kallinikos, LE	en
dc.contributor.author	Gounaris, CE	en
dc.contributor.author	Papayannakos, NG	en
dc.date.accessioned	2014-03-01T01:22:46Z
dc.date.available	2014-03-01T01:22:46Z
dc.date.issued	2005	en
dc.identifier.issn	0255-2701	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16649
dc.subject	Catalyst deactivation	en
dc.subject	HDS kinetics	en
dc.subject	Industrial reactor simulation	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Catalyst activity	en
dc.subject.other	Catalyst deactivation	en
dc.subject.other	Computer simulation	en
dc.subject.other	Desulfurization	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Reaction kinetics	en
dc.subject.other	Feed quality	en
dc.subject.other	Hybrid neural networks	en
dc.subject.other	Hydrodesulfurization	en
dc.subject.other	Industrial HDS reactors	en
dc.subject.other	Chemical reactors	en
dc.subject.other	neural network	en
dc.title	Modelling of the performance of industrial HDS reactors using a hybrid neural network approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cep.2004.06.008	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cep.2004.06.008	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	A hybrid neural network model is presented for the simulation of the performance of industrial HDS reactors. This model can be used in estimating the catalyst deactivation rate and the impact of feed quality on catalyst activity. A deterministic mathematical code simulating the reactor performance for hydrodesulphurization and hydrogen consumption reactions was used. The deterministic code was coupled with a neural network used to correlate the evaluated kinetic parameters from the industrial data with feed quality and catalyst life time. The neural network is also used to predict the kinetic parameters needed for simulation from the feed quality and the catalyst time on stream. A part of the necessary kinetic parameters were obtained from kinetic experiments performed with the industrial catalyst and with representative feeds in a small scale reactor. (C) 2004 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Chemical Engineering and Processing: Process Intensification	en
dc.identifier.doi	10.1016/j.cep.2004.06.008	en
dc.identifier.isi	ISI:000227513700001	en
dc.identifier.volume	44	en
dc.identifier.issue	5	en
dc.identifier.spage	505	en
dc.identifier.epage	515	en