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Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit

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dc.contributor.author Bollas, GM en
dc.contributor.author Papadokonstadakis, S en
dc.contributor.author Michalopoulos, J en
dc.contributor.author Arampatzis, G en
dc.contributor.author Lappas, AA en
dc.contributor.author Vasalos, IA en
dc.contributor.author Lygeros, A en
dc.date.accessioned 2014-03-01T01:19:41Z
dc.date.available 2014-03-01T01:19:41Z
dc.date.issued 2003 en
dc.identifier.issn 0255-2701 en
dc.identifier.uri http://hdl.handle.net/123456789/15658
dc.subject FCC kinetics en
dc.subject Fluid catalytic cracking en
dc.subject Hybrid modeling en
dc.subject Neural networks en
dc.subject Pilot to commercial unit scale up en
dc.subject Process modeling en
dc.subject Riser hydrodynamics en
dc.subject.classification Energy & Fuels en
dc.subject.classification Engineering, Chemical en
dc.subject.other Mathematical models en
dc.subject.other Neural networks en
dc.subject.other Petroleum refineries en
dc.subject.other Pilot plants en
dc.subject.other Oil refinery en
dc.subject.other Fluid catalytic cracking en
dc.subject.other coke en
dc.subject.other catalytic cracking en
dc.subject.other hydrodynamics en
dc.subject.other industrial plant en
dc.subject.other kinetics en
dc.subject.other neural network en
dc.subject.other pilot plant en
dc.subject.other artificial neural network en
dc.subject.other fluid catalytic cracking en
dc.subject.other Greece en
dc.subject.other model en
dc.subject.other oil industry en
dc.subject.other petrochemical industry en
dc.subject.other scale up en
dc.title Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit en
heal.type journalArticle en
heal.identifier.primary 10.1016/S0255-2701(02)00206-4 en
heal.identifier.secondary http://dx.doi.org/10.1016/S0255-2701(02)00206-4 en
heal.language English en
heal.publicationDate 2003 en
heal.abstract The scaling up of a pilot plant fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze. (C) 2003 Elsevier Science 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/S0255-2701(02)00206-4 en
dc.identifier.isi ISI:000183738500010 en
dc.identifier.volume 42 en
dc.identifier.issue 8-9 en
dc.identifier.spage 697 en
dc.identifier.epage 713 en


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