Modelling of an industrial fluid catalytic cracking unit using neural networks

Michalopoulos, J; Papadokonstadakis, S; Arampatzis, G; Lygeros, A

dc.contributor.author	Michalopoulos, J	en
dc.contributor.author	Papadokonstadakis, S	en
dc.contributor.author	Arampatzis, G	en
dc.contributor.author	Lygeros, A	en
dc.date.accessioned	2014-03-01T01:16:46Z
dc.date.available	2014-03-01T01:16:46Z
dc.date.issued	2001	en
dc.identifier.issn	0263-8762	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14205
dc.subject	fluid catalytic cracking	en
dc.subject	process modelling	en
dc.subject	neural networks	en
dc.subject	multi-layer perceptron	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	IDENTIFICATION	en
dc.subject.other	REACTORS	en
dc.title	Modelling of an industrial fluid catalytic cracking unit using neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1205/02638760151095944	en
heal.identifier.secondary	http://dx.doi.org/10.1205/02638760151095944	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	An artificial neural network (ANN) model for determining the steady-state behaviour of an industrial Fluid Catalytic Cracking (FCC) unit is presented in this paper. Industrial data from a Creek petroleum refinery were used to develop, train and check the model. FCC is one of the most important oil refinery processes. Due to its complexity the modelling of the FCC poses a great challenge. The proposed model is capable of predicting the volume percent of conversion based on six input variables. This work is focused on determining the optimum architecture of the ANN, in order to gain good generalization properties. The results show that the ANN is able to accurately predict the measured data. The prediction errors in both training and validation data sets are almost the same, indicating the capabilities of the model to accurately generalize when presented with unseen data. The neural model developed is also compared to an existing non-linear statistical model. The comparison shows that the neural model is superior to the statistical model.	en
heal.publisher	INST CHEMICAL ENGINEERS	en
heal.journalName	CHEMICAL ENGINEERING RESEARCH & DESIGN	en
dc.identifier.doi	10.1205/02638760151095944	en
dc.identifier.isi	ISI:000168482200004	en
dc.identifier.volume	79	en
dc.identifier.issue	A2	en
dc.identifier.spage	137	en
dc.identifier.epage	142	en