NDLI: Aplicação de redes neurais artificiais e de quimiometria na modelagem do processo de craqueamento catalitico fluido

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Aplicação de redes neurais artificiais e de quimiometria na modelagem do processo de craqueamento catalitico fluido

Content Provider	Semantic Scholar
Author	Pimentel, Wagner Roberto De Oliveira Lisbôa, Antonio Carlos Luz
Copyright Year	2005
Abstract	The fluidized bed catalytic cracking process is one of the most important refining processes. It produces, among other distillates, gasoline and liquefied petroleum gas (LPG). It is very difficult to model it by fundamental balances. On the other band, artificial neural networks (ANN) offer conveuient tools to describe complex processes. They are able to learn what is going on with in the process through a limited amount of information, requiring less computing time than phenomenological modeling. The main objective of this work was to develop empírica! models based on ANNs and chemometrics able to relate input and output variables of the FCC process, using data from a pilot and from an industrial plant. Experimental data were obtained from the Petrobras FCC pilot plant located in São Mateus do Sul, Paraná, and from the Petrobras Landulpho Alves Refinery FCC industrial plant located in São Francisco do Conde, Bahia. The principal component analysis (PCA) teclmique was initially used to preprocess the data. Artificial neural networu were then employed with the following supervising training algorithms: BroydenFletcher-Godfarb-Shanno (BFGS), Scaled Conjugated Gradient (SCG) and LevenbergMarquardt (LM). Methods devised to increase the artificial network prediction power were also used. Good resulta were obtained for the pilot and industrial plant with the LM algorithm, using Bayesian regularization, with the model that uses the scores produced by the PCA as inputs (also with LM using regularization). The developed empirical models may be used to optimize FCC risers, for they showed excellent prediction capacity.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://repositorio.unicamp.br/jspui/bitstream/REPOSIP/267304/1/Pimentel_WagnerRobertodeOliveira_D.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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