dc.contributorRodney Rezende Saldanha
dc.creatorBen Hur Salles Rodrigues
dc.date.accessioned2019-08-12T02:36:20Z
dc.date.accessioned2022-10-03T23:10:12Z
dc.date.available2019-08-12T02:36:20Z
dc.date.available2022-10-03T23:10:12Z
dc.date.created2019-08-12T02:36:20Z
dc.date.issued2014-12-09
dc.identifierhttp://hdl.handle.net/1843/BUBD-9VMFLB
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3817949
dc.description.abstractThe acquisition of a process analyzer to obtain, in real time, a products sulfur content may be expensive and its installation can take a while, due to the need of stop the processing unit. Are presented in this work, solutions for building soft sensors with the objective to infer the sulfur content in the product. To achieve that goal, multiple linear regression and artificial neural network techniques are used and their performance are compared in order to obtain the best configuration for constructing a sulfur content soft sensor.
dc.publisherUniversidade Federal de Minas Gerais
dc.publisherUFMG
dc.rightsAcesso Aberto
dc.subjectAnalisador virtual
dc.subjectRedes neurais artificiais
dc.subjectTeor de enxofre
dc.subjectRegressão linear múltipla
dc.subjectHidrotratamento
dc.titleMétodos de construção de analisadores virtuais para estimação de teor de enxofre de hidrocarbonetos
dc.typeMonografias de Especialização


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