The paper deals with the design of neural based soft sensors to improve product qualitymonitoring and control in a refinery by estimating the stabilized gasoline concentration (C5) in the top flow and the butane (C4) concentration in the bottom flow of a debutanizer column, on the basis of a set of available measurements. Three-step predictive dynamic neural models were implemented in order to evaluate in real time the top and bottom product concentrations in the column. The soft sensors designed overcome the great time delayintroduced bythe corresponding gas chromatograph, giving on-line estimations that are suitable for monitoring and control purposes.

SOFT SENSORS FOR PRODUCT QUALITY MONITORING IN DEBUTANIZER DISTILLATION COLUMNS

XIBILIA, Maria Gabriella;
2005-01-01

Abstract

The paper deals with the design of neural based soft sensors to improve product qualitymonitoring and control in a refinery by estimating the stabilized gasoline concentration (C5) in the top flow and the butane (C4) concentration in the bottom flow of a debutanizer column, on the basis of a set of available measurements. Three-step predictive dynamic neural models were implemented in order to evaluate in real time the top and bottom product concentrations in the column. The soft sensors designed overcome the great time delayintroduced bythe corresponding gas chromatograph, giving on-line estimations that are suitable for monitoring and control purposes.
2005
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/1419820
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 294
  • ???jsp.display-item.citation.isi??? 238
social impact