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Soft sensor for predicting crude oil distillation side streams using Takagi Sugeno evolving fuzzy models

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date9/10/2007
Host publicationSystems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on
PublisherIEEE
Pages3305-3310
Number of pages6
ISBN (Electronic)978-1-4244-0991-4
ISBN (Print)978-1-4244-0991-4
Original languageEnglish

Conference

ConferenceConference
CityMontreal, Canada
Period7/10/0710/10/07

Conference

ConferenceConference
CityMontreal, Canada
Period7/10/0710/10/07

Abstract

Prediction of the properties of the crude oil distillation side streams based on statistical methods and laboratory-based analysis has been around for decades. However, there are still many problems with the existing estimators that require a development of new techniques especially for an on-line analysis of the quality of the distillation process. The nature of non-linear characteristics of the refinery process, the variety of properties to measure and control and the narrow window that normally refinery processes operates in are only some of the problems that a prediction technique should deal with in order to be useful for a practical application. There are many successful application cases that refinery units use real plant data to calibrate models. They can be used to predict quality properties of the gas oil, naphtha, kerosene and other products of a crude oil distillation tower. Some of these are distillation end points and cold properties (freeze, cloud). However, it is difficult to identify, control or compensate the dynamic process behaviour and the errors from instrumentation for an online model prediction. (c) IEEE Press