TY - GEN

T1 - A Method for Predicting Quality of the Crude Oil Distillation

AU - Macias, J J

AU - Angelov, Plamen

AU - Xiaowei, Zhou

N1 - "©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE."

PY - 2006/9/8

Y1 - 2006/9/8

N2 - Prediction of the properties of the crude oil distillation sidestreams 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 operate 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 behavior and the errors from instrumentation for an online model prediction. The objective of this paper is to report an application and a study of a novel technique for real-time modeling, namely extended evolving fuzzy Takagi-Sugeno models (exTS) for prediction and online monitoring of these properties of the refinery distillation process. The results illustrate the effectiveness of the proposed technique and it's potential. The limitations and future directions of research are also outlined (c) IEEE Press

AB - Prediction of the properties of the crude oil distillation sidestreams 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 operate 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 behavior and the errors from instrumentation for an online model prediction. The objective of this paper is to report an application and a study of a novel technique for real-time modeling, namely extended evolving fuzzy Takagi-Sugeno models (exTS) for prediction and online monitoring of these properties of the refinery distillation process. The results illustrate the effectiveness of the proposed technique and it's potential. The limitations and future directions of research are also outlined (c) IEEE Press

U2 - 10.1109/ISEFS.2006.251167

DO - 10.1109/ISEFS.2006.251167

M3 - Conference contribution/Paper

SN - 0-7803-9719-3

SP - 214

EP - 220

BT - Evolving Fuzzy Systems, 2006 International Symposium on

PB - IEEE

T2 - 2006 IEEE Symposium on Evolving Fuzzy Systems

Y2 - 7 September 2006 through 9 September 2006

ER -