TY - GEN

T1 - Architectures of evolving fuzzy rule-based classifiers

AU - Angelov, Plamen

AU - Zhou, Xiaowei

AU - Filev, Dimitar

AU - Lughofer, Edwin

N1 - "©2007 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." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

PY - 2007/10/9

Y1 - 2007/10/9

N2 - 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.

AB - 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.

U2 - 10.1109/ICSMC.2007.4413728

DO - 10.1109/ICSMC.2007.4413728

M3 - Conference contribution/Paper

SN - 978-1-4244-0991-4

SP - 2050

EP - 2055

BT - Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on

PB - IEEE

T2 - 2007 IEEE International Conference on Systems, Man, and Cybernetics

Y2 - 7 October 2007 through 10 October 2007

ER -