A Method for Predicting Quality of the Crude Oil Distillation

Abstract

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