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Using Evolving Fuzzy Models to predict Crude Oil Distillation Side Streams

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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, 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 work is to report an application and a study of a novel technique for real-time modelling, namely eXtended Evolving Fuzzy Takagi-Sugeno models (xTS) for prediction and online monitoring of these properties of the refinery distillation process. The results include the online prediction of Soft Sensors for distillation of Naptha and Gasoil Side Streams. The application predicts the quality of the side stream evolving its fuzzy structure and cluster parameters.