A Novel Approach to the Event Prediction and Mitigation Problem in an Ethylene Plant

Principle Component Analysis (PCA) and similar techniques have been used to detect abnormal situations in process industries for well over a decade.  PCA calculates a multidimensional model of a process under normal conditions, and then uses statistical methods to determine the likelihood that current operation is similar to the model.  Understanding the physical sense of the output is essential for aiding the operator to understand causes for deviations, but has proven to be very difficult with PCA. Geometric Process Modeling is a very different approach.  This method requires little to no math or statistics for building models or interpreting the results and has proven quite sensitive in detecting deviations from normal operation. Further, it directly displays key information aiding the panel operator in understanding the situation and targeting remedial action.  As such, it is ideally suited for the Event Prediction and Mitigation (EPM) problem. This paper will present the application of the method to ethylene plant equipment including a ethane cracking furnace and a propylene refrigeration machine.