The Crude Preheat Train (CPT) is a set of large heat exchangers which recover the waste heat from product streams back to preheat the crude oil. The overall heat transfer coefficient in these heat exchangers may be significantly reduced due to fouling. One of the major impacts of fouling in CPT operation is the reduced heat transfer efficiency. The objective of this paper is to develop a predictive model using statistical methods which can a priori predict the rate of the fouling and the decrease in heat transfer efficiency in a heat exchanger in a crude preheat train. This predictive model will then be integrated into a preventive maintenance diagnostic tool to plan the cleaning of the heat exchanger to remove the fouling and bring back the heat exchanger efficiency to their peak values. The fouling model was developed using historical plant operating data and is based on Neural Network. Results show that the predictive model is able to predict the shell and tube outlet temperatures with excellent accuracy, where the Root Mean Square Error (RMSE) obtained is less than 1%, correlation coefficient R2 of approximately 0.98 and Correct Directional Change (CDC) values of more than 90%. A preliminary case study shows promising indication that the predictive model may be integrated into a preventive maintenance scheduling for the heat exchanger cleaning.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston