Staff View: Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool

Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool

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 oper...

Full description

Saved in:

Bibliographic Details
Main Authors:	H., Zabiri, V. R. , Radhakrishnan, M. , Ramasamy, C. S., Wah, V., Do Thanh, N. M., Ramli
Format:	Conference or Workshop Item
Published:	2006
Subjects:	TP Chemical technology
Online Access:	http://eprints.utp.edu.my/3762/1/Zabiri_et_al_full_paper_%28final5%29.pdf http://eprints.utp.edu.my/3762/
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.utp.eprints.3762
record_format	eprints
spelling	my.utp.eprints.37622017-01-19T08:27:23Z Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool H., Zabiri V. R. , Radhakrishnan M. , Ramasamy C. S., Wah V., Do Thanh N. M., Ramli TP Chemical technology 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. 2006 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/3762/1/Zabiri_et_al_full_paper_%28final5%29.pdf H., Zabiri and V. R. , Radhakrishnan and M. , Ramasamy and C. S., Wah and V., Do Thanh and N. M., Ramli (2006) Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool. In: CHEMECA 2006, 17-20 September, 2009, Auckland, New Zealand. http://eprints.utp.edu.my/3762/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
topic	TP Chemical technology
spellingShingle	TP Chemical technology H., Zabiri V. R. , Radhakrishnan M. , Ramasamy C. S., Wah V., Do Thanh N. M., Ramli Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool
description	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.
format	Conference or Workshop Item
author	H., Zabiri V. R. , Radhakrishnan M. , Ramasamy C. S., Wah V., Do Thanh N. M., Ramli
author_facet	H., Zabiri V. R. , Radhakrishnan M. , Ramasamy C. S., Wah V., Do Thanh N. M., Ramli
author_sort	H., Zabiri
title	Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool
title_short	Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool
title_full	Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool
title_fullStr	Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool
title_full_unstemmed	Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool
title_sort	development of heat exchanger fouling model and preventive maintenance diagnostic tool
publishDate	2006
url	http://eprints.utp.edu.my/3762/1/Zabiri_et_al_full_paper_%28final5%29.pdf http://eprints.utp.edu.my/3762/
_version_	1738655292000829440
score	13.211869

Development of Heat Exchanger Fouling Model and Preventive Maintenance Diagnostic Tool

Similar Items