TY  - JOUR
SN  - 13594311
EP  - 2802
AV  - none
SP  - 2791
TI  - Heat exchanger fouling model and preventive maintenance scheduling tool
N1  - cited By 67
Y1  - 2007///
VL  - 27
A1  - Radhakrishnan, V.R.
A1  - Ramasamy, M.
A1  - Zabiri, H.
A1  - Do Thanh, V.
A1  - Tahir, N.M.
A1  - Mukhtar, H.
A1  - Hamdi, M.R.
A1  - Ramli, N.
JF  - Applied Thermal Engineering
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548061844&doi=10.1016%2fj.applthermaleng.2007.02.009&partnerID=40&md5=d19aa4ea25a1d215ee6fb7712a280776
ID  - scholars235
KW  - Fouling; Neural networks; Preventive maintenance; Scheduling; Waste heat
KW  -  Crude preheat train; Heat exchanger fouling; Prediction models
KW  -  Heat exchangers
IS  - 17-18
N2  - The crude preheat train (CPT) in a petroleum refinery consists of a set of large heat exchangers which recovers the waste heat from product streams to preheat the crude oil. In these exchangers the overall heat transfer coefficient reduces significantly during operation due to fouling. The rate of fouling is highly dependent on the properties of the crude blends being processed as well as the operating temperature and flow conditions. 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. A neural network based fouling model has been developed using historical plant operating data. Root mean square error (RMSE) of the predictions in tube- and shell-side outlet temperatures of 1.83 and 0.93, respectively, with a correlation coefficient, R2, of 0.98 and correct directional change (CDC) values of more than 92 show that the model is adequately accurate. A case study illustrates the methodology by which the predictive model can be used to develop a preventive maintenance scheduling tool. © 2007 Elsevier Ltd. All rights reserved.
ER  -