Detection of fouling in a cross-flow heat exchanger using a neural network based technique

This paper presents a method for the detection of fouling in a cross-flow heat exchanger. A numerical model is used to generate data when the heat exchanger is clean and corresponding data when fouling occurs. In a first step, the model is used to generate a long time series by simulating a clean heat exchanger. This allows the determination of a neural network model of the heat exchanger. Then, hundred sets of data are generated by simulating a fouled heat exchanger and it is checked that the simple Cusum test can be used to detect fouling without any false alarm, whatever the reference time series is. (C) 2009 Elsevier Masson SAS. All rights reserved.