Diagnosis of the Fouling Effects in a Shell and Tube Heat Exchanger using Artificial Neural Network

Abstract

In this paper, there presents an identification method of the fouling influence on the heat recovery in a heat exchanger. To evaluate the heat losses due to fouling there is proposed the method based on neural network approach. Because the correctness of anticipation of the heat exchanger behaviour depends on measurement data, it is very important to prepare an appropriate data. Unfortunately, the measured data contains errors caused by inaccurate instruments, disturbances in data transmission, transient state of the operated heat exchanger. To overcome this problem the authors proposed a new approach of filtering the row data using objective function based on standard deviation of measured parameters (temperature, flow rate, etc.) in the heat exchanger. Minimising the objective function, it makes it possible to eliminate gross errors and properly select the time intervals of steady state operation of the heat exchanger. The method was validated using measurement data for the heat exchanger belonging to heat exchanger network connected with a crude distillation unit processing 800 t/h of crude oil. Using artificial neural network the time dependencies between heat losses versus time were obtained.