MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS

Abstract

Fouling is a deposit inside heat exchanger network in a refinery has been identified as
a major problem for efficient energy recovery. This heat exchanger network is also
called Crude Preheat Train (CPT). In this paper, Multi Layer Perceptron (MLP)
neural networks with Nonlinear Auto Regressive with eXogenous input (NARX)
structure is utilized to build the heat exchanger fouling resistant model in refinery
CPT and build predictive maintenance support tool based on neural network and
HYSYS simulation model. The complexity and nonlinierity of the nature of the heat
exchanger fouling characteristics due to changes in crude and product operating
conditions, and also crude oil blends in the feed stocks have been captured very
accurate by the proposed software. The RMSE is used to indicate the performance of
the proposed software. The result shows that the average RMSE of integrated model in
predicting outlet temperature of heat exchangerTH,out and TC,out between the actual
and predicted values are determined to be 1.454 °C and 1.0665 °C, respectively. The
integrated model is ready to usein support plant cleaning scheduling optimization,
incorporate with optimization software.