Heat Exchanger Performance Prediction Modeling using NARX-type Neural Networks - M. , Ramasamy (2007)
The Crude Preheat Train (CPT) in a petroleum refinery recovers waste heat from product streams to preheat the crude oil. Due to high fouling nature of the fluids that flow through the exchangers, the performance deteriorates significantly over time as less heat can be transferred through the fouling layers. Prediction of the performance for optimal scheduling of the CPT operations requires a reasonably accurate mathematical model. There are no guidelines for selecting relevant input variables and correct functional forms for building theoretical models for such nonlinear systems. Neural Network (NN) offers the flexibility to model complex and nonlinear systems with good prediction capabilities. In this paper, prediction models using two different types of NNs are developed and compared for a heat exchanger to predict the change in the outlet temperatures over time. The data required for model building were collected from plant historian in a refinery. The data were processed for removal of outliers through Principal Component Analysis (PCA) and the important input variables (predictors) were selected using Projection to Latent Structures (PLS). A nonlinear auto-regression with exogenous inputs (NARX) type neural network model demonstrates its superior prediction capabilities with a root mean square error of less than 2.5 oC in the outlet temperatures and possesses a correct directional change index of more than 90%.