Modeling heat exchanger using neural networks

Totok R. Biyanto, M. Ramasamy*, H. Zabiri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

Tools to predict the effects caused by frequent changes in the feedstock and in the operating condition in crude preheat train (CPT) in a refinery are essential to maintain optimal operating conditions in the heat exchanger. Currently, no such tools are used in industries. In this paper, an approach based on Nonlinear Auto Regressive with eXogenous input (NARX) type multi layer perceptron neural network model is proposed. This model serves as the prediction tool in order to determine the optimal operating conditions. The neural network model was developed using data collected from CPT in a refinery. In addition to the data on flow rates and temperatures of the streams in the heat exchanger, data on physico-chemical properties and crude blend were also included as input variables to the model. It was observed that the Root Mean Square Error (RMSE) during training and validation phases are less than 0.3°C proving that the modeling approach employed in this research is suitable to capture the complex and nonlinear characteristics of the heat exchanger.

Original languageEnglish
Title of host publication2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007
Pages120-124
Number of pages5
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007 - Kuala Lumpur, Malaysia
Duration: 25 Nov 200728 Nov 2007

Publication series

Name2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007

Conference

Conference2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007
Country/TerritoryMalaysia
CityKuala Lumpur
Period25/11/0728/11/07

Keywords

  • Heat exchanger
  • Modeling
  • Neural network

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