Modeling heat exchanger using neural networks
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...
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my.utp.eprints.27592017-01-19T08:27:14Z Modeling heat exchanger using neural networks T.R., Biyanto M., Ramasamy H., Zabiri TP Chemical technology 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. ©2007 IEEE. 2007 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/2759/1/modeling_heat_exchanger_using_neural_networks.pdf http://www.scopus.com/inward/record.url?eid=2-s2.0-57949108485&partnerID=40&md5=93721787b4e81cc982e23cb5f67aa270 T.R., Biyanto and M., Ramasamy and H., Zabiri (2007) Modeling heat exchanger using neural networks. In: 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007, 25 November 2007 through 28 November 2007, Kuala Lumpur. http://eprints.utp.edu.my/2759/ |
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TP Chemical technology T.R., Biyanto M., Ramasamy H., Zabiri Modeling heat exchanger using neural networks |
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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. ©2007 IEEE.
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Conference or Workshop Item |
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T.R., Biyanto M., Ramasamy H., Zabiri |
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T.R., Biyanto M., Ramasamy H., Zabiri |
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T.R., Biyanto |
title |
Modeling heat exchanger using neural networks |
title_short |
Modeling heat exchanger using neural networks |
title_full |
Modeling heat exchanger using neural networks |
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Modeling heat exchanger using neural networks |
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Modeling heat exchanger using neural networks |
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modeling heat exchanger using neural networks |
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2007 |
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http://eprints.utp.edu.my/2759/1/modeling_heat_exchanger_using_neural_networks.pdf http://www.scopus.com/inward/record.url?eid=2-s2.0-57949108485&partnerID=40&md5=93721787b4e81cc982e23cb5f67aa270 http://eprints.utp.edu.my/2759/ |
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