eprintid: 163 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/00/01/63 datestamp: 2023-11-09 15:15:48 lastmod: 2023-11-09 15:15:48 status_changed: 2023-11-09 15:13:27 type: conference_item metadata_visibility: show creators_name: Biyanto, T.R. creators_name: Ramasamy, M. creators_name: Zabiri, H. title: Modeling heat exchanger using neural networks ispublished: pub keywords: Charge trapping; Chemical properties; Heat exchangers; Mathematical models; Refining; Solar water heaters; Theorem proving; Vegetation; Wireless sensor networks, Crude preheat trains; Input variables; Modeling; Modeling approaches; Multi layers; Neural network models; Nonlinear characteristics; On flows; Operating conditions; Optimal operating conditions; Physico-chemical properties; Prediction tools; Validation phasis, Neural networks note: cited By 9; Conference of 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007 ; Conference Date: 25 November 2007 Through 28 November 2007; Conference Code:74506 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. ©2007 IEEE. date: 2007 official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-57949108485&doi=10.1109%2fICIAS.2007.4658359&partnerID=40&md5=1fa3804febfa0f72b439f15e635117d3 id_number: 10.1109/ICIAS.2007.4658359 full_text_status: none publication: 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007 place_of_pub: Kuala Lumpur pagerange: 120-124 refereed: TRUE isbn: 1424413559; 9781424413553 citation: Biyanto, T.R. and Ramasamy, M. and Zabiri, H. (2007) Modeling heat exchanger using neural networks. In: UNSPECIFIED.