A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri
Heat exchangers are widely used in industry both for cooling and heating large scale industrial processes. This process offers a complete line of thermal fluid heat transfer systems. Therefore, much energy is required to operate this unit. Neural network tecniques have been applied to many thermal p...
| Main Authors: | , |
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| Format: | Research Reports |
| Language: | English |
| Published: |
Research Management Institute (RMI)
2012
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/21022/ |
| _version_ | 1848805140729954304 |
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| author | Abdullah, Zalizawati Kasmuri, Nor Hazelah |
| author_facet | Abdullah, Zalizawati Kasmuri, Nor Hazelah |
| author_sort | Abdullah, Zalizawati |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | Heat exchangers are widely used in industry both for cooling and heating large scale industrial processes. This process offers a complete line of thermal fluid heat transfer systems. Therefore, much energy is required to operate this unit. Neural network tecniques have been applied to many thermal problems, including the prediction of the steady-state and the dynamic behavior of heat exchangers. The neural network is used as a nonlinear process model to predict the future behavior of the controlled process i.e temperature outlet. The ability of neural network system to understand the behavior of this thermal process can lead to energy saving. In this study, the multiple neural network model will be developed to describe the nonlinear dynamic behavior of the heat exchanger. It is based on fact that multiple neural network model often giving improved performance compared to single network systems in terms of their accuracy and generalization capability. Experimental data will be collected from pilot plant heat exchanger equipped with Emerson DeltaV™ DCS in order to provide sufficient data processing to develop the model. The multiple multilayer perceptron neural network model of the heat exchanger will be developed in Matlab™ environment and the performance of the model developed will be assessed. Successful investigation will lead to availability of accurate models to represent the nonlinear dynamic behavior of heat exchanger that can be used in the development of advanced control system. |
| first_indexed | 2025-11-14T22:06:18Z |
| format | Research Reports |
| id | uitm-21022 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T22:06:18Z |
| publishDate | 2012 |
| publisher | Research Management Institute (RMI) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-210222018-10-08T07:38:46Z https://ir.uitm.edu.my/id/eprint/21022/ A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri Abdullah, Zalizawati Kasmuri, Nor Hazelah Neural networks (Computer science) Heat exchangers are widely used in industry both for cooling and heating large scale industrial processes. This process offers a complete line of thermal fluid heat transfer systems. Therefore, much energy is required to operate this unit. Neural network tecniques have been applied to many thermal problems, including the prediction of the steady-state and the dynamic behavior of heat exchangers. The neural network is used as a nonlinear process model to predict the future behavior of the controlled process i.e temperature outlet. The ability of neural network system to understand the behavior of this thermal process can lead to energy saving. In this study, the multiple neural network model will be developed to describe the nonlinear dynamic behavior of the heat exchanger. It is based on fact that multiple neural network model often giving improved performance compared to single network systems in terms of their accuracy and generalization capability. Experimental data will be collected from pilot plant heat exchanger equipped with Emerson DeltaV™ DCS in order to provide sufficient data processing to develop the model. The multiple multilayer perceptron neural network model of the heat exchanger will be developed in Matlab™ environment and the performance of the model developed will be assessed. Successful investigation will lead to availability of accurate models to represent the nonlinear dynamic behavior of heat exchanger that can be used in the development of advanced control system. Research Management Institute (RMI) 2012 Research Reports NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/21022/1/LP_ZALIZAWATI%20ABDULLAH%20RMI%2012_5.pdf Abdullah, Zalizawati and Kasmuri, Nor Hazelah (2012) A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri. (2012) [Research Reports] (Unpublished) |
| spellingShingle | Neural networks (Computer science) Abdullah, Zalizawati Kasmuri, Nor Hazelah A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri |
| title | A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri |
| title_full | A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri |
| title_fullStr | A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri |
| title_full_unstemmed | A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri |
| title_short | A dynamic neural network based model predicting outlet temperature of heat exchanger / Zalizawati Abdullah and Nor Hazelah Kasmuri |
| title_sort | dynamic neural network based model predicting outlet temperature of heat exchanger / zalizawati abdullah and nor hazelah kasmuri |
| topic | Neural networks (Computer science) |
| url | https://ir.uitm.edu.my/id/eprint/21022/ |