MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS
Fouling is a deposit inside heat exchanger network in a refinery has been identified as a major problem for efficient energy recovery. This heat exchanger network is also called Crude Preheat Train (CPT). In this paper, Multi Layer Perceptron (MLP) neural networks with Nonlinear Auto Regressive...
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| Format: | Article |
| Language: | English |
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Informatics Department, Engineering Faculty
2015-07-01
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| Series: | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi |
| Subjects: | |
| Online Access: | https://kursorjournal.org/index.php/kursor/article/view/70 |
| _version_ | 1797743888977362944 |
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| author | Totok R. Biyanto Roekmono Roekmono Andi Rahmadiansyah Aulia Siti Aisyah Purwadi A. Darwito Tutug Dhanardono Titik Budiati |
| author_facet | Totok R. Biyanto Roekmono Roekmono Andi Rahmadiansyah Aulia Siti Aisyah Purwadi A. Darwito Tutug Dhanardono Titik Budiati |
| author_sort | Totok R. Biyanto |
| collection | DOAJ |
| description |
Fouling is a deposit inside heat exchanger network in a refinery has been identified as
a major problem for efficient energy recovery. This heat exchanger network is also
called Crude Preheat Train (CPT). In this paper, Multi Layer Perceptron (MLP)
neural networks with Nonlinear Auto Regressive with eXogenous input (NARX)
structure is utilized to build the heat exchanger fouling resistant model in refinery
CPT and build predictive maintenance support tool based on neural network and
HYSYS simulation model. The complexity and nonlinierity of the nature of the heat
exchanger fouling characteristics due to changes in crude and product operating
conditions, and also crude oil blends in the feed stocks have been captured very
accurate by the proposed software. The RMSE is used to indicate the performance of
the proposed software. The result shows that the average RMSE of integrated model in
predicting outlet temperature of heat exchangerTH,out and TC,out between the actual
and predicted values are determined to be 1.454 °C and 1.0665 °C, respectively. The
integrated model is ready to usein support plant cleaning scheduling optimization,
incorporate with optimization software.
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| first_indexed | 2024-03-12T15:02:56Z |
| format | Article |
| id | doaj.art-e85076f150274745b396d496ecf093ef |
| institution | Directory Open Access Journal |
| issn | 0216-0544 2301-6914 |
| language | English |
| last_indexed | 2024-03-12T15:02:56Z |
| publishDate | 2015-07-01 |
| publisher | Informatics Department, Engineering Faculty |
| record_format | Article |
| series | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi |
| spelling | doaj.art-e85076f150274745b396d496ecf093ef2023-08-13T20:42:52ZengInformatics Department, Engineering FacultyJurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi0216-05442301-69142015-07-018110.28961/kursor.v8i1.70MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYSTotok R. Biyanto0Roekmono Roekmono1Andi Rahmadiansyah2Aulia Siti Aisyah3Purwadi A. Darwito4Tutug Dhanardono5Titik Budiati6Engineering Physics Departement, FTI Sepuluh Nopember Institute of Techology (ITS) SurabayaFood Technology Department, State Polytechnic of JemberEngineering Physics Departement, FTI Sepuluh Nopember Institute of Techology (ITS) Surabaya,Engineering Physics Departement, FTI Sepuluh Nopember Institute of Techology (ITS) Surabaya,Engineering Physics Departement, FTI Sepuluh Nopember Institute of Techology (ITS) Surabaya,Engineering Physics Departement, FTI Sepuluh Nopember Institute of Techology (ITS) Surabaya,Engineering Physics Departement, FTI Sepuluh Nopember Institute of Techology (ITS) Surabaya, Fouling is a deposit inside heat exchanger network in a refinery has been identified as a major problem for efficient energy recovery. This heat exchanger network is also called Crude Preheat Train (CPT). In this paper, Multi Layer Perceptron (MLP) neural networks with Nonlinear Auto Regressive with eXogenous input (NARX) structure is utilized to build the heat exchanger fouling resistant model in refinery CPT and build predictive maintenance support tool based on neural network and HYSYS simulation model. The complexity and nonlinierity of the nature of the heat exchanger fouling characteristics due to changes in crude and product operating conditions, and also crude oil blends in the feed stocks have been captured very accurate by the proposed software. The RMSE is used to indicate the performance of the proposed software. The result shows that the average RMSE of integrated model in predicting outlet temperature of heat exchangerTH,out and TC,out between the actual and predicted values are determined to be 1.454 °C and 1.0665 °C, respectively. The integrated model is ready to usein support plant cleaning scheduling optimization, incorporate with optimization software. https://kursorjournal.org/index.php/kursor/article/view/70ModelingSimulationNeural NetworkFoulingHeat ExchangerCrude Preheat Trai |
| spellingShingle | Totok R. Biyanto Roekmono Roekmono Andi Rahmadiansyah Aulia Siti Aisyah Purwadi A. Darwito Tutug Dhanardono Titik Budiati MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi Modeling Simulation Neural Network Fouling Heat Exchanger Crude Preheat Trai |
| title | MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS |
| title_full | MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS |
| title_fullStr | MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS |
| title_full_unstemmed | MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS |
| title_short | MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS |
| title_sort | modelling and simulation of industrial heat exchanger etworks under fouling condition using integrated neural network and hysys |
| topic | Modeling Simulation Neural Network Fouling Heat Exchanger Crude Preheat Trai |
| url | https://kursorjournal.org/index.php/kursor/article/view/70 |
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