Investigation of boiler energy consumption in the gas refinery units using RSM ANN and Aspen HYSYS
In order to lower total energy consumption, this study focuses on optimizing energy use in refinery boilers. Using Aspen HYSYS simulations and modeling approaches like Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM), data from 579 days of boiler operation was gathered and ex...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024174816 |
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| Summary: | In order to lower total energy consumption, this study focuses on optimizing energy use in refinery boilers. Using Aspen HYSYS simulations and modeling approaches like Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM), data from 579 days of boiler operation was gathered and examined. Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) techniques were used in the ANN modeling. Under the same operating circumstances, Aspen HYSYS estimated an energy usage of 1,355 m³, whereas the actual consumption was 986 m³. While the R2 values for the ANN models were 0.98 for the RBF model and 0.99 for the MLP model, the R2 value derived using RSM was 0.97. Furthermore, the RBF model's performance metrics were 0.0034, whereas the MLP model's were 0.0018. The MLP model is the best choice, according to these findings. It is estimated that burning 26,000 m³ of fuel with an air supply of 23 m³/h at 25.5 °C will result in a steam flow of 525.5 tons per day at 10.5 barg and 256.5 °C. According to actual statistics, these circumstances might prevent the release of 27 tons of carbon dioxide by reducing fuel usage by over 10,000 m³ per hour. By optimizing the combustion stack's air supply, this decrease is accomplished. |
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| ISSN: | 2405-8440 |