Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System
Submerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous s...
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Semnan University
2024-04-01
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| Series: | مجله مدل سازی در مهندسی |
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| Online Access: | https://modelling.semnan.ac.ir/article_8267_b2ddbfe10c5495c991efb2f897ea9426.pdf |
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| author | Hanieh Fani Maleki Amir Ehsan Pheili Monfared Mahmoud Rahmati |
| author_facet | Hanieh Fani Maleki Amir Ehsan Pheili Monfared Mahmoud Rahmati |
| author_sort | Hanieh Fani Maleki |
| collection | DOAJ |
| description | Submerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous studies have shown that the operating conditions of this equipment are effective on the amount of NOx production in it, artificial intelligence tools were used in this research to model and then optimize NOx emission in this equipment. For this purpose, 63 laboratory data were extracted from the researchers' previous researches, and then a combination of adaptive neural fuzzy inference system and genetic algorithm was used to model the data. In the developed system, oxygen concentration, temperature, water-oxygen concentration and solution pH were considered as input parameters to the model and NOx reduction percentage as output. The statistical analysis of the built model showed that this model with correlation coefficient of 0.9714, mean square error of 1.0938, average absolute error percentage of 4.9713 and maximum absolute error percentage of 13.2144 has a good accuracy in estimating the amount of NOx reduction. In the next step after the development of the model, the genetic algorithm and the built model were used to optimize the operating conditions with the lowest NOx emission rate. The results of this part of the research also showed that if the operating conditions are optimized, it is possible to reduce the amount of NOx released up to 37.24% |
| format | Article |
| id | doaj-art-f7432fbb0a4c41fe9d69fc666c44f0f4 |
| institution | Kabale University |
| issn | 2008-4854 2783-2538 |
| language | fas |
| publishDate | 2024-04-01 |
| publisher | Semnan University |
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| series | مجله مدل سازی در مهندسی |
| spelling | doaj-art-f7432fbb0a4c41fe9d69fc666c44f0f42025-01-15T08:14:58ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382024-04-01227619721110.22075/jme.2023.30609.24518267Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference SystemHanieh Fani Maleki0Amir Ehsan Pheili Monfared1Mahmoud Rahmati2Master's student in Chemical Engineering, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, IranAssistant Professor, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, IranAssistant Professor, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, IranSubmerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous studies have shown that the operating conditions of this equipment are effective on the amount of NOx production in it, artificial intelligence tools were used in this research to model and then optimize NOx emission in this equipment. For this purpose, 63 laboratory data were extracted from the researchers' previous researches, and then a combination of adaptive neural fuzzy inference system and genetic algorithm was used to model the data. In the developed system, oxygen concentration, temperature, water-oxygen concentration and solution pH were considered as input parameters to the model and NOx reduction percentage as output. The statistical analysis of the built model showed that this model with correlation coefficient of 0.9714, mean square error of 1.0938, average absolute error percentage of 4.9713 and maximum absolute error percentage of 13.2144 has a good accuracy in estimating the amount of NOx reduction. In the next step after the development of the model, the genetic algorithm and the built model were used to optimize the operating conditions with the lowest NOx emission rate. The results of this part of the research also showed that if the operating conditions are optimized, it is possible to reduce the amount of NOx released up to 37.24%https://modelling.semnan.ac.ir/article_8267_b2ddbfe10c5495c991efb2f897ea9426.pdfartificial intelligencegenetic algorithmadaptive neural fuzzy inference systemair pollutionmodelingnitrogen oxides |
| spellingShingle | Hanieh Fani Maleki Amir Ehsan Pheili Monfared Mahmoud Rahmati Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System مجله مدل سازی در مهندسی artificial intelligence genetic algorithm adaptive neural fuzzy inference system air pollution modeling nitrogen oxides |
| title | Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System |
| title_full | Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System |
| title_fullStr | Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System |
| title_full_unstemmed | Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System |
| title_short | Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System |
| title_sort | modeling and reduction of nox production in submerged combustion vaporizer using fuzzy inference system |
| topic | artificial intelligence genetic algorithm adaptive neural fuzzy inference system air pollution modeling nitrogen oxides |
| url | https://modelling.semnan.ac.ir/article_8267_b2ddbfe10c5495c991efb2f897ea9426.pdf |
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