Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blends
Due to the enormous of fossil fuels and the ensuing increase in automobiles, an unprecedented scenario has arisen with pollution levels that are out of human control. In this study, a fuzzy logic model is developed to predict how well a spark-ignition engine running on gasoline and ethanol mixes wou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-06-01
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| Series: | Green Processing and Synthesis |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/gps-2023-0009 |
| _version_ | 1827922358647652352 |
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| author | Kaliyaperumal Manikandan Sundaresan Ramabalan Pandian Balu Rajendran Silambarasan |
| author_facet | Kaliyaperumal Manikandan Sundaresan Ramabalan Pandian Balu Rajendran Silambarasan |
| author_sort | Kaliyaperumal Manikandan |
| collection | DOAJ |
| description | Due to the enormous of fossil fuels and the ensuing increase in automobiles, an unprecedented scenario has arisen with pollution levels that are out of human control. In this study, a fuzzy logic model is developed to predict how well a spark-ignition engine running on gasoline and ethanol mixes would operate. A test engine was operated on pure gasoline and gasoline–ethanol fuel mixtures in a range of ratios at varying engine speeds. In order to estimate outputs such as brake-specific fuel consumption (BSFC), brake thermal efficiency, nitrogen oxides (NOx), hydrocarbon emissions, and carbon monoxide, a fuzzy logic model, a sort of logic model application, has been developed using experimental data. The developed fuzzy logic model’s output was compared to the results of the trials to see how well it performed. The output parameters were indicated, including braking power, thermal, volumetric, and mechanical efficiency. The input parameters were engine speed and ethanol mixes. Regression coefficients were nearly equal for training and testing data. According to the study, a superior method for accurately forecasting engine performance is the fuzzy logic model. To eliminate proportionality signs from equations, regression analysis is used. It is accurate to develop mathematical relations based on dimensional analysis. Based on the root mean square errors, BSFC is a minimum of 6.12 and brake power is a maximum of 8.16; lower than 2% of errors occur on average. |
| first_indexed | 2024-03-13T04:41:06Z |
| format | Article |
| id | doaj.art-24e47b70051344a4b5a7f911dd60a1e5 |
| institution | Directory Open Access Journal |
| issn | 2191-9550 |
| language | English |
| last_indexed | 2024-03-13T04:41:06Z |
| publishDate | 2023-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Green Processing and Synthesis |
| spelling | doaj.art-24e47b70051344a4b5a7f911dd60a1e52023-06-19T05:52:53ZengDe GruyterGreen Processing and Synthesis2191-95502023-06-011217435510.1515/gps-2023-0009Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blendsKaliyaperumal Manikandan0Sundaresan Ramabalan1Pandian Balu2Rajendran Silambarasan3Department of Mechanical Engineering, Anna University, Chennai, Tamil Nadu, IndiaDepartment of Mechanical Engineering, E.G.S. Pillay Engineering College, Nagappattinam, Tamil Nadu, IndiaDepartment of Automobile Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, IndiaDepartment of Mechanical Engineering, Annapoorana Engineering College, Salem, Tamil Nadu, IndiaDue to the enormous of fossil fuels and the ensuing increase in automobiles, an unprecedented scenario has arisen with pollution levels that are out of human control. In this study, a fuzzy logic model is developed to predict how well a spark-ignition engine running on gasoline and ethanol mixes would operate. A test engine was operated on pure gasoline and gasoline–ethanol fuel mixtures in a range of ratios at varying engine speeds. In order to estimate outputs such as brake-specific fuel consumption (BSFC), brake thermal efficiency, nitrogen oxides (NOx), hydrocarbon emissions, and carbon monoxide, a fuzzy logic model, a sort of logic model application, has been developed using experimental data. The developed fuzzy logic model’s output was compared to the results of the trials to see how well it performed. The output parameters were indicated, including braking power, thermal, volumetric, and mechanical efficiency. The input parameters were engine speed and ethanol mixes. Regression coefficients were nearly equal for training and testing data. According to the study, a superior method for accurately forecasting engine performance is the fuzzy logic model. To eliminate proportionality signs from equations, regression analysis is used. It is accurate to develop mathematical relations based on dimensional analysis. Based on the root mean square errors, BSFC is a minimum of 6.12 and brake power is a maximum of 8.16; lower than 2% of errors occur on average.https://doi.org/10.1515/gps-2023-0009fuzzy logic modelspark ignition engineethanol–gasoline blendsperformanceemission |
| spellingShingle | Kaliyaperumal Manikandan Sundaresan Ramabalan Pandian Balu Rajendran Silambarasan Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blends Green Processing and Synthesis fuzzy logic model spark ignition engine ethanol–gasoline blends performance emission |
| title | Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blends |
| title_full | Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blends |
| title_fullStr | Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blends |
| title_full_unstemmed | Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blends |
| title_short | Development of a fuzzy logic model for the prediction of spark-ignition engine performance and emission for gasoline–ethanol blends |
| title_sort | development of a fuzzy logic model for the prediction of spark ignition engine performance and emission for gasoline ethanol blends |
| topic | fuzzy logic model spark ignition engine ethanol–gasoline blends performance emission |
| url | https://doi.org/10.1515/gps-2023-0009 |
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