Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber
Road vehicles, particularly cars, are one of the primary sources of CO<sub>2</sub> emissions in the transport sector. Shifting to unconventional energy sources such as solar and wind power may reduce their carbon footprints considerably. Consequently, using ammonia as a fuel due to its p...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/3/746 |
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| author | Lijia Fang Hardeep Singh Takuma Ohashi Masato Sanno Guansen Lin Emir Yilmaz Mitsuhisa Ichiyanagi Takashi Suzuki |
| author_facet | Lijia Fang Hardeep Singh Takuma Ohashi Masato Sanno Guansen Lin Emir Yilmaz Mitsuhisa Ichiyanagi Takashi Suzuki |
| author_sort | Lijia Fang |
| collection | DOAJ |
| description | Road vehicles, particularly cars, are one of the primary sources of CO<sub>2</sub> emissions in the transport sector. Shifting to unconventional energy sources such as solar and wind power may reduce their carbon footprints considerably. Consequently, using ammonia as a fuel due to its potential benefits, such as its high energy density, being a carbon-free fuel, and its versatility during storage and transportation, has now grabbed the attention of researchers. However, its slow combustion speed, larger combustion chamber requirements, ignition difficulties, and limited combustion stability are still major challenges. Therefore, authors tried to analyze the combustion pressure of ammonia in a constant-volume combustion chamber across different equivalence ratios by adopting a machine learning approach. While conducting the analysis, the experimental values were assessed and subsequently utilized to predict the induced combustion pressure in a constant-volume combustion chamber across various equivalence ratios. In this research, a two-step prediction process was employed. In the initial step, the Random Forest algorithm was applied to assess the combustion pressure. Subsequently, in the second step, artificial neural network machine learning algorithms were employed to pinpoint the most effective algorithm with a lower root-mean-square error and R<sup>2</sup>. Finally, Linear Regression illustrated the lowest error in both steps with a value of 1.0, followed by Random Forest. |
| first_indexed | 2024-03-08T03:58:06Z |
| format | Article |
| id | doaj.art-de2175fbd7c6409ab74c313a2faf2411 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T03:58:06Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-de2175fbd7c6409ab74c313a2faf24112024-02-09T15:11:50ZengMDPI AGEnergies1996-10732024-02-0117374610.3390/en17030746Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion ChamberLijia Fang0Hardeep Singh1Takuma Ohashi2Masato Sanno3Guansen Lin4Emir Yilmaz5Mitsuhisa Ichiyanagi6Takashi Suzuki7Graduate School of Science and Technology, Sophia University, Tokyo 102-8554, JapanDepartment of Engineering and Applied Sciences, Sophia University, Tokyo 102-8554, JapanGraduate School of Science and Technology, Sophia University, Tokyo 102-8554, JapanGraduate School of Science and Technology, Sophia University, Tokyo 102-8554, JapanGraduate School of Science and Technology, Sophia University, Tokyo 102-8554, JapanDepartment of Engineering and Applied Sciences, Sophia University, Tokyo 102-8554, JapanDepartment of Engineering and Applied Sciences, Sophia University, Tokyo 102-8554, JapanDepartment of Engineering and Applied Sciences, Sophia University, Tokyo 102-8554, JapanRoad vehicles, particularly cars, are one of the primary sources of CO<sub>2</sub> emissions in the transport sector. Shifting to unconventional energy sources such as solar and wind power may reduce their carbon footprints considerably. Consequently, using ammonia as a fuel due to its potential benefits, such as its high energy density, being a carbon-free fuel, and its versatility during storage and transportation, has now grabbed the attention of researchers. However, its slow combustion speed, larger combustion chamber requirements, ignition difficulties, and limited combustion stability are still major challenges. Therefore, authors tried to analyze the combustion pressure of ammonia in a constant-volume combustion chamber across different equivalence ratios by adopting a machine learning approach. While conducting the analysis, the experimental values were assessed and subsequently utilized to predict the induced combustion pressure in a constant-volume combustion chamber across various equivalence ratios. In this research, a two-step prediction process was employed. In the initial step, the Random Forest algorithm was applied to assess the combustion pressure. Subsequently, in the second step, artificial neural network machine learning algorithms were employed to pinpoint the most effective algorithm with a lower root-mean-square error and R<sup>2</sup>. Finally, Linear Regression illustrated the lowest error in both steps with a value of 1.0, followed by Random Forest.https://www.mdpi.com/1996-1073/17/3/746ammoniacombustionmachine learningrandom forestartificial neural networkin-cylinder pressure |
| spellingShingle | Lijia Fang Hardeep Singh Takuma Ohashi Masato Sanno Guansen Lin Emir Yilmaz Mitsuhisa Ichiyanagi Takashi Suzuki Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber Energies ammonia combustion machine learning random forest artificial neural network in-cylinder pressure |
| title | Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber |
| title_full | Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber |
| title_fullStr | Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber |
| title_full_unstemmed | Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber |
| title_short | Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber |
| title_sort | effect of machine learning algorithms on prediction of in cylinder combustion pressure of ammonia oxygen in a constant volume combustion chamber |
| topic | ammonia combustion machine learning random forest artificial neural network in-cylinder pressure |
| url | https://www.mdpi.com/1996-1073/17/3/746 |
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