Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine
In this study, it was aimed to predict and optimize the effects of acetone/gasoline mixtures on spark ignition engine responses at different engine speeds and ignition advance values with artificial neural network and response surface methodology. The regression results obtained from response surfac...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | Science and Technology for Energy Transition |
| Subjects: | |
| Online Access: | https://www.stet-review.org/articles/stet/full_html/2022/01/stet20220014/stet20220014.html |
| _version_ | 1811232176575873024 |
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| author | Uslu Samet Yesilyurt Murat Kadir Yaman Hayri |
| author_facet | Uslu Samet Yesilyurt Murat Kadir Yaman Hayri |
| author_sort | Uslu Samet |
| collection | DOAJ |
| description | In this study, it was aimed to predict and optimize the effects of acetone/gasoline mixtures on spark ignition engine responses at different engine speeds and ignition advance values with artificial neural network and response surface methodology. The regression results obtained from response surface methodology show that absolute variance ratio values for all answers are greater than 0.96. Correlation coefficient values obtained from artificial neural network were obtained higher than 0.91. Mean absolute percentage error values were between 0.8859% and 9.01427% for artificial neural network, while it was between 1.146% and 8.957% for response surface methodology. Optimization study with response surface methodology revealed that the optimum results are 1700 rpm engine speed, 2% acetone ratio and 11° before top dead center ignition advance with a combined desirability factor of 0.76523%. Additionally, in accordance with the confirmation analysis among the optimal outcomes and the estimation outcomes, it was stated that there is a great harmony with a maximum error percentage of 7.662%. As a result, it is concluded that the applied response surface methodology and artificial neural network models can perfectly provide the impact of acetone percentage on spark ignition engine responses at different engine speeds and ignition advance values. |
| first_indexed | 2024-04-12T10:57:59Z |
| format | Article |
| id | doaj.art-6e0fa8a2618e4b0d8d183fb33d701248 |
| institution | Directory Open Access Journal |
| issn | 2804-7699 |
| language | English |
| last_indexed | 2024-04-12T10:57:59Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Science and Technology for Energy Transition |
| spelling | doaj.art-6e0fa8a2618e4b0d8d183fb33d7012482022-12-22T03:36:02ZengEDP SciencesScience and Technology for Energy Transition2804-76992022-01-0177710.2516/stet/2022010stet20220014Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engineUslu Samet0https://orcid.org/0000-0001-9118-5108Yesilyurt Murat Kadir1https://orcid.org/0000-0003-0870-7564Yaman Hayri2Karabük University, Department of Mechanical EngineeringYozgat Bozok University, Department of Mechanical EngineeringKırıkkale University, Automotive Technology ProgramIn this study, it was aimed to predict and optimize the effects of acetone/gasoline mixtures on spark ignition engine responses at different engine speeds and ignition advance values with artificial neural network and response surface methodology. The regression results obtained from response surface methodology show that absolute variance ratio values for all answers are greater than 0.96. Correlation coefficient values obtained from artificial neural network were obtained higher than 0.91. Mean absolute percentage error values were between 0.8859% and 9.01427% for artificial neural network, while it was between 1.146% and 8.957% for response surface methodology. Optimization study with response surface methodology revealed that the optimum results are 1700 rpm engine speed, 2% acetone ratio and 11° before top dead center ignition advance with a combined desirability factor of 0.76523%. Additionally, in accordance with the confirmation analysis among the optimal outcomes and the estimation outcomes, it was stated that there is a great harmony with a maximum error percentage of 7.662%. As a result, it is concluded that the applied response surface methodology and artificial neural network models can perfectly provide the impact of acetone percentage on spark ignition engine responses at different engine speeds and ignition advance values.https://www.stet-review.org/articles/stet/full_html/2022/01/stet20220014/stet20220014.htmlartificial neural networkresponse surface methodologyacetoneoptimizationspark ignition engine |
| spellingShingle | Uslu Samet Yesilyurt Murat Kadir Yaman Hayri Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine Science and Technology for Energy Transition artificial neural network response surface methodology acetone optimization spark ignition engine |
| title | Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine |
| title_full | Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine |
| title_fullStr | Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine |
| title_full_unstemmed | Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine |
| title_short | Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine |
| title_sort | impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine |
| topic | artificial neural network response surface methodology acetone optimization spark ignition engine |
| url | https://www.stet-review.org/articles/stet/full_html/2022/01/stet20220014/stet20220014.html |
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