Application of machine learning algorithms for predicting the engine characteristics of a wheat germ oil–Hydrogen fuelled dual fuel engine |
| |
| Affiliation: | 1. Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey;2. Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India;3. Department of Automobile Engineering, SRM Institute of Science and Technology, NCR Campus, Ghaziabad, 201204, India;4. Department of Thermal Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India;5. Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey;1. Dept. of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India;2. Green Vehicle Technology Research Centre, Department of Automobile Engineering, SRMIST, India;1. Department of Mechanical Engineering, Annamalai University, Annamalainagar, Tamilnadu, India;2. Department of Mechanical Engineering, Easwari Engineering College, Ramapuran, Chennai, Tamilnadu, India;3. Department of Mechanical Engineering, Istinye University, Istanbul, Turkey;4. Department of Information Technology, Annamalai University, Annamalainagar, Tamilnadu, India;5. Department of Mechanical Engineering, Pondicherry Technological University, Puducherry, India;6. Department of Mechanical Engineering, Government College of Engineering, Dharmapuri, Tamilnadu, India;1. Bursa Technical University, Faculty of Engineering and Natural Sciences, Department of Mechanical Engineering, Mimar Sinan Campus, Bursa, 16310, Turkey;2. Istanbul Technical University, Energy Institute, Maslak, TR-34469, Istanbul, Turkey;1. Bursa Technical University, Smart Grid Lab., Department of Electrical and Electronics Engineering, 16300, Bursa, Turkey;2. TEIAS 2nd Regional Directorate Facility and Control Chief Engineering, Bursa, Turkey;1. Department of Thermal Engineering, Saveetha School of Engineering, SIMATS, Chennai, TN, India;2. Department of Mechanical Engineering, Jeppiaar Engineering College, Chennai, TN, India;3. Jeppiaar Research and Development Organization (JRDO), Jeppiaar University, Chennai, TN, India;4. Department of Mechanical Engineering, New Horizon College of Engineering, Bangalore, KA, India;5. Department of Mechanical Engineering, Faculty of Engineering, Düzce University, Düzce, 81620, Türkiye;6. Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam;7. Research Center for Advanced Materials Science (RCAMS), King Khalid University, P. O. Box 9004, Abha 61413, Saudi Arabia;8. Physics Department, Faculty of Science, King Khalid University, P. O. Box 9004, Abha, Saudi Arabia;9. School of Mechanical Engineering, Vellore Institute of Technology Vellore, 632014 Tamil Nadu, India;10. Department of Mechanical Engineering, College of Engineering, King Khalid University, P. O. Box 9004, Abha 61413, Saudi Arabia;11. Department of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, Belgavi), Mangaluru 574153, India;12. University Centre for Research & Development, Department of Mechanical Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India.;1. Department of Automobile Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India;2. Department of Mechanical Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India;3. Department of Mechanical Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India;4. Department of Thermal Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India;5. Department of Mechanical Engineering, Jeppiaar Institute of Technology, Kancheepuram, Tamil Nadu, India |
| |
| Abstract: | In this research work, performance and emission parameters of wheat germ oil (WGO) -hydrogen dual fuel was investigated experimentally and these parameters were predicted using different machine learning algorithms. Initially, hydrogen injection with 5%, 10% and 15% energy share were used as the dual fuel strategy with WGO. For WGO +15% hydrogen energy share the NO emission is 1089 ppm, which is nearly 33% higher than WGO at full load. As hydrogen has higher flame speed and calorific value and wider flammability limit which increases the combustion temperature. Thus, the reaction between nitrogen and oxygen increases thereby forming more NO. Smoke emission for WGO +15% hydrogen energy share is 66%, which is 15% lower compared to WGO, since the heat released in the pre-mixed phase of combustion is increased to a maximum with higher hydrogen energy share compared to WGO. Different applications including internal combustion engines have used machine learning approaches for predictions and classifications. In the second phase various machine learning techniques namely Decision Tree (DT), Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Machines (SVM)) were used to predict the emission characteristics of the engine operating in dual fuel mode. The machine learning models were trained and tested using the experimental data. The most effective model was identified using performance metrics like R-Squared (R2) value, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The result shows that the prediction by MLR model was closest to the experimental results. |
| |
| Keywords: | Hydrogen energy Emissions Performance Wheat germ oil Dual fuel engine Machine learning |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
