Performance & emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches |
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| Affiliation: | 1. İskenderun Technical University, Department of Mechanical Engineering, Hatay, Turkey;2. İskenderun Technical University, Department of Mechatronics Engineering, Hatay, Turkey;3. İskenderun Technical University, Department of Electrical and Electronical Engineering, Hatay, Turkey;1. Department of Mechanical Engineering, Federal University of Rio de Janeiro, Cidade Universitária, 21941-970 Rio de Janeiro, Brazil;2. Department of Electrical Engineering, Federal University of Pará, 01 Augusto Correa Street, 66075-110 Belém, Pará, Brazil;3. Department of Mechanical Engineering, Federal University of Pará, 01 Augusto Correa Street, 66075-110 Belém, Pará, Brazil;1. Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Egypt;2. Mechanical Engineering Department, Faculty of Engineering, Misr University for Science and Technology, 6th October City, Egypt;1. Department of Mechanical Engineering, University of Engineering and Technology, Lahore, 54000, Punjab, Pakistan;2. Department of Mechanical Engineering, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan;3. Directorate of Public Instruction (Colleges), Government of Punjab, Lahore, 54000, Punjab, Pakistan;4. Department of Electrical Engineering, Lahore College for Women University, Lahore, 54000, Punjab, Pakistan;5. Institute of Business and Management, University of Engineering and Technology, Lahore, 54000, Punjab, Pakistan;6. Department of Mechanical Engineering, College of Engineering and Technology, University of Sargodha, Sargodha, 40100, Pakistan;7. Faculty of Engineering, University of Management and Technology, Lahore, 54000, Pakistan;8. Alternative Fuel Research Laboratory (AFRL), Energy Division, Department of Mechanical Engineering, Faculty of Engineering, Erciyes University, 38039, Kayseri, Turkey;1. Department of Mechanical Engineering, GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India;2. Department of Mechanical Engineering, University College of Engineering, Villupuram, Tamilnadu, India;3. Department of Automobile Engineering, Madras Institute of Technology campus, Anna University, Chennai, India;4. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Uttar Pradesh, India |
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| Abstract: | Most of the studies on conventional fuel types that can be used in internal combustion engines have been made in order to improve performance values. Nowadays environmental problems have shown that emission values are more important and interest in low carbon alternative fuels has highly increased in recent years. In this study, performance and emission values of soybean biodiesel (B25) fuel mixture used in diesel engine were investigated in detail by making different ratios of hydroxy (HHO) enrichment (3, 5 and 7 L/min). HHO enrichments increased brake torque and power outputs with direct correlation to flow rate amount; at the same time brake specific fuel consumption has decreased. Also, one of the main objectives of this study is to predict the optimum hydrogen requirement against performance reductions and NOx formations among test fuels (3, 5, and 7 L/min HHO enriched B25), too by using artificial intelligence. For developing the ANN structure, Levenberg-Marquardt (LM) learning algorithm was used to adjust the weights in the cascade forward network. The results show that the ANN model has 95,82%, 96,07%, and 92,35% estimation accuracies for motor torque, motor power, and NOx emission, respectively. |
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| Keywords: | Hydroxy (HHO) Biodiesel Artificial intelligence CI engine ANN |
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