Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms
Abstract The growing need for sustainable energy sources and stricter environmental regulations necessitate the development of alternative fuels with lower emissions and improved performance. This study addresses these challenges by optimizing the performance and emission characteristics of a single...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97092-2 |
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| author | M. S. Aswathanrayan N. Santhosh Srikanth Holalu Venkataramana Kurugundla Sunil Kumar Sarfaraz Kamangar Amir Ibrahim Ali Arabi Sameer Algburi Osamah J. Al-sareji A. Bhowmik |
| author_facet | M. S. Aswathanrayan N. Santhosh Srikanth Holalu Venkataramana Kurugundla Sunil Kumar Sarfaraz Kamangar Amir Ibrahim Ali Arabi Sameer Algburi Osamah J. Al-sareji A. Bhowmik |
| author_sort | M. S. Aswathanrayan |
| collection | DOAJ |
| description | Abstract The growing need for sustainable energy sources and stricter environmental regulations necessitate the development of alternative fuels with lower emissions and improved performance. This study addresses these challenges by optimizing the performance and emission characteristics of a single-cylinder diesel engine powered by neem oil biodiesel blends enhanced with alumina nanoparticlesusing the powerful desirability-based optimization. Neem oil, a non-edible feedstock, was selected to avoid competition with food resources, while alumina nanoparticles were utilized for their catalytic properties to enhance combustion efficiency. The process involved experimental evaluation of biodiesel blends (B10, B20, and B30) combined with alumina nanoparticles at concentrations of 100 ppm, 150 ppm, and 200 ppm using a design of experiments approach. With the engine running at maximum load of 100% and an aluminum oxide concentration of 100 parts per million, the optimal fuel mix comprises of 89.85% diesel and 30% biodiesel. The lowest brake-specific fuel consumption of 0.45 kg per kilowatt-hour that the optimization produced points to effective fuel use. With a little variance of 3.33%, the brake thermal efficiency was maximized at 38.18%, quite near to the validation result of 37.89%. The alumina nanoparticles enhanced combustion through improved fuel atomization and oxidation due to their high surface area and catalytic effects. To further validate the effectiveness of RSM, the results are compared with the performance of several advance machine learning algorithms, including linear regression, decision tree, and random forest. The random forest model demonstrated the highest predictive accuracy for performance (test R2 = 0.9620, Test MAPE = 3.6795%), making it the most reliable statistical approach for predicting BSFC compared to linear regression and decision Tree models. The random forest model also outperformed other approaches in predicting emissions, achieving the highest accuracy with a test R2 of 0.9826 and the lowest test MAPE of 9.3067%.This integrated experimental and predictive approach provided a robust framework for optimizing biodiesel formulations, identifying the ideal combination of biodiesel blend ratio and nanoparticle concentration. The findings highlight the potential of neem oil biodiesel blends enhanced with alumina nanoparticles to achieve a sustainable balance between improved engine performance and reduced emissions in CI engines. |
| format | Article |
| id | doaj-art-21164f63711b4b93af07cfaea3fb2ef6 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-21164f63711b4b93af07cfaea3fb2ef62025-08-20T02:17:01ZengNature PortfolioScientific Reports2045-23222025-04-0115113010.1038/s41598-025-97092-2Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithmsM. S. Aswathanrayan0N. Santhosh1Srikanth Holalu Venkataramana2Kurugundla Sunil Kumar3Sarfaraz Kamangar4Amir Ibrahim Ali Arabi5Sameer Algburi6Osamah J. Al-sareji7A. Bhowmik8Department of Mechanical Engineering, ICEASDepartment of Mechanical Engineering, MVJ College of EngineeringDepartment of Aeronautical Engineering, NitteMeenakshi Institute of TechnologyDepartment of Marine Engineering, Faculty of Engineering, Sri Venkateswara College of EngineeringMechanical Engineering Department, College of Engineering, King Khalid UniversityMechanical Engineering Department, College of Engineering, King Khalid UniversityAl-Kitab UniversityFaculty of Engineering, University of PannoniaCentre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityAbstract The growing need for sustainable energy sources and stricter environmental regulations necessitate the development of alternative fuels with lower emissions and improved performance. This study addresses these challenges by optimizing the performance and emission characteristics of a single-cylinder diesel engine powered by neem oil biodiesel blends enhanced with alumina nanoparticlesusing the powerful desirability-based optimization. Neem oil, a non-edible feedstock, was selected to avoid competition with food resources, while alumina nanoparticles were utilized for their catalytic properties to enhance combustion efficiency. The process involved experimental evaluation of biodiesel blends (B10, B20, and B30) combined with alumina nanoparticles at concentrations of 100 ppm, 150 ppm, and 200 ppm using a design of experiments approach. With the engine running at maximum load of 100% and an aluminum oxide concentration of 100 parts per million, the optimal fuel mix comprises of 89.85% diesel and 30% biodiesel. The lowest brake-specific fuel consumption of 0.45 kg per kilowatt-hour that the optimization produced points to effective fuel use. With a little variance of 3.33%, the brake thermal efficiency was maximized at 38.18%, quite near to the validation result of 37.89%. The alumina nanoparticles enhanced combustion through improved fuel atomization and oxidation due to their high surface area and catalytic effects. To further validate the effectiveness of RSM, the results are compared with the performance of several advance machine learning algorithms, including linear regression, decision tree, and random forest. The random forest model demonstrated the highest predictive accuracy for performance (test R2 = 0.9620, Test MAPE = 3.6795%), making it the most reliable statistical approach for predicting BSFC compared to linear regression and decision Tree models. The random forest model also outperformed other approaches in predicting emissions, achieving the highest accuracy with a test R2 of 0.9826 and the lowest test MAPE of 9.3067%.This integrated experimental and predictive approach provided a robust framework for optimizing biodiesel formulations, identifying the ideal combination of biodiesel blend ratio and nanoparticle concentration. The findings highlight the potential of neem oil biodiesel blends enhanced with alumina nanoparticles to achieve a sustainable balance between improved engine performance and reduced emissions in CI engines.https://doi.org/10.1038/s41598-025-97092-2Linear regressionDecision treeRandom forestNanoparticlesExhaust gas tempBiofuels |
| spellingShingle | M. S. Aswathanrayan N. Santhosh Srikanth Holalu Venkataramana Kurugundla Sunil Kumar Sarfaraz Kamangar Amir Ibrahim Ali Arabi Sameer Algburi Osamah J. Al-sareji A. Bhowmik Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms Scientific Reports Linear regression Decision tree Random forest Nanoparticles Exhaust gas temp Biofuels |
| title | Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms |
| title_full | Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms |
| title_fullStr | Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms |
| title_full_unstemmed | Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms |
| title_short | Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms |
| title_sort | prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ml algorithms |
| topic | Linear regression Decision tree Random forest Nanoparticles Exhaust gas temp Biofuels |
| url | https://doi.org/10.1038/s41598-025-97092-2 |
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