Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus
Abstract Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-87640-1 |
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| author | V Vinoth Kannan Bhavesh Kanabar J Gowrishankar Ali Khatibi. Sarfaraz Kamangar Amir Ibrahim Ali Arabi Pushparaj Thomai Jasmina Lozanović |
| author_facet | V Vinoth Kannan Bhavesh Kanabar J Gowrishankar Ali Khatibi. Sarfaraz Kamangar Amir Ibrahim Ali Arabi Pushparaj Thomai Jasmina Lozanović |
| author_sort | V Vinoth Kannan |
| collection | DOAJ |
| description | Abstract Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi’s biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy. The study identified the Teak biodiesel blend (TB20) with a 5 ml Elaeocarpus Ganitrus additive (TB20 + R5) as the optimal formulation, achieving the highest Brake Thermal Efficiency and reduced Brake-Specific Fuel Consumption. Desirability analysis further confirmed the blend’s superior performance and emissions characteristics, with a desirability rating of 0.9282. This work highlights the potential of hybrid optimization approaches for improving biodiesel performance and emissions without engine modifications, contributing to the advancement of sustainable energy practices in internal combustion engines. |
| format | Article |
| id | doaj-art-53af716a5f0c495e94e18cbea5d30eb8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-53af716a5f0c495e94e18cbea5d30eb82025-02-02T12:20:23ZengNature PortfolioScientific Reports2045-23222025-01-0115112810.1038/s41598-025-87640-1Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus GanitrusV Vinoth Kannan0Bhavesh Kanabar1J Gowrishankar2Ali Khatibi.3Sarfaraz Kamangar4Amir Ibrahim Ali Arabi5Pushparaj Thomai6Jasmina Lozanović7Indra Ganesan College of EngineeringDepartment of Mechanical Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi UniversityDepartment of Computer Science Engineering, School of Engineering and Technology, JAIN (Deemed to be University)Management and Science UniversityMechanical Engineering Department, College of Engineering, King Khalid UniversityMechanical Engineering Department, College of Engineering, King Khalid UniversityDepartment of Mechanical Engineering, Kings College of EngineeringDepartment of Engineering, FH Campus Wien - University of Applied SciencesAbstract Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi’s biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy. The study identified the Teak biodiesel blend (TB20) with a 5 ml Elaeocarpus Ganitrus additive (TB20 + R5) as the optimal formulation, achieving the highest Brake Thermal Efficiency and reduced Brake-Specific Fuel Consumption. Desirability analysis further confirmed the blend’s superior performance and emissions characteristics, with a desirability rating of 0.9282. This work highlights the potential of hybrid optimization approaches for improving biodiesel performance and emissions without engine modifications, contributing to the advancement of sustainable energy practices in internal combustion engines.https://doi.org/10.1038/s41598-025-87640-1Machine learningOptimizationPredictionANNKNNRSM |
| spellingShingle | V Vinoth Kannan Bhavesh Kanabar J Gowrishankar Ali Khatibi. Sarfaraz Kamangar Amir Ibrahim Ali Arabi Pushparaj Thomai Jasmina Lozanović Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus Scientific Reports Machine learning Optimization Prediction ANN KNN RSM |
| title | Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus |
| title_full | Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus |
| title_fullStr | Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus |
| title_full_unstemmed | Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus |
| title_short | Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus |
| title_sort | artificial intelligence based prediction and multi objective rsm optimization of tectona grandis biodiesel with elaeocarpus ganitrus |
| topic | Machine learning Optimization Prediction ANN KNN RSM |
| url | https://doi.org/10.1038/s41598-025-87640-1 |
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