Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights
Abstract Biodiesel presents a favourable economic outlook and environmental benefits, yet it faces limitations such as diminished calorific value and suboptimal combustion characteristics. Recent research focuses on enhancing biodiesel performance using nanoparticles and thermal barrier coatings. Th...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-04033-0 |
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| author | V. S. Shaisundaram P. V. Elumalai S. Padmanabhan U. Nalini Ramachandran Abhishek Kumar Tripathi Cui Yaping B. Nagaraj Goud S. Prabhakar |
| author_facet | V. S. Shaisundaram P. V. Elumalai S. Padmanabhan U. Nalini Ramachandran Abhishek Kumar Tripathi Cui Yaping B. Nagaraj Goud S. Prabhakar |
| author_sort | V. S. Shaisundaram |
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| description | Abstract Biodiesel presents a favourable economic outlook and environmental benefits, yet it faces limitations such as diminished calorific value and suboptimal combustion characteristics. Recent research focuses on enhancing biodiesel performance using nanoparticles and thermal barrier coatings. This study investigates non-edible biodiesel from Momordica seed oil, tested on a single-cylinder diesel engine. Biodiesel blends of 10%, 20%, and 30% Momordica seed biodiesel were enhanced with cerium oxide nano additives at 45 ppm and evaluated using a partially stabilized zirconia-coated piston and cylinder liner. Additionally, machine learning (ML) algorithms, including Multiple Linear Regression (MLR), Gradient Boosting Regression (GBR), and Random Forest Regression (RF), were applied to predict thermal performance metrics using input parameters such as Fuel, Compression Ratio (CR), Load, and Peak Pressure (Bar). Among these, RF demonstrated the highest predictive accuracy, achieving the best R² values of 0.86 for Brake Thermal Efficiency (BTE) and 0.62 for Carbon Monoxide (CO) prediction, with the lowest Mean Absolute Error (MAE) of 1.30 and 2.88, respectively. These results highlight the potential of ML models in optimizing engine performance for sustainable energy systems across various engine types and fuel sources. |
| format | Article |
| id | doaj-art-d67c355db2574ba59a5e2cbcfa54d2ca |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d67c355db2574ba59a5e2cbcfa54d2ca2025-08-20T03:05:22ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-04033-0Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insightsV. S. Shaisundaram0P. V. Elumalai1S. Padmanabhan2U. Nalini Ramachandran3Abhishek Kumar Tripathi4Cui Yaping5B. Nagaraj Goud6S. Prabhakar7Department of Automobile Engineering, Vels Institute of Science, Technology & Advanced StudiesDepartment of Mechanical Engineering, Aditya UniversityDepartment of Mechanical Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyDepartment of Applied Sciences, Chemistry Section University of Technology and Applied Sciences MuscatDepartment of Mining Engineering, Aditya UniversityFaculty of Education, Shinawatra UniversityDepartment of Aeronautical Engineering, MLR Institute of TechnologySchool of Mechanical Enginnering, Wollo UniversityAbstract Biodiesel presents a favourable economic outlook and environmental benefits, yet it faces limitations such as diminished calorific value and suboptimal combustion characteristics. Recent research focuses on enhancing biodiesel performance using nanoparticles and thermal barrier coatings. This study investigates non-edible biodiesel from Momordica seed oil, tested on a single-cylinder diesel engine. Biodiesel blends of 10%, 20%, and 30% Momordica seed biodiesel were enhanced with cerium oxide nano additives at 45 ppm and evaluated using a partially stabilized zirconia-coated piston and cylinder liner. Additionally, machine learning (ML) algorithms, including Multiple Linear Regression (MLR), Gradient Boosting Regression (GBR), and Random Forest Regression (RF), were applied to predict thermal performance metrics using input parameters such as Fuel, Compression Ratio (CR), Load, and Peak Pressure (Bar). Among these, RF demonstrated the highest predictive accuracy, achieving the best R² values of 0.86 for Brake Thermal Efficiency (BTE) and 0.62 for Carbon Monoxide (CO) prediction, with the lowest Mean Absolute Error (MAE) of 1.30 and 2.88, respectively. These results highlight the potential of ML models in optimizing engine performance for sustainable energy systems across various engine types and fuel sources.https://doi.org/10.1038/s41598-025-04033-0Momordica seed biodieselPartially stabilized zirconiaHermal performance predictionMultiple linear regressionGradient boosting regressionRandom forest regression |
| spellingShingle | V. S. Shaisundaram P. V. Elumalai S. Padmanabhan U. Nalini Ramachandran Abhishek Kumar Tripathi Cui Yaping B. Nagaraj Goud S. Prabhakar Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights Scientific Reports Momordica seed biodiesel Partially stabilized zirconia Hermal performance prediction Multiple linear regression Gradient boosting regression Random forest regression |
| title | Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights |
| title_full | Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights |
| title_fullStr | Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights |
| title_full_unstemmed | Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights |
| title_short | Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights |
| title_sort | impact of metal oxides on thermal response of zirconia coated diesel engines fueled by momordica biodiesel machine learning insights |
| topic | Momordica seed biodiesel Partially stabilized zirconia Hermal performance prediction Multiple linear regression Gradient boosting regression Random forest regression |
| url | https://doi.org/10.1038/s41598-025-04033-0 |
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