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Machine learning based analysis of diesel engine performance using Fe₃O₄ nanoadditive in sterculia foetida biodiesel
Srinivasarao Mylapalli1, Yaswanth Kumar Reddy Maddi1, Joga Rao Bikkavolu2
1Department of Mechanical Engineering, Sanketika Vidya Parishad Engineering College, Visakhapatnam, Andhra Pradesh, India.
Scientific Reports
|November 7, 2025
Summary
Adding magnetite nanoparticles to sterculia foetida methyl ester (SME) diesel blends enhances engine efficiency and reduces harmful emissions. Machine learning models accurately predict these performance improvements, supporting sustainable fuel technologies.
Area of Science:
- Sustainable Fuel Technologies
- Nanoparticle Applications in Engines
- Combustion Science
Background:
- Sterculia foetida methyl ester (SME) is a potential biofuel for diesel engines.
- Nanoparticle additives can modify fuel properties and engine performance.
- Optimizing nanoparticle dispersion is crucial for effective fuel enhancement.
Purpose of the Study:
- To investigate the impact of Fe₃O₄ (magnetite) nanoparticle additions on diesel engine performance, combustion, and emissions when blended with SME.
- To evaluate the effectiveness of different Fe₃O₄ concentrations (50, 75, 100 ppm) in SME-diesel blends.
- To develop and validate a machine learning model for predicting engine performance and emission characteristics of these nano-fuel blends.
Main Methods:
- SME was produced via transesterification.
- Surface-modified Fe₃O₄ nanoparticles (NPs) were dispersed in SME using ultrasonication.
- Engine tests were conducted using pure diesel, SME25 (25% SME, 75% diesel), and SME25 with varying Fe₃O₄ concentrations.
- A machine learning model was developed to predict performance and emissions.
Main Results:
- Brake thermal efficiency (BTE) increased by up to 6.69%, and specific fuel consumption (SFC) decreased by 7.23% with 100 ppm Fe₃O₄ addition.
- Combustion parameters like cylinder pressure (CP) and heat release rate (HRR) increased by 4.46% and 24.21%, respectively.
- Significant reductions in CO (23.92%), HC (22.42%), NOx (5.38%), and smoke emissions (3.61%) were observed with the SME + 100Fe blend.
- The ML model demonstrated high prediction accuracy (R values from 0.9973 to 0.99995).
Conclusions:
- Fe₃O₄ nano-additives in SME blends demonstrably improve diesel engine performance and reduce emissions.
- The integration of machine learning provides an accurate and data-efficient method for modeling fuel performance.
- This approach shows significant potential for advancing sustainable fuel technologies.
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