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Machine learning based prediction of diesel engine emissions and performance using hemp biodiesel enriched with nano
Anchupogu Praveen1, Krupakaran Radhakrishnan Lawrence2, R Satya Mehar3
1Department of Mechanical Engineering, Bapatla Engineering College, Bapatla, Andhra Pradesh, India.
Scientific Reports
|June 1, 2026
Summary
This study explored hemp biodiesel with nano additives for diesel engines. Hemp biodiesel blends with MWCNT nano additives improved engine efficiency and reduced emissions, showing promise for cleaner fuel alternatives.
Area of Science:
- * Combustion science and engine technology.
- * Materials science, focusing on nano additives.
- * Computational modeling and machine learning applications.
Background:
- * Growing demand for sustainable and cleaner energy sources.
- * Need for improved performance and reduced emissions in diesel engines.
- * Potential of biodiesel and nano additives as alternative fuels.
Purpose of the Study:
- * To experimentally and computationally evaluate diesel engine performance using hemp biodiesel with nano additives (Al2O3, TiO2, MWCNT).
- * To characterize hemp biodiesel and nano additives using advanced analytical techniques.
- * To develop predictive models for engine parameters and emissions.
Main Methods:
- * Experimental testing of hemp biodiesel blends with varying nano additive concentrations at different engine loads.
- * Characterization of fuels and additives using Fourier-Transform Infrared Spectroscopy (FTIR), Gas Chromatography-Mass Spectrometry (GC-MS), Scanning Electron Microscopy (SEM), and X-ray Diffraction (XRD).
- * Development and validation of predictive models using Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms.
Main Results:
- * HMBD20 + MWCN100 fuel demonstrated higher Brake Thermal Efficiency (BTE) and lower Brake Specific Fuel Consumption (BSFC).
- * CO and HC emissions were reduced with HMBD20 + TINP100, while NOx emissions increased at peak load.
- * DT and ANN models achieved high prediction accuracy (R² > 0.98) for BTE, BSFC, CO, HC, Smoke, and NOx.
- * Optimal performance was achieved with HMBD20 + 100 ppm MWCNT at 75% load, showing improved efficiency and reduced emissions.
Conclusions:
- * Nano additives significantly enhance the performance and reduce emissions of hemp biodiesel in diesel engines.
- * Machine learning models accurately predict engine parameters and emissions, aiding in optimization.
- * Hemp biodiesel enriched with nano additives presents a viable and cleaner alternative for diesel engine applications.
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