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. 2025 Jul 21;15(1):26457.
doi: 10.1038/s41598-025-04033-0.

Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights

V S Shaisundaram  1 , P V Elumalai  2 , S Padmanabhan  3 , U Nalini Ramachandran  4 , Abhishek Kumar Tripathi  5 , Cui Yaping  6 , B Nagaraj Goud  7 , S Prabhakar  8
Affiliations

Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights

V S Shaisundaram et al. Sci Rep. .

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.

Keywords: Energy efficiency; Gradient boosting regression; Hermal performance prediction; Momordica seed biodiesel; Multiple linear regression; Partially stabilized zirconia; Random forest regression.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Experimental setup of Engine.
Fig. 2
Fig. 2
SEM image of thermal barrier coating.
Fig. 3
Fig. 3
Effect of MCSO’s Brake Thermal Efficiency on Coated Engine.
Fig. 4
Fig. 4
Effect of MCSO’s Specific Fuel Consumption on Coated Engine.
Fig. 5
Fig. 5
Effect of MCSO’s Carbon Monoxide on Coated Engine.
Fig. 6
Fig. 6
Effect of MCSO’s Carbon Dioxide on Coated Engine.
Fig. 7
Fig. 7
Effect of MCSO’s Hydrocarbon on Coated Engine.
Fig. 8
Fig. 8
Effect of MCSO’s Oxides of Nitrogen on Coated Engine.
Fig. 9
Fig. 9
Effect of MCSO’s Smoke on Coated Engine.
Fig. 10
Fig. 10
Ternary plot for data distribution.
Fig. 11
Fig. 11
Pearson correlation plot.
Fig. 12
Fig. 12
Comparison of experimental and predicted values for the best-performing machine learning models: (a) Random Forest (RF) for Brake Thermal Efficiency (BTE) and Specific Fuel Consumption (SFC), (b) Multiple Linear Regression (MLR) for Hydrocarbon (HC) emissions and Smoke levels, (c) Gradient Boosting Regression (GBR) for Carbon Monoxide (CO) emissions.
See this image and copyright information in PMC

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