Comparative Analysis of Engine Performance, Noise Emissions, and Energy Efficiency of Euro Diesel and Safflower Methyl Ester Fuels Using Artificial Neural Networks

Aydın, Fatih; Cayan, Busra Nur

doi:10.24200/sci.2025.65872.9710

Comparative Analysis of Engine Performance, Noise Emissions, and Energy Efficiency of Euro Diesel and Safflower Methyl Ester Fuels Using Artificial Neural Networks

Articles in Press

Document Type : Research Article

Authors

Fatih Aydın ¹

Busra Nur Cayan ²

¹ Faculty of Engineering, Department of Energy Systems Engineering, Necmettin Erbakan University, Konya, Turkey

² The Graduate School of Natural and Applied Sciences, Necmettin Erbakan University, Konya, Turkey.

10.24200/sci.2025.65872.9710

Abstract

In this study, energy analysis, engine performance, and noise emission tests of Euro diesel and safflower methyl ester fuels were conducted on a diesel engine. The experiments were conducted independently for each fuel type across engine speeds ranging from 1000 to 2400 rpm, and the physicochemical properties of the fuels were characterized and evaluated through engine testing. Noise emission values were recorded from four different points around the engine at a distance of one meter and were compared with those of reference diesel fuel. According to the test results, the most suitable fuel type was determined based on engine performance. noise emission and energy analysis. In this study, modeling was performed using artificial neural networks (ANNs) based on experimentally obtained data, and the noise emission characteristics of B₁₀₀ and D¹⁰⁰ fuels were analyzed. Both raw and normalized datasets were evaluated to assess the predictive accuracy of the models. It was concluded that the predictive success was closely associated with the choice of training algorithms and transfer functions utilized. The findings highlight that selecting suitable models and algorithms tailored to the structure of the dataset plays a critical role in enhancing prediction accuracy.

Keywords

Safflower Methyl Ester

Energy Analysis

Engine Performance

Noise Emission Analysis

Artificial Neural Networks

Subjects