Environmental Assessment of a Diesel Engine Fueled with Various Biodiesel Blends: Polynomial Regression and Grey Wolf Optimization

A series of tests were carried out to assess the environmental effects of biodiesel blends made of different vegetable oil, such as corn, sunflower, and palm, on exhaust and noise diesel engine emissions. Biodiesel blends with 20% vegetable oil biodiesel and 80% diesel fuel by volume were developed. The tests were conducted in a stationary diesel engine test bed consisting of a single-cylinder, four-stroke, and direct injection engine at variable engine speed. A prediction framework in terms of polynomial regression (PR) was first adopted to determine the correlation between the independent variables (engine speed, fuel type) and the dependent variables (exhaust emissions, noise level, and brake thermal efficiency). After that, a regression model was optimized by the grey wolf optimization (GWO) algorithm to update the current positions of the population in the discrete searching space, resulting in the optimal engine speed and fuel type for lower exhaust and noise emissions and maximizing engine performance. The following conclusions were drawn from the experimental and optimization results: in general, the emissions of unburned hydrocarbon (UHC), carbon dioxide (CO2), and carbon monoxide (CO) from all the different types of biodiesel blends were lower than those of diesel fuel. In contrast, the concentration of nitrogen oxides (NOx) emitted by all the types of biodiesel blends increased. The noise level produced by all the forms of biodiesel, especially palm biodiesel fuel, was lowered when compared to pure diesel. All the tested fuels had a high noise level in the middle frequency band, at 75% engine load, and high engine speeds. On average, the proposed PR-GWO model exhibited remarkable predictive reliability, with a high square of correlation coefficient (R-2) of 0.9823 and a low root mean square error (RMSE) of 0.0177. Finally, the proposed model achieved superior outcomes, which may be utilized to predict and maximize engine performance and minimize exhaust and noise emissions.

Automated summary

The environmental effects of biodiesel blends made of different vegetable oils on diesel engine emissions and noise were assessed through a series of tests. A prediction framework and a regression model optimized by the grey wolf optimization algorithm were developed to determine the optimal engine speed and fuel type for reducing emissions and maximizing engine performance. The experimental and optimization results showed that biodiesel blends generally had lower emissions of unburned hydrocarbon, carbon dioxide, and carbon monoxide compared to diesel fuel, but higher nitrogen oxide emissions. The noise level produced by biodiesel, especially palm biodiesel, was lower than that of pure diesel. The proposed PR-GWO model exhibited remarkable predictive reliability and could be used to predict and maximize engine performance while minimizing exhaust and noise emissions.