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

Ali Alahmer, Hussein Alahmer, Ahmed Handam and Hegazy Rezk
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Ali Alahmer: Department of Mechanical Engineering, Faculty of Engineering, Tafila Technical University, Tafila 66110, Jordan
Hussein Alahmer: Department of Automated Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan
Ahmed Handam: Renewable Energy Engineering Department, Faculty of Engineering, Amman Arab University, Amman 11953, Jordan
Hegazy Rezk: College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, AI-Kharj 16278, Saudi Arabia

Sustainability, 2022, vol. 14, issue 3, 1-32

Abstract: 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 (CO 2 ), 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.

Keywords: pollutant emissions; noise emissions; grey wolf optimization; polynomial regression; diesel engine; vegetable oil; biodiesel (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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