Modeling and optimization of performance and emission parameters of a diesel engine: A comparative evaluation between date seed oil biodiesel produced via three different heating systems

Hassanian Abdolkarim Allami,Mohammad Tabasizadeh,Abbas Rohani,Hamed Nayebzadeh, Abdolali Farzad,Marziyeh Hoseinpour

ENERGY CONVERSION AND MANAGEMENT(2023)

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摘要
In this study, the performance of a diesel engine fueled with date seed oil (DSO) biodiesel, produced under microwave, ultrasonic and conventional heating system, is investigated by experimental and statistical methods. The multi-linear regressions (MLR) were developed using the percentage of biodiesel in fuel and the engine speeds (as independent variables) and the power, torque, and SFC and CO, CO2, HC, O2, and NOx emissions (as dependent variables). Then, the best-fitted regression model was used for analyzing the response surface of the performance parameters, and finding the optimal values of biodiesel blending percentages using genetic algo-rithm (GA) approaches at different engine speeds. Finally, the engine performance parameters were assessed when the engine is fueled with these optimal blending for three types of biodiesel. The results showed that technologies used in the biodiesel production would have a noticeable effect on the response of engine perfor-mance, however, the response surface of exhaust emission follows roughly the same trend with different slope and curvature. Moreover, optimization indicated that the optimal percentage of DSO biodiesel at different speeds was between 11%-%13, %13-%19, and %17-%20 for microwave, ultrasonic, and conventional production method, respectively. Subsequently, when the engine is fueled with the optimal blending of biodiesel at every engine speed, biodiesel produced via an alternative heating system can significantly improve the engine per-formance and reduce exhaust emissions, especially HC, CO, and NOx by microwave-assisted produced biodiesel.
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关键词
Date seed oil biodiesel,Conventional heating system,Microwave,Ultrasonic,Multi linear regression,Genetic algorithm
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