Abstract:The current study aims to investigate and compare the effects of waste plastic oil blended with n-butanol on the characteristics of diesel engines and exhaust gas emissions. Waste plastic oil produced by the pyrolysis process was blended with n-butanol at 5%, 10%, and 15% by volume. Experiments were conducted on a four-stroke, four-cylinder, water-cooled, direct injection diesel engine with a variation of five engine loads, while the engine’s speed was fixed at 2500 rpm. The experimental results showed that th… Show more
“…In our previous study, 44 which examined the engine performance and emissions resulting from the blending of waste plastic oil produced through the pyrolysis process, with n -butanol at the same diethyl ether ratios as used in our present study (5%, 10% and 15%), optimal outcomes in terms of maximum BTE and minimum NO x were obtained through the NSGA-II multi-objective optimization. These optimal results are depicted as a Pareto frontier in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…The results, presented in Tables 6 and 7, were selected as input factors for the optimization process. Considering the number of objectives to be optimized, the nondominated sorting genetic algorithm II (NSGA-II), a widely adopted optimization algorithm in the eld of energy, [44][45][46] was chosen. The ow operation chart depicted in Fig.…”
Section: Optimal Solution For Engine Performance and Emission Via Mul...mentioning
The combined NSGA-II algorithm and GRNNs model accurately predicted the multi-objective function, enabling identification of the optimal DEE percentage in WPO and engine operating condition to achieve maximum engine efficiency and minimum emissions.
“…In our previous study, 44 which examined the engine performance and emissions resulting from the blending of waste plastic oil produced through the pyrolysis process, with n -butanol at the same diethyl ether ratios as used in our present study (5%, 10% and 15%), optimal outcomes in terms of maximum BTE and minimum NO x were obtained through the NSGA-II multi-objective optimization. These optimal results are depicted as a Pareto frontier in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…The results, presented in Tables 6 and 7, were selected as input factors for the optimization process. Considering the number of objectives to be optimized, the nondominated sorting genetic algorithm II (NSGA-II), a widely adopted optimization algorithm in the eld of energy, [44][45][46] was chosen. The ow operation chart depicted in Fig.…”
Section: Optimal Solution For Engine Performance and Emission Via Mul...mentioning
The combined NSGA-II algorithm and GRNNs model accurately predicted the multi-objective function, enabling identification of the optimal DEE percentage in WPO and engine operating condition to achieve maximum engine efficiency and minimum emissions.
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