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• W2623486529 abstract "The depletion of oil reserves as well as environmental pollution resulting from the burning of fossil fuels has led to ongoing research and development of biodiesels in order to substitute fossil fuels. The objective of this study is to investigate the performance and exhaust emissions of a single-cylinder direct injection diesel engine fueled with Jatropa curcas-Ceiba pentandra biodiesel-diesel blends. The Jatropa curcas-Ceiba pentandra biodiesel (J50C50) is produced by mixing the crude oils at an equal volume ratio, followed by degumming, acid-catalyzed esterification, and alkaline-catalyzed transesterification. The B10, B20, B30, B40, and B50 blends are produced by blending 10, 20, 30, 40, and 50 vol% of J50C50 biodiesel with diesel. In general, the engine performance of B10 is close to that of diesel fuel, and facilitates achieving higher engine torque, brake power, and brake thermal efficiency comparable to other blends. Blending J50C50 biodiesel with diesel reduces the carbon dioxide emissions and smoke opacity, but increases the nitrogen oxide and carbon monoxide emissions. In addition, artificial neural network models are developed to predict the engine performance and exhaust emission parameters, and the models give excellent results, whereby the coefficient of determination is more than 98% and the mean absolute percentage error is less than 5% for all parameters. In general, the artificial neural network models give accurate and reliable results, without the need for an explicit mathematical representation. Based on the results, it can be concluded that the J50C50 biodiesel-diesel blends qualify as alternative fuels in diesel engines." @default.
• W2623486529 created "2017-06-15" @default.
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• W2623486529 date "2017-10-01" @default.
• W2623486529 modified "2023-10-05" @default.
• W2623486529 title "Experimental study and prediction of the performance and exhaust emissions of mixed Jatropha curcas-Ceiba pentandra biodiesel blends in diesel engine using artificial neural networks" @default.
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• W2623486529 doi "https://doi.org/10.1016/j.jclepro.2017.06.065" @default.
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