Performance and emission characteristics of a CI engine using nano particles additives in biodiesel-diesel blends and modeling with GP approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Ghanbari:2017:Fuel,

• author = "M. Ghanbari and G. Najafi and B. Ghobadian and T. Yusaf and A. P. Carlucci and M. Kiani Deh Kiani",
• title = "Performance and emission characteristics of a {CI} engine using nano particles additives in biodiesel-diesel blends and modeling with {GP} approach",
• journal = "Fuel",
• year = "2017",
• volume = "202",
• pages = "699--716",
• month = "15 " # aug,
• keywords = "genetic algorithms, genetic programming, Nano additives, Diesel-biodiesel blends, Ultrasonic",
• ISSN = "0016-2361",
• URL = "http://www.sciencedirect.com/science/article/pii/S0016236117305380",
• DOI = "doi:10.1016/j.fuel.2017.04.117",
• abstract = "The performance and the exhaust emissions of a diesel engine operating on nano-diesel-biodiesel blended fuels has been investigated. Multi wall carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) were produced and added as additive to the biodiesel-diesel blended fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel and biodiesel fuels, increased diesel engine performance variables including engine power and torque output up to 2percent and brake specific fuel consumption (bsfc) was decreased 7.08percent compared to the net diesel fuel. CO2 emission increased maximum 17.03percent and CO emission in a biodiesel-diesel fuel with nano-particles was lower significantly (25.17percent) compared to pure diesel fuel. UHC emission with silver nano-diesel-biodiesel blended fuel decreased (28.56percent) while with fuels that contains CNT nano particles increased maximum 14.21percent. With adding nano particles to the blended fuels, NOx increased 25.32percent compared to the net diesel fuel. This study also presents genetic programming (GP) based model to predict the performance and emission parameters of a CI engine in terms of nano-fuels and engine speed. Experimental studies were completed to obtain training and testing data. The optimum models were selected according to statistical criteria of root mean square error (RMSE) and coefficient of determination (R2). It was observed that the GP model can predict engine performance and emission parameters with correlation coefficient (R2) in the range of 0.93-1 and RMSE was found to be near zero. The simulation results demonstrated that GP model is a good tool to predict the CI engine performance and emission parameters.",
• notes = "Also known as \cite{GHANBARI2017699}",

}

Genetic Programming entries for M Ghanbari GholamHasan Najafi Barat Ghobadian Talal Yusaf Antonio Paolo Carlucci Mostafa Kiani Deh Kiani

Citations