Performance and emission characteristics of a CI engine using nano particles additives in biodiesel-diesel blends and modeling with GP approach
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- @Article{Ghanbari:2017:Fuel,
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author = "M. Ghanbari and G. Najafi and B. Ghobadian and
T. Yusaf and A. P. Carlucci and M. Kiani Deh Kiani",
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title = "Performance and emission characteristics of a {CI}
engine using nano particles additives in
biodiesel-diesel blends and modeling with {GP}
approach",
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journal = "Fuel",
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year = "2017",
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volume = "202",
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pages = "699--716",
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month = "15 " # aug,
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keywords = "genetic algorithms, genetic programming, Nano
additives, Diesel-biodiesel blends, Ultrasonic",
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ISSN = "0016-2361",
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URL = "https://iris.unisalento.it/bitstream/11587/414004/5/PROOFFUEL2017.pdf",
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URL = "http://www.sciencedirect.com/science/article/pii/S0016236117305380",
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DOI = "doi:10.1016/j.fuel.2017.04.117",
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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.",
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notes = "Also known as \cite{GHANBARI2017699}",
- }
Genetic Programming entries for
Mani Ghanbari
GholamHasan Najafi
Barat Ghobadian
Talal Yusaf
Antonio Paolo Carlucci
Mostafa Kiani Deh Kiani
Citations