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

Ghanbari, M. and Najafi, G. and Ghobadian, B. and Yusaf, T. and Carlucci, A. P. and Kiani Deh Kiani, M. (2017) Performance and emission characteristics of a CI engine using nano particles additives in biodiesel-diesel blends and modeling with GP approach. Fuel, 202. pp. 699-716. ISSN 0016-2361


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 2% and brake specific fuel consumption (bsfc) was decreased 7.08% compared to the net diesel fuel. CO2 emission increased maximum 17.03% and CO emission in a biodiesel-diesel fuel with nano-particles was lower significantly (25.17%) compared to pure diesel fuel. UHC emission with silver nano-diesel-biodiesel blended fuel decreased (28.56%) while with fuels that contains CNT nano particles increased maximum 14.21%. With adding nano particles to the blended fuels, NOx increased 25.32% 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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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Date Deposited: 07 Jun 2022 23:59
Last Modified: 09 Jun 2022 00:37
Uncontrolled Keywords: Nano additives, Diesel-biodiesel blends, Ultrasonic, Genetic programming
Fields of Research (2008): 09 Engineering > 0902 Automotive Engineering > 090201 Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
Fields of Research (2020): 40 ENGINEERING > 4002 Automotive engineering > 400201 Automotive combustion and fuel engineering
Identification Number or DOI: https://doi.org/10.1016/j.fuel.2017.04.117
URI: http://eprints.usq.edu.au/id/eprint/48902

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