Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/33311
Title: Spline regression modelling of PTO performance of tractor fuelled with different biodiesels
Authors: Aybek, Ali
Üçok, Serdar
Üçgül, Mustafa
Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.
0000-0001-8528-7490
Aslan, Selçuk
T-2708-2018
7006604572
Keywords: Agriculture
Biodiesel
Performance characteristics
Power take-off
Tractor
Spline modelling
Diesel-engine
Blends
Oil
Issue Date: May-2017
Publisher: Chinese Acad Agricultural Engineering
Citation: Aybek, A. vd. (2017). ''Spline regression modelling of PTO performance of tractor fuelled with different biodiesels''. International Journal of Agricultural and Biological Engineering, 10(3), 115-120.
Abstract: The objective of this study was to investigate the possibility of fitting spline regression models for power take-off (PTO) performance characteristics of an agricultural tractor tested with four different fuels, including diesel fuel (B0) and three biodiesel blends made of canola oil (B10: 10% biodiesel + 90% petro-diesel blend; B20: 20% biodiesel + 90% petro-diesel blend; B30: 30% biodiesel + 90% petro-diesel blend). The performance characteristics evaluated were PTO power, engine torque, engine fuel consumption, and specific fuel consumption. Due to sharp slopes in measured quantities around the nominal engine speed (2200 r/min), compared to the standard regression method, the spline regression models suited well to the experimental data with coefficient of determination R-2= 0.99 for PTO power and engine torque. R-2 varied between 0.97 and 0.99 for fuel consumption and 0.91 and 0.95 for specific fuel consumption. The weaker correlation found for specific fuel consumption could be attributed to profound fluctuations in measured data causing atypical pattern in the corresponding graphs around the nominal engine speed. It was concluded that splines were useful to accurately predict the measured PTO power and engine torque. Neither standard methods nor splines were sufficient to obtain very good regression models for specific fuel consumption.
URI: https://doi.org/10.3965/j.ijabe.20171003.3220
http://hdl.handle.net/11452/33311
ISSN: 1934-6344
1934-6352
Appears in Collections:Scopus
Web of Science

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