INVESTIGATION OF DIESEL ENGINES PERFORMANCE USING BLENDS OF WASTE VEGETABLE OILS

Document Type : Original Article

Authors

Mechanical Power Engineering Department Faculty of Engineering, Minoufiya University, Shebin El-Kom, Egypt

Abstract

In the present time, the human is facing two essential problems. These problems are the depletion
and the environmental pouution of the fossil oil. Vegetable oils after some chemical treatments
may be used as an alternative to solve partially these problems, since these oils are renewable and
environmental iiiendly. Biofuel extracted l?om vegetable oils is considered as a renewable
alternative diesel engine fuel. In this study, an experimental investigation on the performance of a
direct injection diesel engine is performed. Five blends of waste palm kernel oil (WPKO) with
diesel fuel are used (B5-WPKO, BIO-WPKO, B20-WPKO, B30-WPKO and B40-WPKO).
Another three blends of waste sunflower oil (WSFO) (B20-WSFO, B30-WSFO and B40-WSFO)
with diesel fuel (D!?) are employed. Because of the great effect of the fuel viscosity on atomhtion
process and combustion, the WPKO and WSFO are preheated to approach diesel fuel viscosity.
For this purpose the effect of temperature on the viscosity of WPKO and WSFO is addressed. The
study proves that,using fuel blends (BS-WPKO, BIO-WPKO and B2O-WPKO) these leads to an
increase in the brake thermal efficiency (BTE) and decrease in brake specific fuel consumption
(BSFC) compared with that of DF at different loads. B30-WSFO blend contributes to increase the
BTE compared with that of DF and no significant change is noticed in BSFC. When heating
blends, the BTE increase and BSFC decrease compared with that at normal conditions. Waste
vegetable oils (WVO) blends can be used in diesel engines without any modification.
Mathematical model is developed to simulate the engine behavior when running with WSFO as
biofuel. The model is used as a tool to predict the engine performance at wide range of fuel blends.
The result show fair agreement with experimental data