Artificial neural network approach to investigate the effect of injection pressure and timing on diesel engine fuelled with diestrol Online publication date: Thu, 21-Jan-2016
by G.R. Kannan
International Journal of Oil, Gas and Coal Technology (IJOGCT), Vol. 11, No. 2, 2016
Abstract: The present investigation is an attempt to study the effect of injection pressure and injection timing on performance, emission and combustion characteristics of a diesel engine fuelled with a blend of 30% waste cooking palm oil (WCO) methyl ester, 60% diesel and 10% ethanol (B30D60E10) or diestrol fuel using the artificial neural network (ANN) model. The experimental investigation was carried out on a single cylinder, four strokes DI diesel engine under full load (100%) condition at a constant speed of 1,500 rpm to obtain data for training and testing the proposed ANN model. Among the various networks tested the network with the combination of one hidden layer and 11 neurons within it showed the better correlation coefficient for the prediction of engine performance, emission and combustion characteristics. The ANN model was validated with the test data which was not used for training and found to be very well correlated. [Received: October 21, 2013; Accepted: September 9, 2014]
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