Search Results

Biodiesels from various sources form a large number of fuels when blended in various proportions with Diesel. Hence it becomes necessary to analyze, evaluate and select the optimal fuel blend. Since it is extremely tedious to manually test every one of these combinations, this paper introduces an elegant method for the above required analysis by establishing a definite relationship between the fuel properties and engine performance by using Artificial Neural Networks. ANNs are trained to predict engine performance based on fuel properties and to aid in optimizing the ratio with which a Biodiesel has to be blended with Diesel.

Biodiesels, produced from natural and renewable sources such as vegetable oils are most likely to replace petroleum derived diesel as a CI engine fuel in the long term. However it may be intended to use Biodiesels as blends with diesel in standard proportions. This work makes a thorough analysis of the variation of performance and emission characteristics of CI engine with respect to the proportion of Biodiesel in the blend and also attempts to find the optimal blend depending upon properties of the Biodiesel using Artificial Neural Networks (ANNs).There may exist a particular value of the proportion for every Biodiesel for which the best performance and/or lowest emissions are obtained. Artificial Neural Networks (ANNs) are used for this correlation between percentage of Biodiesel in the blend with performance and emissions. Fuel properties are used as an input to generalize the solution so that the same network can be used for different bio-esters.

The rise in the number of automobiles on the road increases the air pollution caused by automotive exhaust. Therefore it becomes important to continually analyze and find new and improved methods to reduce engine emissions. This paper augments the studies carried on engine emissions by establishing a method of prediction of concentration various species in the CI engine exhaust based upon the instantaneous pressure rise in the combustion chamber. The rate of rise of cylinder pressure depends upon the combustion process, which in turn is controlled by various parameters such as injection timing, compression ratio, inlet air properties and most importantly the quality of the fuel used. This rate of pressure rise is assumed to control the rate at which the various species are formed as it depends upon the combustion process itself. In this experiment the fuel alone is changed maintaining all other parameters constant.

Research has shown that the emission characteristics of Biodiesels are different compared to petroleum derived diesel. Though overall emissions of most of the Biodiesels are less than that of diesel, it has been found that many bio-esters have higher NOX emissions. This necessitates the testing of the various blends and selection of the Biodiesel that has acceptable emission characteristics especially with respect to NOX emissions. Due to the sheer number of variations, it becomes very tedious to manually test for every blend and for every Biodiesel. This paper introduces an elegant method of the above required analysis by establishing an Artificial Neural Network (ANN) that is trained to predict engine emission based on fuel properties. Emission data is collected by testing a CI engine under different loads for series of blends with diesel of various Biodiesels and is used to train the network.