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In recent years, much research has been carried to find suitable alternative fuel to petroleum products. In the present investigation experimental work has been carried out to analyze the performance and emissions characteristics of a single cylinder compression ignition DI engine fuelled with the blends of mineral diesel and biodiesel at the different injection pressures. The optimal value of the injection pressure was observed as 200 bar in the range of 180 to 220 bar. The performance parameters evaluated were brake thermal efficiency, break specific fuel consumption and the emissions measured were carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), and oxides of nitrogen (NOx). The results of experimental investigation with biodiesel blends with diesel are compared with that of diesel. The results indicated that the CO emissions are slightly less, HC emissions were also observed to be less for B10 and B20, and NOx emissions decreased by 39 % for B10 and 28 % for B20 compared with B100. The brake thermal efficiency of the engine decreased around 6% for all blends in comparison with diesel, and the break specific fuel consumption was slightly more for B10 and B20.

This paper proposes the mathematical modelling using artificial neural network (ANN) for predicting the performance and emission characteristics of spark-ignition (SI) engine using tert butyl alcohol (TBA) gasoline blends. The experiments are performed with a four-stroke three cylinder carburetor type SI engine at three different revolution per minutes such as 1500, 2000, and 2500 with different blends ranging from 0% to 5% and at 10%. Experimental data are used for training an ANN model based on the feed-forward back-propagation approach for predicting the data at 6-9% with the same speeds. Results show that the blending of TBA with gasoline improves the emission characteristics compared with the gasoline. From the experimental testing data, root mean squared-error was found to be 0.9997% with the network 3-1-10. During this study, The ANN model accurately anticipates the performance and emissions of the engine.

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