Fast depletion of fossil fuel resources forces the extensive research on the alternative fuels. Vegetable oils edible or non edible can be a better substitute for the petroleum diesel. Karanja, a non edible oil can be a potential source to replace the diesel fuel. To investigate the feasibility of Karanja oil as an alternative diesel fuel, its biodiesel was prepared through the transesterification process. The Biodiesel was then subjected to performance and emission tests in order to assess its actual performance, when used as a diesel engine fuel. The data generated for the 20, 50 and 100 percent blended biodiesel were compared with base line data generated for neat diesel fuel. Result showed that the Biodiesel and its blend showed lower thermal efficiency. Emission of Carbon monoxide, unburned Hydrocarbon and smoke was found to be reduced where as oxides of nitrogen was higher with biodiesel and its blends. Keywords: alternate Diesel fuel; Biodiesel; Karanja oil methyl ester; performance and emission

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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