Combustion, performance, and emission behavior of a CI engine fueled with different biodiesels: A modelling, forecasting and experimental study. (1st May 2023) - Nema, Vivek Kumar; Singh, Alok; Chaurasiya, Prem Kumar; Gogoi, Tapan Kumar; Verma, Tikendra Nath; Tiwari, Damodar
Highlights: RSM and ANN model was conducted for all the engine characters. The oxides of nitrogen emissions from the Blend were higher around 20% than that of diesel. The hydrocarbon emissions were lower between 7% and 62% than conventional diesel fuel. ANN and RSM models are in good agreement with the experiment values. Abstract: The current automobile industry is greatly dependent upon petroleum supplies and is moving toward its terminating phases very soon. The depletion of petrol fuel reserves is therefore driving research in the field of alternative fuels. Several biofuels are produced from plants and natural waste, thus having the potential to reduce the level of CO2 in the atmosphere. Therefore, it is renewable and relatively cheaper than conventional fossil fuels since it is abundant and easier to produce. In this present experimental work, four types of biodiesels were investigated: SME (soybean methyl ester 100% by volume), RME (rapeseed methyl ester 100% by volume), B55 (SME 50%, RME 50%), B82 (SME 80%, RME 20%), and diesel fuel. The heat release rate diagram showed combustion period and ignition delay in the Blend 82 at elevated compression ratio compared to diesel fuel. Brake thermal efficiency was on average 4% to 7% lesser than diesel fuel for B82 blend; the value of cylinder pressure was higher in upper-level compression ratio but without knocking, thus ensuring safe combustion. Lower compression ratios gave lower values with maximum reduction of approximately 35% due to reduced temperature and lower brake mean effective pressure. The oxides of nitrogen emissions from the Blend 82 were higher around 20% than that of diesel. Also, the hydrocarbon emissions of the Blend 82 were lower between 7% and 62% than conventional diesel fuel. In addition to optimization and various results connectivity, Response surface analysis (RSM) and then ANN (Artificial neural network) modeling is performed for all the engine characteristics at the end of study.