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This present investigation focuses on prediction of engine responses of a single cylinder CI engine powered by bio-diesohol (diesel-palm biodiesel-ethanol) blends using RSM (response surface methodology) and ANN (artificial neural network) model. RSM combined with multi-level general full factorial design (FFD) is used for the prediction of brake thermal efficiency (BTE), brake specific fuel consumption (BSEC), and nitrogen oxides (NOx). The engine experimental data is trained in ANN model using Levenberg-Marquardt back propagation training algorithm with logistic-sigmoid activation function. Different statistical measures are calculated to quantify the errors and correlations of the predicted models. Comparatively lower prediction error and higher correlation have been observed from the ANN model compared to RSM. The range of overall mean square error (MSE) and correlation coefficient are found (0.0003–0.00059) & (0.99403–0.998) and (0.00019–0.00035) & (0.99943–0.99971) from RSM and ANN model respectively. The range of overall mean absolute percentage error (MAPE) from ANN model (3.13–4.55%) is found lower compared to RSM model (3.97–6.6%). Thereafter, RSM and ANN predicted responses are introduced in fuzzy logic system for the optimization of engine operating parameters. At 100% load, the D75B20E5 (75% diesel + 20% palm biodiesel + 5% ethanol) blend has been found best for the optimization of BTE, BSEC and NOx emission. Finally, after the confirmation test, it has been revealed that the performance of D75B20E5 blend is as comparable to diesel.

The present world faces the crisis of fossil fuels as it will no longer sustain in the future, gives a knowledge to look on waste resources, where waste rice and neem seed are utilizable for the production of biofuels by the process of fermentation and transesterification. In this paper, the physiochemical properties of the produced biofuels are examined. The 5% rice wine alcohol is used as an additive with neem methyl ester-diesel blend to lower the viscosity and flash point for the better performance in the single cylinder compression ignition engine. Different blends as D90B5RW5, D85B10RW5, D75B20RW5, and D65B30RW5 are prepared and examined experimentally by varying the engine loads (0–100%) at constant engine speed (1500 rpm). Experimental results showed the reduction of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbon (HC) and smoke opacity, however oxide of nitrogen (NOx) is found higher. In the performance, D75B20RW5 shows 8% higher brake thermal efficiency and 3.33% lower brake specific fuel consumption compared to diesel. While in the combustion the peak cylinder pressure and net heat release rate are seen to be higher for biodiesel blends with additive.