This study deals with artificial neural network (ANN) modeling a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of engine. To acquire data for training and testing the proposed ANN, two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results reveal that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model based on standard Back-Propagation algorithm for the engine was developed. Multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) were 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values by the model was obtained as 0.0004. Keywords

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Abstract:A comprehensive combustion analysis has been conducted to evaluate the performance of a commercial DI engine, water cooled two cylinders, in-line, naturally aspirated, RD270 Ruggerini diesel engine using waste vegetable cooking oil as an alternative fuel. In order to compare the brake power and the torques values of the engine, it has been tested under same operating conditions with diesel fuel and waste cooking biodiesel fuel blends. The results were found to be very comparable. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The total sulfur content of the produced biodiesel fuel was 18 ppm which is 28 times lesser than the existing diesel fuel sulfur content used in the diesel vehicles operating in Tehran city (500 ppm). The maximum power and torque produced using diesel fuel was 18.2 kW and 64.2 Nm at 3200 and 2400 rpm respectively. By adding 20% of waste vegetable oil methyl ester, it was noticed that the maximum power and torque increased by 2.7 and 2.9% respectively, also the concentration of the CO and HC emissions have significantly decreased when biodiesel was used. An artificial neural network (ANN) was developed based on the collected data of this work. Multi layer perceptron network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. The results showed that the training algorithm of Back Propagation was sufficient enough in predicting the engine torque, specific fuel consumption and exhaust gas components for different engine speeds and different fuel blends ratios. It was found that the R 2 (R: the coefficient of determination) values are 0.99994, 1, 1 and 0.99998 for the engine torque, specific fuel consumption, CO and HC emissions, respectively.

In this research, the effect of ethanol as gasoline additive has been investigated into metal corrosion of a fuel delivery system. Corrosion tests have been performed in gasoline with different percentages of ethanol, using weight loss (immersion test) and electrochemical impedance stereoscopy (EIS) procedures. Surface of test specimens were checked by scanning electron microscopy (SEM) after 144 days of immersion in test solution. Also corroded components were analyzed using energy-dispersive X-ray analysis (EDAX) method. Test results, investigations, and analyses, altogether show more corrosion with the increase in ethanol percentage and water content in gasoline. Test results show that among different materials in fuel delivery system, aluminum alloys and hard soldering alloys have less corrosion than the others. Also chloride and sulfide were recognized as the main compound of corrosion products; therefore, the control of these two elements in fuel delivery system is a must in case of using ethanol as fuel additive in near future.

A new method has been introduced to predict the power of important classes of energetic compounds including nitroaromatics, acyclic and cyclic nitramines, nitrate esters and nitroaliphatics. In this method, the predicted specific impulse and the corrected heat of detonation on the basis of H2O‐CO2 arbitrary decomposition, have been used to calculate the power of an explosive with the molecular formula CaHbNcOd as determined by the Trauzl test. The predicted results show good agreement with respect to the measured values for both pure and mixture of explosives. The calculated volume expansions of pure energetic compounds have a root mean square (rms) deviation of 33 cm3 from 73 measured values (corresponding to 58 molecules). For 9 explosive mixtures, the predicted volume expansions have an rms deviation of 39 cm3 from the experimental results.