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Waste cooking oils (WCOs) are renewable and in nature can be directly used as fuel into the compression ignition engines. However, the reduction in brake thermal efficiency and increasing smoke emission and oxides of nitrogen need to be solved. There are more techniques used past researchers to improves the performance and reduced the emissions characteristics of WCO. In this present work, an experimental investigation made on the effect of ethanol on engine's behavior using Waste Cooking oil (WCO) based dual fuel diesel engine. A single-cylinder diesel engine was operated and modified the intake to operate dual fuel mode at the maximum power output of 3.54 kW. Ethanol is introduced as primary fuel into the intake manifold and WCO as pilot fuel. The ethanol energy share (EES) of the total fuel was varied from 5% to 40% with a step of 5%, at fixed engine speed equal to 1500 rpm.

The fuel injection timing is one of the most important operating parameters that affect the atomization, mixture formation and combustion which determines the performance and emissions of a gasoline engine. Optimizing the injection timing will improve the performance of the engine to a large extend. Towards this end artificial neural-network (ANN) technique using Levenberg-Marquardt (LM) training algorithm is used to train and optimize the fuel injection timing of a single cylinder, four-stroke gasoline engine. Experimental studies have been carried out to obtain training as well as test data. For various engine speeds between 700 and 5000 rpm and for different manifold absolute pressures, fuel injection timing was measured by conducting experiments. The experimental data set generated is used to train the neural network to arrive at the optimized performance of the engine.