Experimental Study and Prediction of Performance and Emission in an SI Engine Using Alternative Fuel with Artificial Neural Network
In this study, firstly, the effect of compression ratio (CR), air excess coefficient (AEC) and ignition timing (IGT) on the performance and exhaust emissions in a spark ignition (SI) engine fueled with pure ethanol was experimentally investigated. The engine tests were conducted for four varied CR, three types of AEC and three different IGT at 2400 rpm, and the performance and exhaust emission of the engine were recorded. The results obtained from the pure ethanol were compared with those of the gasoline. Secondly, the performance and exhaust emission of SI engine for the same test conditions were estimated with a backpropagation artificial neural network (ANN) model. The ANN model was created using the C# programming language. The ANN model was trained with the data obtained from the experimental study. The engine torque, brake specific fuel consumption (BSFC), hydrocarbon (HC) emission and carbon dioxide (CO2) emission were estimated by the backpropagation ANN model. When the experimental data were compared with the predicted values, it was seen that the error percentages of the difference were acceptable. When the results were generally evaluated, it was observed that was improved of the performance and exhaust emissions as using pure ethanol at lowering the IGT and increasing the CR. In addition, it is seen that the ANN model can be used to estimate the performance and emissions of an SI engine using alternative fuels.
Artificial neural network; Ethanol; Engine performance; Exhaust emissions.
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