A two stroke free piston engine’s performance and exhaust emission using artificial neural networks

The performance and exhaust emissions of a Free piston linear engine (FPLE) were ascertained for various equivalence ratios (0.7, 0.8, 0.9, 1.0, 1.1 and 1.2). After that, the usability of Artificial neural networks (ANNs) in case of FPLE has been tested. Actually, the aim was to examine the best suited operational condition of, and show the possibility of using ANN, for this kind of engine technology. We first interrogated the thermal efficiency, generated power, total heat release rate, indicated mean effective pressure, exhaust gas temperature, and exhaust emissions such as CO, CO₂, NO_x and O₂ for the chosen range of equivalence ratios and then gathered experimental data in order to train and test an artificial neural network model for prediction. The experimental results showed that, running the engine on the slightly lean side stoichiometrically, can fulfil the goals of higher engine performance and lower emissions. We used the back propagation learning algorithm for ANN and observed that the correlation coefficients (R) vary between 0.990-0.999, Mean absolute percentage error (MAPE) vary between 0.885-5.9%, and coefficient of determination (R²) vary between 0.937-0.999; which showed a welldefined relationship between the predicted and experimental values.