Artificial neural network, ANN-PSO and ANN-ICA for modelling the Stirling engine

Nowadays, shortage of fossil fuels resources, especially oil, and also global attention to environmental hazards produced by the internal combustion process have caused extensive researches on the development of renewable energy engine technology. Among all kinds of renewable resources, solar energy Stirling engines have their own special situation for energy generation with lower pollutants and sustainable sources. The Stirling engine is an external combustion engine that uses any external heat source to generate mechanical power. Various parameters affect the performance of the Stirling engine. In this study, artificial neural network (ANN) was applied to estimate the power and torque values obtained from a Stirling heat engine (Philips M102C engine). It employs the Levenberg-Marquardt algorithm for training ANN with back propagation network for estimating the power and torque of the Stirling heat engine. The performances of the imperialist competitive algorithm (ICA)-ANN and ANN-particle swarm optimisation (PSO) are compared with the performance of the ANN based on mean square error (MSE) and correlation coefficient. PSO and ICAs are applied to determine the initial weights of the neural network. The obtained results indicate that ANN-PSO has a better performance than ICA-ANN and ANN alone; also the MSE for the ANN-PSO is lower as well. Considering the results obtained from this study, there is very good agreement between the output of the testing phase of the ANN-PSO model with experimental data and they are very close to each other.