The application of artificial neural networks to predict the performance of solar chimney filled with phase change materials

In this study, the effects of phase change material on a solar chimney were studied both experimentally and numerically. Paraffin wax, as a phase change material, was installed inside the small constructed solar chimney in the campus of the University of Tehran with 1.5 m collector radius, 3 m chimney height and 20 cm chimney diameter. The goal of the paper was to illustrate the application of a properly constructed Artificial Neural Network model to predict the performance of the solar chimney, filled with phase change material. The parameters TA and Tair were reported as the input and output data, respectively. Then, a multi-layer neural network using MATLAB software was employed to find a relationship between the inputs and outputs. The process had eight inputs and four outputs, and it was shown that the trained network was well-suited for the problem modeling. Comparing the results with the experimental data indicates the accuracy of the analysis. As a result of the research, the correlation between the predicted values by the network and the experiments, for all outputs, was more than 99%. Moreover, the average value of the relative errors for all outputs were less than 3%.