ANN-Based Performance Modeling of a Solar Air Collector with Varying Absorber Surfaces

Abstract

In this study, an Artificial Neural Network (ANN) approach was employed to predict the outlet air temperature and thermal efficiency of a solar air collector equipped with porous absorber surfaces. The experimental data used for model development were obtained from a custom-built solar air collector whose absorber surface was constructed using porous metallic scourers. Three different absorber surface configurations were tested under varying operating conditions. The dataset included measurements of inlet air temperature, solar irradiance, air mass flow rate, and surface temperatures recorded at four distinct points on the absorber. Corresponding outlet air temperatures and thermal efficiency values were also determined experimentally. ANN models were trained using this dataset, and the prediction results were graphically compared with experimental outcomes for all three surface types. To further evaluate the model's performance, test data were utilized, and the results were assessed using the correlation coefficient (R) and mean squared error (MSE) metrics. The ANN model demonstrated high predictive accuracy, yielding an R value of 0.99987 and an MSE of 0.0901.