Experimental and machine learning-based analysis of peanut drying using solar Photovoltaic-Thermal (PVT) collectors with forced convection and latent heat storage

This paper provides a detailed experimental and machine learning analysis of peanut drying with a hybrid solar photovoltaic-thermal (PVT) collector system. Four drying techniques were tested: open sun drying, natural convection, forced convection, and forced convection combined with a paraffin wax-based Phase Change Material (PCM) to store latent heat. Each condition involved drying 5 kg of wet peanuts with an initial moisture content of 40 %. The drying processes were simulated using three machine learning models Gaussian Process Regression (GPR), Radial Basis Function (RBF), and Multilayer Perceptron (MLP) to predict moisture removal and drying performance. Model accuracy was measured by RMSE, MAPE, and R2. The results show that forced convection with PCM was the most successful approach, lowering drying time from around 42 h (open sun drying) to only 18 h and attaining the best drying efficiency of 68.23 %. The greatest electrical efficiency was 11.24 %, while the collector efficiency was 21.89 %. The RBF network outperformed the GPR and MLP models in moisture removal and drying performance, with R2 values of 0.97 and 0.98, respectively. This study concludes that integrating PCM with forced convection in a PVT-dryer system, together with powerful machine learning predictions, provides a highly efficient and sustainable approach for agricultural product preservation that outperforms existing techniques.