Experimental investigation and machine learning modeling of PCM-integrated solar dryers for red chilli drying

This study presents a hybrid approach for enhancing solar drying performance through the integration of phase change material (PCM)-based thermal energy storage and machine learning-driven predictive modeling. Conventional solar drying systems are often limited by intermittent solar radiation and temperature fluctuations, resulting in non-uniform drying and reduced efficiency. To overcome these limitations, PCM is employed to store excess thermal energy during peak solar hours and release it during off-sun periods, thereby stabilizing the drying environment and extending operational duration. Experimental investigations on red chilli drying demonstrate a clear performance improvement across system configurations, where drying efficiency increased from 41.76% under natural convection to 60.28% with forced convection, further rising to 70.04% with PCM integration and reaching 77.12% with fin-enhanced PCM trays, representing an overall improvement of approximately 85% compared to the baseline system. In addition, inter-tray moisture variation was reduced from 15 to 20% (without PCM) to 7–8% with PCM and further to 3–5% with finned PCM, indicating significantly improved temperature uniformity and drying consistency. The incorporation of PCM also enabled an extension of drying time by 2–3 h beyond peak solar availability. Machine learning models were applied to capture the non-linear drying behaviour, with the Support Vector Machine (SVM) achieving superior predictive performance (R2 = 0.88) with reduced error metrics compared to Decision Tree and K-Nearest Neighbor models. The novelty of this work lies in the synergistic coupling of latent heat storage and data-driven modeling to simultaneously enhance thermal efficiency and predictive capability, providing a robust framework for optimizing solar drying systems. This integrated approach offers a significant advancement toward energy-efficient and intelligent agricultural drying technologies.