Experimental appraisal & dual efficiency optimization of a modified indirect solar dryer: Heat & mass transfer analysis with a hybrid ANN approach

Abstract

The dehydration of food and agricultural products involves complex heat and mass transfer processes, necessitating efficient drying techniques. This study evaluates the performance of a modified Indirect Solar Dryer (ISD) with a double-glazed corrugated collector and a shelf-type drying chamber. Experiments conducted on grapes (initial moisture content: 78% w.b.) demonstrate that ISD significantly outperforms Open Sun Drying (OSD), achieving higher peak efficiencies (50%–70% vs. 40%–60%) and better moisture removal (final moisture content: 0.10–0.15 vs. 0.30–0.40 for OSD). To predict drying kinetics, various empirical models were analyzed, with the Midilli et al. model providing the best statistical fit. To further enhance ISD performance, this study employs hybrid Artificial Neural Network (ANN) models optimized using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). Among these, ANN-GWO demonstrated the highest predictive accuracy. The models were validated with experimental data, and sensitivity analyses assessed the impact of key input parameters. These findings contribute to optimizing solar drying systems for improved energy efficiency and sustainability in agricultural applications. Future research should explore advanced thermal energy storage solutions to enhance drying performance under varying environmental conditions.