Advancements in computational tools for indirect solar drying systems: a comprehensive review

Solar drying has emerged as a sustainable and energy-efficient alternative to traditional open sun drying, offering faster drying rates, reduced contamination, and improved product quality. To enhance the design and performance of indirect solar dryers (ISDs), computational modeling has become an essential tool. This review systematically analyzes 25 computational tools, including ANSYS Fluent, MATLAB, COMSOL Multiphysics, TRNSYS, and RETScreen for their role in simulating, optimizing, and predicting ISD performance under diverse operating conditions. Quantitative findings highlight that ANSYS Fluent enables thermal efficiency improvements of up to 71.3 %, while MATLAB simulations predict drying durations between 3.74 and 7.45 h with an uncertainty of ±5 %. COMSOL Multiphysics demonstrates predictive accuracy with mean absolute percentage errors of 5.3 % (temperature), 3.7 % (moisture content), and 6.3 % (air velocity). Despite these advancements, critical research gaps persist, including the absence of standardized modeling frameworks, insufficient attention to product quality retention, and limited integration of economic and environmental parameters. This review’s novelty lies in its holistic comparison of computational tools and its emphasis on emerging technologies, such as Artificial Intelligence (AI), Machine Learning (ML), and Artificial Neural Networks (ANN) for adaptive control and real-time performance optimization, setting a foundation for future advancements in ISD systems.