MATLAB/Simulink-Based Digital Twin Framework for Real-Time Intelligent Monitoring and Control of Cabinet-Type Solar Drying Processes

The growing demand for sustainable food preservation technologies has accelerated the development of renewable-energy-based drying systems for agricultural and medicinal products. Cabinet-type solar dryers offer an energy-efficient alternative to conventional drying methods; however, their performance is strongly influenced by continuously changing environmental conditions, including solar radiation, ambient temperature, relative humidity, and airflow rate. Although numerous mathematical models have been proposed to describe solar drying processes, most existing approaches are limited to offline simulations and do not provide a unified Digital Twin framework capable of real-time prediction, virtual process monitoring, and intelligent control. To address this limitation, this study proposes a MATLAB/Simulink-based Digital Twin framework that establishes a dynamic virtual representation of a cabinet-type solar dryer using transient heat and mass transfer models. The framework integrates energy and mass conservation equations describing solar heat absorption, convective heat transfer, moisture diffusion, evaporation, and airflow dynamics with virtual sensing to continuously estimate chamber temperature, product moisture content, drying rate, and thermal efficiency under varying operating conditions. Furthermore, a model-based intelligent control strategy is incorporated to adaptively regulate airflow according to the predicted thermal state, thereby maintaining favorable drying conditions and improving process stability. Simulation results demonstrate that the proposed Digital Twin accurately reproduces the nonlinear dynamic behavior of the drying process while providing stable real-time prediction over a wide range of environmental conditions. Compared with conventional simulation-based approaches, the proposed framework improves process observability, predictive decision-making capability, temperature stability, and overall thermal efficiency while reducing unnecessary energy losses. The developed MATLAB/Simulink Digital Twin provides a scalable cyber-physical platform for intelligent solar drying systems and establishes a practical foundation for future integration with IoT-enabled monitoring, cloud-based data management, and Industry 4.0 technologies, enabling more reliable, energy-efficient, and autonomous renewable-energy-driven drying processes.