Digital Twin–Enabled Data-Driven Strategy for Real-Time Thermal Control in Direct Hybrid Solar Dryers

Cadi Ayyad University, Faculty of Sciences and Technologies, Laboratory of Mathematics Artificial Intelligence and Sustainable Technologies (LMAIST), Marrakech, 40000, Morocco

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Published online: 22 May 2026

Abstract

In this study, a high-fidelity digital twin structure for the adaptive thermal regulation of hybrid direct solar dryer is proposed. Matlab's System Identification Toolbox was used to identify the dynamic models of the drying chamber temperature and humidity from experimental data. An Extended Kalman Filter (EKF) was used for real-time state estimation and online updating of the model parameters during varying operating conditions to maintain an accurate real-time synchronization between the physical system and its virtual counterpart. This updated digital twin was subsequently used within a predictive functional control (PFC) strategy permitting better disturbance rejection and compensation for the sluggish thermal dynamics of the drying process. Moreover, a Hybrid Adaptive Differential Evolution (HADE) algorithm was implemented for adaptive tuning of the control and model parameters, enhancing robustness and tracking performance in a nonlinear environment. The physical dryer (controlled by a National Instruments PLC) was physically connected to the virtual model, which had been developed in MATLAB and interfaced through LabVIEW using a bidirectional TCP/ IP communication architecture. These enable reliable monitoring, adaptive thermal regulation, and real-time accurate prediction per experimental results.