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    Integrated CFD–drying model for design optimisation of multi-layer rack drying systems for mackerel processing
    (2026-08-01)
    Nabudda, Kriengkrai
    ;
    Thasnas, Natakorn
    ;
    Poungthong, Pongthep
    This study presents an integrated Computational Fluid Dynamics (CFD)–Page model framework for analysing and optimising the drying performance of a six-layer mackerel drying system. Experimental drying data were fitted using the Page model (k = 0.185, n = 1.32), achieving high predictive accuracy (RMSE = 0.018). The coupled CFD–drying model was applied to evaluate moisture removal behaviour and the effect of rack spacing on airflow distribution, temperature uniformity, and heat transfer. Results revealed distinct layer-dependent drying characteristics, with the uppermost layer exhibiting the fastest moisture removal due to greater exposure to hot, low-humidity airflow. CFD simulations for rack spacings of 8–12 mm showed that spacing significantly influences airflow penetration and thermal distribution. A mesh-independent model with 261,785 elements ensured numerical reliability. The optimal rack spacing was identified as 10 mm, providing the most uniform airflow and temperature distribution. Surrogate model optimisation further predicted an optimal spacing of 10.21 mm, improving drying uniformity, energy efficiency, and overall system performance.
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    Multi-Regime GRU-Scheduled PID Control for Greenhouse VPD Regulation with Real-Time PLC Deployment
    (2026-01-01)
    Wanna, Natthanan
    ;
    Nabudda, Kriengkrai
    ;
    Sitthaphanit, Suphasit
    ;
    Thasnas, Natakorn
    Vapor pressure deficit is a physiologically meaningful climate variable governing plant transpiration and water transport in greenhouse cultivation. However, accurate regulation in evaporative-cooled greenhouses is challenging due to nonlinear temperature-humidity coupling, time-varying dynamics, and rapidly changing solar disturbances. Conventional fixed-gain proportional-integral-derivative controllers, tuned for a single operating condition, often exhibit performance degradation under regime transitions. This study proposes a multi-regime admissible-region-constrained gain-scheduled control framework in which a gated recurrent unit network serves as a supervisory scheduler to generate smooth, near-optimal controller gains for real-time vapor pressure deficit regulation. Control-oriented first-order-plus-dead-time models are identified under representative operating regimes, and an interval-bounded plant ensemble is constructed to capture dynamic variability. Regime-wise optimal gains are obtained offline using hybrid evolutionary optimization and used as supervisory training labels. To support safe deployment, all scheduled gains are constrained within a pre-validated admissible stability region, such that each deployed gain configuration remains inside an offline-validated frozen-time stabilizing set. The controller is implemented on an industrial programmable logic controller and experimentally validated in a full-scale evaporative greenhouse under real environmental disturbances. Comparative multi-day experiments demonstrate improved tracking accuracy, reduced overshoot, smoother actuator behavior, and enhanced disturbance attenuation relative to fixed-gain strategies, while maintaining deterministic real-time execution. The results establish an industrially deployable learning-based gain-scheduling framework for practical greenhouse climate control.