Drying is one of the most important processing tasks in modern agriculture. However, the existing circulating dryers have been limited to manual operation and empirical experience, leading to suboptimal drying efficiency and low product quality. Furthermore, the paddy deep-bed drying can also exhibit strong nonlinear dynamics, time delays in the heat and mass transfer, and multivariable coupling among key operational parameters, such as the grain moisture content, airflow rate, and drying temperature. Particularly, there is a high sensitivity to multiple internal and external disturbances, including the fluctuations in the ambient humidity, the initial grain moisture, and the feed rate. Collectively, it is often required for effective and robust control strategies in the challenging task. In this study, an advanced controller was presented to optimize the paddy circulation counter-flow drying, according to the theoretical framework of the model predictive control (MPC). The optimal configuration was specifically designed to accommodate the dynamic behavior and structural constraints. The controller was also used to dynamically regulate the paddy mass flow rate—the primary operational variable—in real time. Thereby, the residence time of the material was adjusted within the drying chamber. The entire drying trajectory was guided toward the final moisture content with high precision. A prediction model was embedded to forecast the future system states over a finite receding horizon. A sequence of the optimal control was computed to minimize the deviations from the target setpoints, according to the physical, operational, and actuator constraints. The objective function of the controller was formulated to incorporate an analytically derived attenuation coefficient, denoted as ‘β’. The penalty term was also applied, as the control input changed. As such, these modification was effectively relaxed the constraint on the paddy flow rate. More responsive adjustments were realized during transient conditions. The closed-loop stability was maintained to prevent excessive wear on the actuators or mechanical components. Simulations and experiments were conducted to evaluate the performance of the controller, including its robustness, resilience against unmeasured disturbances, and the accuracy of time-varying setpoints tracking. Specific case studies were designed to investigate the generalization of the controller for the precise control under abnormal operating scenarios, such as sudden equipment malfunctions. Thereby, the controller was then validated as suitable for the real-world industrial applications. Experimental results demonstrate that the MPC controller significantly mitigated the adverse effects of the major practical disturbances, particularly on some variations in the initial moisture content of the incoming wet paddy and ambient relative humidity. Among all test conditions, the maximum absolute deviation between the actual and target moisture content of the discharged paddy remained below 0.35% on a wet basis (wet basis), indicating the exceptional control accuracy and consistency. The controller was achieved remarkably in the low relative average deviations (RAD) of 0.11%, 0.07%, and 0.01%, respectively, under dynamic setpoint transitions, including the step changes, linear ramp profiles, and sinusoidal reference signals. The superior tracking performance was obtained to fully meet the varying operations. In the anti-interference tests, the artificial disturbances were introduced with the peak amplitudes of ±40%, ±60%, and ±90% at the outlet stage of the drying, in order to simulate the severe process upsets; The average relative deviation in the final moisture content was maintained at only 0.32%, 0.40%, and 0.41%, respectively, under these extreme conditions. Comparative analysis revealed that there was the decrease in the average relative deviation (RAD) between the actual and target moisture content of the paddy: The moisture content RAD decreased by 11.1%, 18.4%, and 30.5%, respectively, compared with the uncontrolled condition; The RAD decreased by 5.9%, 11.1%, and 21.2%, respectively, compared with the conventional PID control; The moisture content RAD decreased by 3.0%, 7.5%, and 14.6%, respectively, compared with the feedforward PID. Collectively, the outstanding performance of the controller was highlighted to reduce the impact of the unknown, variable, and potentially destructive disturbances caused by the abnormal or unpredictable operating conditions. Therefore, the optimal parameter can also provide for a theoretically sound and practically implementable framework for the process stability in the rice circulation.

This study aims to elucidate the dynamics of moisture and temperature alterations in Camellia oleifera seeds during hot air drying and subsequent bursting. A systematic investigation was carried out to optimize the parameters for superior drying. The exceptional energy efficiency was characterized by minimizing energy consumption. The physical attributes of Camellia oleifera seeds were measured to determine their inherent properties, including thermal conductivity and density. The thermodynamic behavior of Camellia oleifera seeds was explored during drying at distinct temperature intervals of 52, 62, and 72 °C. According to Fick's Second Law, the effective moisture diffusion was then obtained corresponding to each temperature regime. An Arrhenius equation model was constructed in the empirically derived data on effective moisture diffusivity using reverse engineering. There was a significant correlation between effective moisture diffusivity, drying temperature, and activation energy. At the same time, a mathematical framework was designed to combine the heat and mass transfer, in order to simulate the drying of Camellia oleifera seeds. The predictions exhibited striking consistency with the experiments, with a maximum error of 8.5%, indicating the remarkable precision and reliability of the model. The results show that the hot-air drying dynamics of Camellia oleifera seeds were fundamentally dominated by internal mass transfer. The higher moisture gradients were observed than those of temperature ones. The fluctuation of drying rates shared a uniform pattern over the varying drying temperatures. The effective moisture diffusivity of Camellia oleifera seeds increased significantly over the temperature spectrum from 52 °C to 72 °C, ranging from 3.299 4×10⁻¹⁰−5.582 6×10⁻¹⁰ m²/s. The energetic transformations were computed as the activation energy of 25.025 kJ/mol during drying. Therefore, the variable temperature drying was performed better for Camellia oleifera seeds. There was the governing impact of three parameters—the initial wind temperature, the moisture conversion threshold, and the concluding wind temperature on specific energy consumption and drying velocity. Response surface optimization was applied to determine the optimal combination of drying parameters: an initial wind temperature of 63.7 °C, a moisture conversion point of 38.5%, and a terminal wind temperature of 74.8 °C. The better performance was achieved under these optimal conditions. Specific energy consumption was reduced to 5.040 kJ/g and a drying rate peaking at 0.048 g/(g·h). Compared with the model, relative errors for specific energy consumption and drying rate were 7.4% and 12.1%, respectively, indicating the pragmatic applicability and accuracy of the optimized parameters. In summary, a robust theoretical groundwork was offered to refine the practical drying parameters for Camellia oleifera fruit hot air drying and bursting, paving the way for industrial implementation and dissemination of Camellia oleifera fruit drying. Thus, considerable academic significance was provided for the promising practical application.