In practical industrial-scale paddy drying production, manual empirical operation is still widely used for process control. This often leads to poor uniformity in the moisture content distribution of discharged grains, affecting product quality. Model Predictive Control (MPC) is considered the most effective control method for paddy drying, but its implementation in industrial-scale drying is hindered by its high computational cost. This study aims to address this challenge by proposing a deep-learning-based model predictive control (DL-MPC) strategy for paddy drying. By establishing a mapping relation between the inlet and outlet paddy moisture content and paddy flow velocity, a DL-MPC strategy suitable for multistage counter-flow paddy drying systems is proposed. DL-MPC systems are developed using long short-term memory (LSTM) neural networks and trained using datasets from single-drying-stage and multistage drying systems. Simulation and analysis are conducted, followed by verification experiments on a 5HNH-15 multistage counter-flow paddy dryer. The results show that the DL-MPC system significantly improves computational speed while achieving satisfactory control performance. The predicted paddy flow velocity exhibits a smooth variation and matches field data obtained from multiple transition points, confirming the effectiveness of the designed DL-MPC system. The mean absolute error between the predicted and actual paddy moisture content under the DL-MPC system is 0.190% d.b., further supporting the effectiveness of the control system.

To realize energy-saving and efficient industrial grain drying, the present work studied the variable-temperature drying process of corn drying in a novel industrial corn-drying system with a heat recycling and self-adaptive control function. The drying kinetics, thermal performance, heat-loss characteristics and the heat-recycling performance of the drying system under different allocations between flue gas and hot air were investigated, and the optimized drying process was proposed and compared with two constant drying processes. The results showed that the optimized drying process exhibited better drying kinetic and thermal performance than the two constant drying processes. More specifically, the total heat loss, total energy consumption and specific energy consumption of the optimized drying process were ascertained to be 36,132.85 MJ, 48,803.99 MJ and 7290.27 kJ/kg, respectively, which were lower than those of the other two processes. On the other hand, the thermal efficiency of the drying chamber for the optimized drying process was ascertained to be varied within the range of 6.81–41.71%. Overall, the validation results showed that the optimized drying process can significantly improve the drying performance of the drying system.

Recently, heat pump drying has been widely used in tobacco processing. Considering the importance of this issue, it is of significant importance to further investigate temperature distribution and energy analysis in the drying process. To develop an energy-saving, environmental-friendly, and high-quality tobacco drying method, temperature distribution, dehumidification performance, the economic issues, and thermal efficiency of a heat pump curing barn (HPCB) and a traditional coal-fired bulk curing barn (TCCB) were compared. The regional temperature eigenvalue model was applied to describe the temperature uniformity with HPCB and TCCB. Moreover, thermal efficiency was obtained through energy tests. The obtained results showed that HPCB is beneficial to improve the drying quality of tobacco. The performed analyses showed that the thermal efficiency of the TCCB and HPCB was 42.02% and 66.53%, respectively. Accordingly, heat pump technology is recommended for industrial drying of tobacco leaves and obtaining high-quality products.

Curing modulation is one of the important processes in tobacco production, so it is crucial to recognize tobacco flue-curing states effectively and accurately. This study created a dataset of the complete tobacco flue-curing process in a bulk curing barn environment and proposed a lightweight recognition model based on a feature skip connections module. Firstly, the image data was enhanced using a color correction matrix, which was used to recover the true color of the tobacco leaf in order to reduce the misidentification of adjacent states. Secondly, the convolutional neural network model proposed in this paper introduced Spatially Separable convolution to enhance the extraction of tobacco leaf texture features. Then, the standard convolution in Short-Term Dense Concatenate (STDC) was replaced with Depthwise Separable Convolutional blocks with different expansion rates to reduce the number of model parameters and FLOPs (Floating Point Operations Per Second). Finally, the Tobacco Flue-Curing State Recognition Network (TFSNet) was constructed by combining the SimAm attention mechanism. The experimental results showed that the model accuracy was improved by 1.63 percentage points after the color correction process. The recognition accuracy of TFSNet for the seven states of tobacco flue-curing was as high as 98.71%. The number of params and the FLOPs of the TFSNet model were 203,058 and 172.39 M, which were 98.18% and 90.55% lower than that of the ResNet18 model, respectively. The size of the model was 0.78 mb, and the time consumed per frame was only 21 ms. Compared with the mainstream model, TFSNet significantly improved the detection speed while maintaining high accuracy, and it provided effective technical support for the intelligentization of the tobacco flue-curing process.

Drying involves the evaporation of moisture, accompanied by simultaneous heat transfer, mass transfer, and momentum transfer. While the diffusion law is considered an applicable model for explaining the drying phenomenon, the actual drying process cannot be accurately predicted using an analytical solution with a constant diffusion coefficient. Energy efficiency in the drying process is low due to an insufficient understanding of the mechanisms governing moisture migration from solids to air. The development of drying theory has stalled due to an unsolvable discrepancy between experimental results and analytical results. This study analyzes the effect of the binding energy of moisture in paddy rice on the diffusion coefficient. The theoretical relationship between water activity and drying rate in paddy rice was investigated, and the drying process was successfully explained by analyzing free energy transfer and transition theory. The mechanism of rice grain drying was described using a new theoretical solution for the drying process. These results provide new insights into the development of a scientific evaluation standard for assessing the efficiency of actual drying processes.