Moisture migration in bulk materials can cause many problems for bulk material handling. The review aims to find an appropriate method for dynamic moisture migration in unsaturated bulk materials during processing. A large amount of research on moisture migration has been done from both macroscopic and microscopic views. From the macroscopic view, moisture migration in bulk materials such as soil, sand, and grain, were relatively well understood based on the diffusion theory and infiltration theories. The microscopic theory, liquid bridge rupture, and vibration were also well developed. The theories and simulation models can be widely used in geotechnology, food engineering and process control of bulk material. However, current research concerns little about dynamic moisture migration in unsaturated bulk material. Some further development of theories and simulation models are needed.

To enhance the real-time performance and accuracy of damage detection in conveyor belts with poor image quality, insufficient sample feature information, and numerous scattered minor damages, we propose an improved visual detection method based on YOLOv5, incorporating Improved Depth-Separable Convolution (IDSC) and Scale Sequence Feature Fusion (SSFF). The IDSC method significantly reduces network parameters, achieving a lightweight model while maintaining detection accuracy, whereas the SSFF method effectively integrates spatial and multi-scale feature information, enhancing the identification of minor damage. Compared to existing methods, our approach provides a superior balance between detection speed and accuracy. The proposed IDSC-SSFF model achieves a mean average precision (mAP) of 95.65% while maintaining an inference speed of 139.1 FPS, meeting the stringent requirements of real-time defect detection in industrial environments.

Stacking of bulk solids in terminals, ships, trains, and other bulk solids storage yards is always challenging, considering the requirements of optimal utilization of storage areas. In this study, stacking shapes of a variety of bulk solids with different particle sizes were tested, and the curved shapes of the stockpiles were extracted to establish the actual three-dimensional models of the bulk solids accordingly. The three-dimensional curved stockpiles were used to design the bulk solids stacking strategy where the bulk stacking locations, the stacking volume, and the stacking flatness were optimized. A modified golden section method with a self-influenced factor was developed to improve the calculation efficiency of the stacking algorithm for bulk solids stacking flatness. Furthermore, the bulk solids stacking strategy and algorithm were verified by experiments. The results showed that the curved three-dimensional models were very close to the actual shapes of the bulk solids stockpiles, while the improved golden section method was more accurate and efficient than the traditional golden section method in determining the optimal values of the stacking volumes in terms of flatness. For the different bulk solids tested, the experiment results showed that good flat stacking can be achieved by using the developed stacking strategy.