Deep coal mining operates in increasingly extreme environments, where substantial dust generated during the mining process poses serious risks to both production safety and miners' health. To understand the dust transport pattern and miners' particulate exposure risk within a full-scale heading face under various ventilation configurations, airflow patterns and dust dispersion characteristics were systematically investigated using the Euler-Lagrangian approach. A user-defined function was developed to enable the dynamic injection of particles, simulating realistic dust generation processes. The average residence times of particles with varying sizes were calculated, and the number of particles entering the respiratory zone was quantified to assess the driver's exposure risk. Results revealed that the driver remained in the recirculation zone in all layouts. The centrally positioned ventilation cylinder facilitated the suspension of smaller particles. With a total suspension time of 300 s and an air supply velocity of 11 m/s, particles sized below 20 µm exhibited the longest average residence time—11.24% and 26.24% longer than in layout R (cylinder on the opposite side of the driver) and layout L (cylinder on the driver's side), respectively. Smaller respiratory dust (i.e., d p < 7.07 µm) and particles originating from the upper part of the dust injection surface were most likely to enter the driver's respiratory zone. Increasing the fresh air supply alone proved insufficient to reduce particulate exposure. Among the configurations, layout L was optimal, reducing particulate exposure by 65.12% compared to layout R. This study provides theoretical insights for optimizing ventilation strategies within the heading face.

Targeted drug delivery to the deep lung improves therapeutic outcomes, but respiratory system variability complicates drug spray design. Numerical simulations offer insights for individualized treatments but are computationally intensive, highlighting the need for surrogate models for real-time deposition prediction. This study comprehensively explores the multi-task predictive capability of regression models, including Linear regression (LR), Bayesian regression (BR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), XGBoost, and CatBoost, for predicting total and regional deposition rates of inhaled particles in airway. A training dataset is obtained from well-validated CFD simulations with realistic human airway model using Euler-Lagrangian method. The results indicate that LR, BR, and SVM yield unsatisfactory predictive accuracy, with average R² values in range of 0.21 to 0.73. Comparatively, BPNN and decisiontree-based models show great potential in predicting total deposition rate in the upper and central airway. However, for regional deposition rate prediction, BPNN did not consistently yield high accuracy, particularly for oral deposition (R² = 0.538). Comparatively, XGBoost emerges as optimal model, achieving an R² approximately close to 1 on both the training and testing datasets, with predictive errors within the range of ±0.5. The overall results demonstrate that decision-tree-based models, particularly XGBoost, have superior performance in accurately predicting both total and regional deposition rates of inhaled particles within airway. Despite limitations like geometry complexity and data quantity, the workflow developed in this study is expected to pave the way for future research integrating ML models into drug delivery device design and evaluation.