Yongtai Pan

Efficient calibration of discrete element meso-parameters is essential for reliable rock fragmentation modeling. This study focuses on green sandstone, combining uniaxial compression tests with PFC3D simulations to establish an XGBoost–stepwise regression framework for macro–meso-parameter calibration of the parallel bond model. XGBoost was used to identify the dominant meso-parameters governing peak strength, elastic modulus, and Poisson’s ratio, and stepwise regression was applied to construct explicit nonlinear mapping equations. Peak strength is mainly controlled by shear strength τ_cp and normal strength σ_cp, while elastic modulus and Poisson’s ratio are primarily influenced by bond modulus E_cp and stiffness ratio k_p^*. Introducing quadratic and interaction terms improved model fit, with adjusted R² increasing by 27.5% and 11.2%, respectively. The calibrated parameters reproduced laboratory mechanical indices with errors of 0.09%–3.745% and showed good agreement with the observed shear–brittle failure pattern. Based on the calibrated model, a representative impact-fragmentation simulation further revealed staged conversion of input energy into fracture-related energy during crack initiation, propagation, and through-failure. The proposed framework improves the efficiency and interpretability of PBM parameter calibration and supports DEM-based analysis of rock fragmentation and energy evolution.

The sizer crusher, widely used in mining for its large processing capacity and compact size, however, suffers from high energy waste and accident risks of large particle material due to fixed power-frequency rotational speed, especially in coarse crushing. To realize efficient and energy-saving operation, this study focuses on material nipping mechanics, particle size detection, and adaptive rotational speed regulation. First, a material nipping mechanics based on tooth profile parameters was established to accurately quantify nipping conditions for different particle sizes, which resolves the limitations of traditional equivalent smooth roll theory. Second, a multi-task learning particle size detection model integrated with mineral morphological constraints was built. Via dual-branch collaboration and dynamic weight adjustment, the model achieved 90.3% detection precision, 4.5 mm mean absolute error, and 6.6 mm root mean square error. Finally, through single-particle and particle swarm experiments, a particle size-rotational speed-energy consumption model was developed, and an adaptive strategy was proposed: 40 r/min (60% of 65 r/min power frequency) for regular particles, with 23.3% energy saving and only 13.8% processing capacity loss; 50 r/min for large particles, with 10.5% energy saving and only 5.7% processing capacity loss; and shutdown warning for extra-large particles to prevent equipment damage. This study supports sizer intelligent upgrading and mineral processing energy conservation. The model’s code is presented at https://github.com/shidajin1996-alt/MPPDN.git.

Crushing is a critical step in the efficient utilization of quasi-brittle materials such as ores and solid wastes. During this process, materials undergo fracture, and the product particles are ejected, carrying significant kinetic energy. This study investigates typical quasi-brittle materials—concrete and quartz glass—by conducting impact crushing tests using a drop-weight apparatus under varying contact modes and input energy levels. High-speed camera was employed to capture the fracture patterns of the materials and the trajectories of the ejected particles, enabling the calculation of kinetic energy during crushing. The results indicate that under point contact loading, both kinetic energy and its proportion increase significantly with rising input energy. In contrast, under surface contact loading, the kinetic energy and its proportion exhibit minimal change as input energy increases. The average ejection velocity of particles from quartz glass specimens during crushing was 6.28 m/s, which is 2.21 times that of concrete specimens. Moreover, the average proportion of kinetic energy in quartz glass crushing was 5.049%, approximately 14.43 times greater than that in concrete. Enhancing material toughness and adopting surface contact loading help reduce both the kinetic energy and its proportion during crushing. This research contributes to minimizing kinetic energy loss and improving the efficiency of energy utilization in crushing processes.

In mineral engineering, harmful foreign objects mixed in the feed can cause faults in the sizer crusher (Sizer). The traditional feed identification methods are inefficient and manual, which fail to adapt to the present production conditions of high belt speed, thick material layer and large throughput. In previous studies, Sizer feed identification focused on single-component rather than mixed feeds. Therefore, we proposed a new Sizer mixed-feed identification method based on continuous wavelet transform (CWT) and squeeze-and-excitation residual network (SE-ResNet). Firstly, the three-channel sensor signals acquired during the Sizer feeding process were subjected to CWT, and the resulting scalograms were fused as the image dataset. Secondly, a Sizer feed identification model based on SE-ResNet was designed for single-component and mixed-feed classification. Ablation studies and method comparisons were conducted to validate the performance of the proposed model. Finally, the method of this paper was tested with industrial datasets to demonstrate its effectiveness and generalizability. The results show that the proposed method can achieve an accuracy of 85.99±0.16% for Sizer mixed-feed identification. This method also significantly improves the identification ability of wood, which solves the problem of distinguishing between coal and wood in the feed. Besides, industrial tests suggest that our method has good Sizer mixed-feed identification performance with an average accuracy of 95.64±1.77%, which can meet the demand of industrial production for the safe and smooth operation of the Sizer.

To investigate the effect of crack angle on the fracture performance of brittle materials under tensile load, a molecular dynamics simulation method based on ReaxFF is used to establish an amorphous silica model through the high-temperature melting and annealing process. Under the simulation environment of 300 K, 1.013 × 10⁵ Pa and 5 × 10⁹ s⁻¹, the impact of crack angle on the fracture performance of the model from three perspectives is analyzed: material mechanical properties, micro fracture process, and energy evolution. The result indicates that as the crack angle increases, the ultimate strain and stress of the model decrease accordingly. The crack propagation path of the model will exhibit a “Z” shape due to the coupling effect of tensile and shear stress. The elastic energy efficiency and new surface energy efficiency of the model increase with the increase in crack angle, and the most new surface is generated at 45° crack angle. The linear regression model and asymptotic regression model are used to fit the trends of elastic energy efficiency and new surface energy efficiency with crack angle, respectively, with correlation coefficients R² of 0.986 and 0.994. In the actual comminution process, the input energy required for crushing as well as the surface area and morphology of the material after crushing can be changed by adjusting the angle between the load and the main crack of the material being broken.