Yunzhang Rao

This study investigated wetting body migration and blind area distribution variations under different height differences (Δh) using indoor experiments and numerical simulations. Results show that the Δh of the injection hole shifts the wetting body intersection backward. Due to the increase in Δh, the vertical migration of the wetting peak at the No. 1 liquid injection hole accelerates, and the horizontal migration tends to be stable, which indicates that the Δh promotes the vertical seepage by changing the hydraulic gradient, which is beneficial to accelerate the leaching process. The migration of the wetting peak presents the characteristics of ‘fast first and then slow’, and it is easy to form a blind area in the later stage of leaching. When Δh is 0 and 3 cm, the blind area is concentrated between the two holes in the upper part of the ore heap. When Δh increases to 5 and 7 cm, the blind area expands to the top of the No. 1 hole. The simulation results show that although the increase in Δh can accelerate the recovery of water pressure in the near-end injection hole, it will increase the difference in leaching efficiency between ‘near-end’: when Δh is small, the wetting body diffuses symmetrically and the blind area is easy to eliminate; the increase in Δh leads to the asymmetric migration of the wetting body, and the remote area faces a significant risk of a blind area due to a low water pressure and low concentration.

The accurate prediction of dissolved oxygen (DO) concentration in rivers is very important for the management of aquatic ecosystems, However, the hybrid model of ' modal decomposition + prediction ' for predicting the nonlinear change of dissolved oxygen in rivers is still insufficient. In this paper, a frequency division prediction framework based on the optimal ensemble of Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed. The dissolved oxygen sequence was decomposed into multiple components by CEEMDAN, and the long short-term memory network (LSTM), support vector regression (SVR) and multi-layer perceptron (MLP) models were constructed to predict each component independently. An innovative grid search algorithm with constraints is constructed, and the advantages of each model are complemented by dynamic combination. The optimal ensemble scheme is obtained with the goal of minimizing the mean absolute error ( MAE ) of the training set. The empirical study of monitoring sections A and B in the Ganjiang River Basin shows that : in the prediction task, the prediction of the training set, the MAE of the integrated model is 18.6-35.5% lower than that of the ensemble model, the root mean square error ( RMSE ) is 22.1-22.8% lower, and the determination coefficient ( R2 ) reaches 0.954 and 0.972. In particular, the error accumulation of MAE in the 3-day prediction is 27.2-81.4% lower than that of the mixed model. This framework enables the modes of multi-component dissolved oxygen series prediction to be effectively aliasing, and provides an extensible technical path for the intelligent management of the basin.

Due to the intersection of three mining rights in a mining area, the stability of the rock mass is mutually affected after mining operations. To study the optimal backfill ratio and the surface stability after backfilling in the adjacent goaf areas of the three mines in this mining area, a mineral deposit model is established using Rhino software. The model spans 2500 m in the east–west direction, 3000 m in the north–south direction, and ranges from an underground elevation of −610 m below. FLAC3D software was then used to analyze the surface stability under two different backfill ratios after the complete excavation of the ore body. Additionally, 52 monitoring points were set up at critical buildings and structures. The results revealed that after the complete excavation of the ore body, large-scale surface subsidence occurred in the mining area, with the main subsidence center located in the Yinzhushan mining area. Under backfill condition 1, six monitoring points experienced settlements exceeding 30.00 mm, with a maximum settlement of 53.98 mm. Under backfill condition 2, three monitoring points exceeded 30.00 mm, with a maximum settlement of 51.93 mm. The level displacement deformation at the monitoring points under both conditions met the stability requirements specified by national standards. By comparing the settlements at the monitoring points, it was determined that backfill condition 2 represents the optimal backfill ratio. This study provides a theoretical basis for practical backfilling operations in the mine.

In in situ leaching, fine particles can be stripped and transported with the leach solution, significantly altering the particle size distribution and pore structure of each layer of the rare earth ore body. In this study, water and magnesium sulfate were used as leaching agents. Based on indoor column leaching experiments, particle gradation experiments, and pore structure tests, this study investigates and analyzes the patterns of particle migration and changes in pore structure in rare earth ores with varying fine particle contents under leaching conditions. The results indicate that during the leaching process, the degree of change in particle gradation follows the order of upper layer > middle layer > lower layer. As the depth increases, the soil becomes denser, leading to reduced permeability, a slower seepage rate of the leaching solution, and a higher fine particle content, making the effect more pronounced. During magnesium sulfate leaching, the overall trend of porosity in the rare earth ore structure initially increases and then decreases. Additionally, a higher fine particle content corresponds to higher porosity. In the early and late stages of leaching, pore size changes involve the transformation of larger pores into smaller ones, followed by the conversion of smaller pores into larger ones. Moreover, the higher the fine particle content, the greater the degree of transformation between the pore sizes.

The permeability of ionic rare is a crucial factor influencing the leaching rate of rare earth elements. In the Gannan region, many ionic rare earth ores exhibit poor permeability and high compressibility compared to sandy soils. The permeability coefficient is a key indicator of the hydraulic performance of these ores. Thus, this study investigates the permeability coefficients of ionic rare earth ores with varying fines contents during the leaching process, with a specific focus on analyzing the impact of fines on permeability performance. To provide a comprehensive assessment of the influence of fines, we prepared ionic rare earth ore samples with fines contents of 5%, 10%, 15%, 20%, 25%, and 30%, ensuring that the overall particle size distributions remained consistent with the original gradation. A constant head permeability test was employed to measure the permeability coefficients of these ore samples throughout the leaching process. We specifically examined how varying fines contents influenced permeability across the upper, middle, and lower layers of the ore body, as well as the overall permeability when subjected to both distilled water and magnesium sulfate solutions. To further elucidate the differences in permeability performance among the various rare earth ore samples, we performed a data fitting analysis of the initial permeability coefficients against fines content, uniformity coefficient, average particle diameter, and void ratio. This analysis aims to quantify the fines effect across different rare earth ores and establish correlations among state parameters, such as fines content, and the initial permeability coefficient.

In the in-situ leaching method of ionic rare earth, ion exchange reaction between rare earth ions and leaching agent ions is carried out, which allows the rare earth ions to be leached from the ore body as the leaching solution flows through the pores. This indicates that the leaching process of rare earth ions is closely related to the seepage field, ion exchange field, and ion migration process of the leaching solution. In this study, an ionic rare earth mine located in Longnan of Jiangxi Province was taken as the research object. By conducting nuclear magnetic resonance scanning on the ore samples of this mine and vectorizing the nuclear magnetic resonance images, a two-dimensional geometric model of pores was obtained. Then, COMSOL Multiphysics software was used to establish a coupled numerical model of seepage–exchange–migration of the ionic rare earth mine during the leaching process at the pore scale to study the seepage situation of leaching solution with different injection strengths and concentrations, as well as the exchange and migration process. The results show that increasing the concentration of magnesium ions can increase the difference of ion diffusion concentration, accelerate the forward exchange rate of ions, promote the forward exchange reaction, and improve the concentration gradient of rare earth ions in the leaching solution. The more significant the diffusion effect, the higher the ion migration rate, while at the same time inhibiting the reverse adsorption of rare earth ions, and accelerating the leaching efficiency of rare earth ions. In addition, increasing the strength of the injection solution allows rare earth ions to leach out of the ore body earlier, shortens the leaching cycle, and thus reduces the peak concentration of leached rare earth ions. By analyzing the effects of the strength of the injection solution and leaching concentration on ionic rare earth leaching, the influence of those two factors on engineering economy can be briefly evaluated, which can be provided as a reference for the optimization of ionic rare earth mining technology.

Rockburst present substantial hazards in both deep underground construction and shallow depths, underscoring the critical need for accurate prediction methods. This study addressed this need by collecting and analyzing 69 real datasets of rockburst occurring within a 500 m burial depth, which posed challenges due to the dataset's multi-categorized, unbalanced, and small nature. Through a rigorous comparison and screening process involving 11 machine learning algorithms and optimization with KMeansSMOKE oversampling, the Random Forest algorithm emerged as the most optimal choice. Efficient adjustment of hyper parameter was achieved using the Optuna framework. The resulting KMSORF model, which integrates KMeansSMOKE, Optuna, and Random Forest, demonstrated superior performance compared to mainstream models such as Gradient Boosting (GB), Extreme Gradient Boosting (XBG), and Extra Trees (ET). Application of the model in a tungsten mine and tunnel project showcased its ability to accurately forecast rockburst levels, thereby providing valuable insights for risk management in underground construction. Overall, this study contributes to the advancement of safety measures in underground construction by offering an effective predictive model for rockburst occurrences.

Ion-absorbed rare earth deposits react with the leaching agent during the in situ leaching process through ion exchange and hydration, which change the stability of ore agglomerates and even result in mining slopes or landslides. Indoor simulated column leaching assays were conducted on ion-absorbed rare earth deposit samples by using magnesium sulfate solution as the leaching solution. Surface zeta potential, double electric layer thickness, particle gradation, and pore structure were analyzed to measure the different concentrations and pHs of leaching solutions impact on the stability of ore agglomerates. Results show that the critical magnesium sulfate solution concentration and pH affecting the stability of deposit sample agglomerates are 3.5 and 4, respectively. The chemical replacement reaction between the leaching agent and rare earth ions occurs during column leaching when it reaches its zero-point potential at a pH of 3.5168. This breaks the balance between the van der Waals gravitational force and double-layer repulsion in clay particles and induces the disruption of agglomerates, which causes the difference in the pore radius ratio of the ore samples before and after column leaching. It is of great engineering guidance to solve the problems of slope instability and landslides that may occur in the ore body during the mining process of ionic rare earth ore.