In order to accurately predict the mine water inflow after exposing the aquifer in the open-pit mine of the main ore body of Kambove mine, the mine field pumping test was carried out in combination with the existing hydrogeological data of the mine, and the mine pit water inflow in different mining years was calculated based on the pressure bearing transfer to no pressure formula in the large well method. Based on field hydrogeological data and pumping test results, the main ore body is divided into strong water-rich zone (Ⅰ), medium water-rich zone (Ⅱ) and an aquifer (Ⅲ). When the mine is pushed to a level of 1 300 min the third year of production, the underground water inflow is the smallest. When the mining level reached 1 270 m in the sixth year of production, the underground water inflow reached its maximum value.Combined with the rainfall in normal rainy season and rainstorm season, the total water inflow of the mine pit can reach 23 152 m³/d and 46 552 m³/d respectively. The research results can provide important data support for the development of subsequent waterproofing and drainage plans for the mine.

In order to enhance the shear performance of graphite tailings, based on the indoor direct shear test, the effects of clay content, water content, quicklime content and polypropylene fiber content on the shear strength of graphite tailings were determined by single factor test. The optimum mix ratio of clay, water and quicklime was obtained by response surface method (RSM), and the optimum mix ratio scheme was verified. The microscopic characteristics of the samples were analyzed by scanning electron microscopy. The results show that with the addition of clay, water, quicklime and polypropylene fiber, the shear strength and cohesion of the solidified graphite tailings increase first and then decrease, and the internal friction angle tends to be stable.When the clay content is 29.38%, the water content is 15.82%, and the quicklime content is 3.59%, the cohesion of the solidified graphite tailings reaches the best, which is 9.417 3 kPa. The average cohesion obtained by the test is 9.487 kPa. The relative error between the predicted value and the test value is 0.7%, less than 5%, which verifies the feasibility of the response surface design model.The SEM results show that the agglomeration structure is formed between the modified graphite tailings particles and the clay, which improves the shear strength of the solidified material. The research results are of great significance to the reinforcement and protection of graphite tailings reservoir slope.

Compound portland-sulphoaluminate cement coal-based solid waste grouting material was prepared by using coal gangue and fly ash as raw materials, supplemented by portland cement and sulphoaluminate cement as cementitious materials. By adjusting the mass ratio of portland cement and sulphoaluminate cement, the effects of cement with different mass ratios on the mechanical properties and flow properties of grouting materials were studied. The pore size characteristics of grouting materials were analyzed by nuclear magnetic resonance, and the hydration products of grouting materials were analyzed by scanning electron microscope and energy spectrum. The results show that when the mass ratio of portland cement to sulphoaluminate cement is 3:7, the 28 d compressive strength and flexural strength of the specimen are 17.05 MPa and 5.00 MPa, respectively, the slump is 34.83 cm, the setting time is 174 min, and the comprehensive performance is the best. The main hydration product of the grouting material is C-A-S-H gel, which is mutually reinforced with the skeleton formed by gangue powder aggregate, fly ash, C-S-H gel, which effectively improves the strength of the specimen. The internal pore size of the grouting material is mainly distributed in 0−0.63 μm and 1−4 μm, and the pore size of 0.05−0.4 μm occupies the main position. The research results can provide some theoretical guidance for the development and engineering application of grouting materials.

Conventional ecological restoration surveys in mines have the characteristics of long time, high cost, and difficulty in tracing the past, which have certain limitations. In order to quickly and intuitively obtain the distribution and restoration progress of historical abandoned mines in aimed region, taking northwest Yunnan as the research area, multi temporal remote sensing data was used to summarize the interpretation indicators of distribution in historical abandoned mines, mine ecological restoration models, and specific restoration measures in this area. Through conducting detailed interpretation and analysis and combining with field investigation and verification work, the distribution of historical abandoned mines in this area and the subsequent ecological restoration situation of mines were clarified. The results show that there are a total of 3 060 historical mining spots in this area, with mining and transfer sites as the main types of damage, accounting for 81.8% of the total number of spots. The involved minerals are mainly non-metallic minerals. In terms of mine restoration, 765 mining spots with an area of approximately 1 468 hectares have been repaired and treated, mainly through engineering restoration. The investigation and research results can provide a reference for the investigation and restoration of abandoned mines, as well as important data support for the planning, policy formulation, and deployment of mine ecological restoration and management work.

In order to study the influence of dynamic obstacles on mine wind speed, four influencing factors of roadway inlet wind speed, mine car driving speed, mine car length and blocking ratio were selected when the mine car was driving in the transportation roadway. The dynamic grid technology was used for numerical simulation, and the variation law of the influence of different factors on the roadway wind flow was obtained. The results show that the greater the difference between the inlet wind speed of the roadway and the driving speed of the mine car, the more obvious the average wind speed change of the cross section of the roadway, and the longer the interference distance of the mine car to the wind flow of the roadway, which greatly affects the stability of mine ventilation system. The mine car length and the blocking ratio have little effect on the stability of the mine ventilation system. Using the stepwise linear regression analysis method in SPSS software, the influence of four influencing factors on the change degree of roadway wind speed from strong to weak is mine car driving speed, roadway inlet wind speed, blocking ratio and mine car length. The research results are of great significance to maintaining the stability of mine ventilation system.

In order to reduce the energy consumption of ball mill, ZQM250×100 ball mill was used to study the grinding energy consumption of an iron ore after high-pressure roller grinding, and the energy required for the newly generated －0.074 mm grade product after grinding was used as the evaluation standard of grinding energy consumption. The effects of medium filling rate, ore addition quantity, water addition quantity and mill frequency on the energy consumption of the newly generated －0.074 mm product, the significance of the interaction between the factors and the optimal test conditions were analyzed and predicted by the response surface methodology, and the results were compared with the single factor test results. The optimum conditions obtained by single factor test are as follows: medium filling rate is 40%, ore addition quantity is 700 g, water addition quantity is 300 mL (mass concentration is 70%), mill frequency is 50 Hz, and energy consumption of the newly generated －0.074 mm grade product is 169.54 (kW·h)/t. The response surface methodology analysis show that the mill frequency has the greatest influence on the energy consumption of the newly generated －0.074 mm grade product, followed by the medium filling rate, water addition quantity, and ore addition quantity. The interaction between medium filling rate and mill frequency is the most obvious, followed by water addition quantity and mill frequency, ore addition quantity and water addition quantity, medium filling rate and ore addition quantity, ore addition quantity and mill frequency, medium filling rate and water addition quantity. The model prediction based on the response surface methodology shows that the optimum test conditions are: 39.71% medium filling rate, 698.15 gore addition quantity, 339.65 mL (mass concentration 67.27%) water addition quantity, and 49.73 Hz mill frequency. Under this optimal condition, the predicted energy consumption of the newly generated －0.074 mm product is 120.53 (kW·h)/t, and the average energy consumption of the verification test is 118.97 (kW·h)/t, which is very close to the predicted value, indicating that the response surface prediction model is reasonable and effective.

In order to solve the problems of high energy consumption, material accumulation in the air duct, and local wear in the swing mill, an analysis was conducted on the existing structures of the intake air duct, including volute and air duct blades. Based on fluid dynamics, multiphase flow, and discrete element coupling simulation technology, numerical simulations were conducted on the internal wind and particle fields of a pendulum mill. Response surface simulation tests were conducted on the volute shape, the blades angle of air duct, and the blades number of air duct. The results show that optimizing the volute shape is beneficial for reducing pressure loss and equipment energy consumption. Reasonable selection of blade angle and number is beneficial for reducing the uneven coefficient of air flow velocity in the air duct and regulating the flow field. Based on multi-objective optimization of pressure loss and non-uniformity coefficient, the optimal solution for the air duct structure was determined as volute shape of 2, blades numbers of 18, and blade angle of 55°. The collection efficiency of finished particles in the optimized scheme is as high as 95.3%, which is 13.5% higher than that of the original scheme. The simulation results are consistent with the field test data, which can provide a guidance for the structural optimization design of the pendulum mill.

Aiming at the problem that the convolutional neural network applied in the existing copper ore grade classification lacked the relationship between the construction and the induction of long-distance features, a copper ore grade classification method based on the integrated model of Swin Transformer and EfficientNet was proposed. This method made full use of the inductive ability of Swin Transformer V2-t architecture for long-distance feature relations and the advantage of EfficientNet V2-s in capturing subtle local features. By adding a linear layer to integrate the output results of the two models, and dynamically adjusting the weight of the linear layer according to the output of a single model itself, the mapping relationship was optimized, and the classification performance was significantly improved. The experimental results show that the accuracy rate of the fusion model in the classification task is 92.891%, the accuracy rate is 93.095%, and the recall rate is 92.654%. Compared with the single model before integration, the integrated comprehensive model has improved the classification accuracy by 1.3 percentage points, the accuracy rate by 1.9 percentage points and 2.186 percentage points respectively, and the recall rate by 0.474 percentage points and 0.237 percentage points respectively.

Coal gangue sorting is an essential process in coal mine production, and automatic identification of coal gangue is a prerequisite for intelligent sorting. Coal gangue object detection based on deep learning can make full use of image features, and the upper limit of accuracy is high, but there are problems such as large amount of model calculation and difficulty in deploying real-time sorting sites. Aiming at the above problems, a lightweight DVBS-YOLOv8s coal gangue object detection method was proposed. To enhance the inference speed of the model, DSConv and Vanillanet modules were introduced into the backbone of YOLOv8s, and Slim-neck was incorporated into the neck of the network to achieve lightweight design. Additionally, to improve the feature capture ability of the model for conveyor belt background and coal gangue objects, BoT and Slim-neck were introduced. Finally, WIoU and Soft-NMS algorithms were used to enhance the training effect. An image acquisition test platform was set up, and a dataset of 14 790 coal gangue images was self-constructed for testing. The results show that in the coal gangue object detection task, compared with YOLOv8s, the improved DVBS-YOLOv8s-WS network model pays more attention to global features, increases the receptive field, and inference speed increase by 32%. Compared to the YOLOv5 series, YOLOv7 series, SSD, Faster-RCNN, and YOLOv8s, both detection accuracy and speed are improved, providing reference for achieving high-precision real-time sorting of coal gangue based on deep learning.

Aiming at the problem of excessive arsenic content in zinc concentrate and low grade of arsenic concentrate in a zinc polymetallic mine in Inner Mongolia, the flotation process of copper, zinc and arsenic was systematically studied. The combination of lime, sodium humate and zinc sulfate was used to inhibit arsenic-sulfur minerals, ethyl xanthate and Z-200 were used as combined collectors to enhance the selectivity of copper flotation. During the zinc flotation stage, the lime and NY were used as inhibitors to inhibit arsenic, the copper sulfate was used to activate sphalerite, and the collector ZC was used to improve the recovery rate of zinc. In the arsenic flotation stage, the copper-zinc flotation tailings were further finely ground(－0.074 mm accounts for 68.10%), and the arsenic recovery rate was improved by reagents combined with sulfuric acid, sodium silicate and copper sulfate. Copper concentrate with a grade of 29.35%, recovery rate of 82.17% and arsenic content of 0.46% are obtained through preferential flotation process. The zinc concentrate grade is 46.85%, the recovery rate is 86.34% and the arsenic content is 0.52%. The arsenic concentrate contains arsenic grade of 32.20% and recovery of 87.94%. When the grinding fineness is optimized to－0.074 mm accounts for 80.13%, the dissociation degree of copper-zinc minerals is significantly improved. Compared with the original beneficiation process, the arsenic content of copper-zinc concentrate is greatly reduced. The research results provide technical reference for solving the difficult problem of beneficiation of high arsenic-zinc polymetallic ores.