Abandoned mine not only contains a large number of available resources, but also has many risk factors. In order to scientifically determine the reutilization mode and take into account the risk of many abandoned mines, a risk framework of abandoned mines including six dimensions of technical risk, safety risk, environmental risk, community risk, legal risk and financial risk was constructed, and the risk assessment of reutilization was carried out by LEC risk assessment method. Then, from the perspective of risk management, the method combining improved analytic hierarchy process (IAHP) and TOPSIS was proposed to determine the best reutilization mode of abandoned mines. Finally, taking the closed mine of Muchengjian Coal Mine in Jingxi Mining Area as an example, the proposed method was verified. The results show that the high risk factors of the closed mine are mainly concentrated on safety risk and social risk, and ecotourism is selected as the optimum reutilization mode. This risk management-based method can provide a reference for optimizing the reutilization mode of abandoned mines and effectively reduce the negative effects of mine closure.

China's iron ore resources are increasingly depleted, and they have difficult to select characteristics such as poor, fine and miscellaneous. The traditional anionic collector has large dosage, complex reagent system and poor activity. In order to solve this problem, the surfactant processed by industrial waste amine (YTDB) was used as cationic collector to study the single mineral flotation test and mineral adsorption mechanism of quartz and hematite. The results show that when dodecylamine (DDA) is used as collector, under the conditions of pH=7 and collector dosage of 20 mg/L, the recovery rate of quartz is 78.36%, and the recovery rate of hematite is 12.57%. At this time, the flotation difference is the largest. When YTDB is used as collector, under the condition of pH=7 and collector dosage of 15 mg/L, the recovery rate of quartz is 91.27%, and the recovery rate of hematite is 12.67%. At this time, the flotation difference is the largest. YTDB is obviously better than DDA in flotation index, and saves the dosage of reagent to a certain extent. By testing the infrared spectrum, surface tension and Zeta potential before and after the interaction of the agent and the mineral, it is found that YTDB adsorbed on the surface of quartz.

With the continuous exploitation of wolframite resources, the raw ore gradually tends to be poor and fine. Flotation has become an effective means to improve the recovery efficiency of fine wolframite. In recent years, study of flotation technology has mainly focused on the development of flotation reagents. Taking the development of wolframite flotation reagents as the starting point, the development of collectors, activators and inhibitors in wolframite flotation was introduced. The combination methods, advantages and disadvantages and indexes of reagents were described in detail. The flotation process mechanism of flotation reagents and the mechanism of solid-drug surface action were analyzed. The results show that the chelating collector has strong selectivity, but the cost of the reagent is high, the manufacturing process is complex and the stability is poor, and there are few reagents that can be applied in actual production. Fatty acid collectors are widely used because of their strong collecting ability and low price, but their selectivity will be reduced, and they are often used in combination with other agents. The collecting performance of arsonic acid and phosphonic acid collectors is better than that of fatty acid collectors, but because of its high price and environmental pollution, it has not been applied in actual industrial production. The combined use of collectors can not only reduce the production cost of mines, but also reduce the use of toxic agents to a certain extent. Highly selective activators and inhibitors can achieve efficient separation of wolframite slime and gangue minerals. According to the existing situation, new reagents with high selectivity, low dosage, environmental protection and non-toxicity should be developed according to different ore properties.

With the increase of mining depth, the backfill body is affected by high ground stress, mining disturbance and groundwater erosion. In order to explore the influence of groundwater erosion on the mechanical properties of cemented backfill body with polypropylene fiber tailings, uniaxial compression test and Brazilian splitting test were carried out on specimens with different fiber contents, and specimens with better mechanical properties were selected. Based on the groundwater erosion environment, uniaxial compression and acoustic emission monitoring tests were carried out to study the damage and failure evolution characteristics of polypropylene fiber backfill body under the action of groundwater. The results show that with the increase of polypropylene fiber content, the compressive strength of backfill body increases first and then decreases, and the peak strength of the specimen with 0.3% fiber content is 3.82 MPa, which is the best. After groundwater erosion, the cumulative ringing count characteristics of acoustic emission can be divided into three stages: initial active stage, steady growth stage and rapid growth stage, and obvious damage precursor characteristics appear in the steady growth stage. The durability of the backfill body specimens with different pH erosion of groundwater is as follows: eroded backfill body specimens with pH=9> eroded backfill body specimens with pH=7> eroded backfill body specimens with pH=5> non eroded backfill body specimens. With the increase of groundwater pH, the RA-AF shear crack signal continues to decrease, the damage and failure of the backfill body changes from shear failure to tensile failure. The research results can provide reference for the improvement of mechanical properties and durability of mine backfill body.

In order to explore the influence of physical properties of tailings on solid flux and optimize the thickening process parameters, the total tailings samples of 10 typical metal mines were selected, and the quantitative relationships between solid flux and tailings particle size or density was systematically studied. Combined with static flocculation sedimentation and dynamic thickening test data, a solid flux prediction model based on particle size-density composite parameters was established. The results show that under the condition of static flocculation sedimentation, the type and unit consumption of flocculant significantly affect the sedimentation rate and underflow concentration, and rational regulation of flocculation conditions can effectively improve sedimentation efficiency. The solid flux is significantly positively correlated with the square root of the median particle size and the density correction value of the tailings (R²≥0.94). The particle size-density composite parameters prediction model established based on nonlinear regression can accurately characterize the quantitative relationship between the physical properties of tailings and solid flux. Under dynamic thickening conditions, the feed rate of tailings slurry is linearly positively correlated with the solid flux, and the solid content of the overflow water forms a dual constraint mechanism on the flux threshold. The comparative test shows that the dynamic thickening process can increase the underflow concentration by 10%−15% compared with the static sedimentation, which fully verifies the technical advantages of the deep cone thickener in the preparation of high concentration slurry. The research results can provide theoretical basis and technical support for efficient thickening and intelligent filling of mine tailings.

Aiming at the problem of low prediction accuracy of existing blasting vibration velocity prediction formulas in complex ground environments, a BP neural network model based on improved grey wolf optimization (I-GWO) Algorithm was proposed. The grey wolf algorithm was improved by changing the convergence factor function of the neural network to enhance optimization accuracy, initializing the wolf pack position through chaotic mapping to accelerate solution speed, and dynamically adjusting weights based on step size Euclidean distance to improve optimization efficiency. Based on the monitoring data of blasting vibration velocity at the Lilou-Wuji Iron Mine, the I-GWO-BP model was established by selecting the blast center distance, the maximum single-stage charge amount, and total charge amount as input parameters. The results show that the convergence speed and accuracy of the I-GWO-BP model are better than those of the GWO-BP model and BP model, and the optimization effect is significant. The predicted values of the I-GWO-BP model are basically within the confidence band of the measured values ±0.08 cm/s, with an average absolute percentage error of 13.84%. Its prediction performance is significantly better than other prediction methods, and its prediction accuracy is high. The research results can provide some reference for predicting the blasting vibration velocity in mines.

With the continuous progress of mineral resources exploration technology, the intelligent identification of rock minerals has become increasingly important in the field of mineral composition analysis. In order to analyze the influence of complex texture structure and variable mineral morphology of rock thin section images on intelligent identification technology, an intelligent identification model of rock minerals based on improved YOLOv8 algorithm (Mineral-YOLO model) was proposed. The Mineral-YOLO model innovatively integrates the LSK module to enhance the identification capacity of the model for different target and background information differences. The ODConv technology is introduced to reduce the influence of background interference, thereby improving the performance of the convolutional network. The loss function is optimized to improve the accuracy mAP of bounding box positioning. In the model training, the self-built data set was extended using the combination enhancement technology, so that the samples of the data set were more abundant. The validation set was used to verify the trained model. The results show that the mean average accuracy of the proposed mineral intelligent identification model is 83.3% and F₁ is 78% when identifying 6 kinds of minerals. Compared with the YOLOv8 model, it is increased by 3 percentage points and 1 percentage points respectively, which proves the high efficiency and accuracy of the Mineral-YOLO model in the intelligent identification of rock minerals.

In order to realize the safe and efficient mining of metal mines in the surface protection area, taking the mining of inclined orebodies under slope in Paishanlou Gold Mine as the engineering background, the roof caving characteristics, stability conditions of caving arch and its control and utilization technology in the goaf of inclined orebodies were systematically studied. It was analyzed and concluded that the inclined orebodies in Paishanlou Gold Mine conform to the characteristics of the arch caving model, and the mathematical relationships between the critical caving span and caving height of the inclined orebodies under slope were established. A goaf caving process control scheme with the subarea mining model as the core was proposed, and the subarea open-stope mining with subsequent centralized filling mining technology was developed. The results of on-site practice show that after adopting this mining technology, the mining cost is decreased by 26.8 yuan/t, the production capacity is increased by 20%, the ore loss rate is decreased by 1.5%, the dilution rate is decreased by 3%, and the mined metal quantity is increased by 427.5 kg. This method not only meets the surface protection requirements of Paishanlou Gold Mine, but also achieves the goals of low-cost, safe and efficient mining. The research results can provide technical references for the mining of low-grade orebodies in surface protection areas.

In order to explore the potential value of a large amount of safety hazard data in the construction process of intelligent mines, taking a mine in Shandong as an example, comprehensive analysis of its historical safety hazard data from 2014 to 2023 was conducted, and a multidimensional analysis model for mine safety management was constructed. Firstly, a Multi-Layer Perceptron (MLP) was used to construct a personnel, equipment, and environmental classification model for identifying hazards and accidents. Using the Latent Dirichlet Allocation (LDA) topic model, equipment hazards were classified into eight topics of lighting, transportation, support, electrical, firefighting, blasting, ventilation, and miscellaneous. Then, based on the principle of Apriori algorithm, key information was extracted from unstructured hazard text, and the relationship between different hazard features and topics was explored and analyzed. Finally, deep analysis of the data mining results was conducted using a combination of multidimensional analysis and data visualization techniques. The results indicate that equipment related hazards are high-risk areas that require special attention in the safety management of the mine. The lack of support for the roof, potholes on sloping road surfaces, and installation of switch grounding are significant hazard topics and associated rules, and the areas such as S16181 and S18165 are gathering areas for this type of hazard. The multidimensional analysis model constructed by the research can provide a basis for the analysis of mining safety hazards.

As an environmentally friendly material, gold-tailings-based concrete has a wide range of potential applications. However, the complexity of the material composition of gold-tailings-based concrete, traditional prediction methods of compressive strength are often difficult to capture the nonlinear correlation and multivariate coupling characteristics within the material, resulting in insufficient prediction accuracy. Thus, a strength prediction model for gold-tailings-based concrete was proposed based on a deep learning binary fusion model (DP), a fusion Convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). Firstly, the mineral, chemical composition and particle size distribution of gold tailings were analyzed, and their leaching toxicity was tested according to relevant standards to ensure their safety and stability as concrete materials. Subsequently, the gold tailings concrete dataset was constructed through experiments and applied to the training and validation of the model. In order to further verify the predictive ability of the model, it was applied to real engineering cases. The results show that the proposed model exhibits high accuracy in both the training and testing process, and is capable of effectively predicting the compressive strength of the gold-tailings-based concrete. The actual engineering cases show that the error range between the predicted and measured compressive strength of concrete with 20%−40% gold tailings is −4.1%−5.7%, which further proves the potential of the model to be applied in engineering practice.