Uranium is an important raw material for the development of nuclear power, and radioactive wastewater is generated during the mining and processing of uranium resources. The treatment of radioactive wastewater has attracted much attention. This article introduces the research progress of radioactive wastewater treatment technology in uranium mines from the aspects of physical, chemical, and biological methods,and explores the interaction mechanisms, development status, advantages, and limitations of various methods for removing radioactive nuclides in the process of treating radioactive wastewater, such as oxidation-reduction, adsorption, and chelation precipitation. Reasonable suggestions and prospects are proposed for the application of radioactive wastewater treatment technology in uranium mining and metallurgy.

Dexing Copper Mine integrates the organization and management of the mining production process with modern mining technologies. Through the DIMINE 3D mining software, digital modeling and updates of the resources in certain mining areas have been completed. However, issues such as data silos in the existing CAD-based geological and surveying system, data integration challenges, and the absence of a database version in the early stages of the DIMINE software continue to constrain the mine's progress toward intelligent mining. To promote smart mining, enhance the efficiency and economic viability of mining processes, and address fundamental aspects of geology, surveying, and mining in intelligent mining construction, the functionalities of the original system were analyzed, researched and optimized. A customized development was carried out based on the DIMINE software system to achieve full coverage of the existing geological and surveying platform's functions and data at Dexing Copper Mine, ensuring the preservation of historical data and meeting the operational requirements of geological and surveying tasks.

The Husab Uranium Mine has complex ore body and issues of ore body displacement during production blasting, which leads to severe ore-waste mixing. Additionally, there is a lack of measurement methods for the truck after loading. To detect the grades after loading and distinguish the ore from the waste, further to control and measure the ore dilution and ore loss, Husab Mine designed and developed the radiomatric truck scanner. By determining the exact time points of entry and exit through infrared beam detection, and identifing the truck identities using radio frequency identification technology, the dynamic scanning is achieved. By using three sets of gamma spectrometers, the error caused by irregular ore loading and deviations in truck driving paths, is minimized. By integrating the on-site data collection system, the server-based data management system, and the client data management system, the management of multiple truck scanner become available. By connecting the truck scanner to the dispatch system, the scanner data can be used to guide trucks on where to unload at the stockpile. To assess the impact of the truck scanner on uranium mine production, both the shovel boundary model data and truck scanner data were compared against the grade control model data, which serve as a benchmark, to calculate the dilution rate and ore loss rate under each scenario, the dilution rate and ore loss rate dropped from 8.14% and 12.62% to -3.36% and 4.85%. The summary and Analysis of two months' production data revealed that, the scanner can effectively distinguish the ore with different grades and seperate the ore and waste. It’s found out that the ore transported reduced 8.4%, the recovered metal increased 4.7%, and the average grade on the stockpile increased 19.8%. These findings demonstrate the significant and positive impact of the truck scanner on production at Husab Uranium Mine.

A sandstone-type uranium deposit in Xinjiang belongs to auspicious prospecting stage with exploration line of 200 m×200 m, low exploration degree, thin ore bed and low uranium content. In addition, there are complex hydrogeological conditions with poor permeability (permeability coefficient is only 0.045 m/d) and high pressure head. No leaching test work has been carried out in this deposit area. In order to study the leaching of uranium resources in the deposit, field leaching test was carried out by adopting neutral leaching and improving the pressure leaching technology, the results show that the average extraction volume of single hole is 1.7 m³/h, the average mass concentration of uranium in leaching solution is 40.37 mg/L, the maximum mass concentration of uranium in single hole is 390.22 mg/L, and the annual leaching recovery is 13.39%, it provides technical support for the exploitation of uranium resources in the deposit.

Machine learning algorithms can automatically learn and extract features from a large amount of geological data to achieve fast and accurate lithology identification. In this paper, the logging data of several wells in a sandstone-type uranium deposit in Inner Mongolia were randomly divided into training sets and verification sets according to the ratio of 7∶2. The model structure was adjusted and the hyperparameters were optimized for training. BC1401, BC2802, BC4603 and BC7206 well were used for testing to realize the comparative analysis of 5 kinds of models, such as random forest, XGBoost, K value proximity algorithm, BP neural network and SMOTE-LSTM algorithm. The results show that SMOTE-LSTM model has the most superior stability and accuracy, with an accuracy of 84.6%.

In order to promote the informationization construction of mining enterprises and enhance the safety informationization management capabilities, we explore the application of Python programming language and PyCharm editor to establish a basic database and big data analysis model for safety inspections in a certain mining enterprise. We construct a multi-dimensional analysis and evaluation system for historical safety inspection issues, new safety inspection issues, and other data, including time, space, problem categories, and causes. We explore the laws and development trends of intrinsic safety and safety inspection problems, and take targeted solutions accurately. The establishment of databases and analysis model can also be used for comprehensive evaluation of security inspection issues, visually displaying important information such as the time, place, category, and cause of the problem, providing objective data support for timely detection of repetitive problems and hidden danger investigation, avoiding the long-term existence of security problems that cannot be eradicated, and effectively reducing security risks. At present, the security big data model is mainly applied in security inspection work, and there is still room for exploration in the fields of security management system construction, "dual control" system operation, security education and training, accident prediction and warning in the future.

With the growing global demand for nuclear energy, the evaluation of the quality of uranium resource distribution and the precise measurement of reserves have become increasingly important. The uranium fission prompt neutron logging technology, as a critical tool for uranium ore logging exploration, offers the advantage of providing quantitative results unaffected by radioactive equilibrium. However, the measurement accuracy is influenced by the pulse width and yield of the neutron source. This study utilizes a uranium logging instrument equipped with an associated particle sealed-tube neutron generator, which enables time and spatial statistics of the companion α particles emitted by the outgoing neutrons. This approach helps to eliminate interference from the source neutrons and enhances the accuracy of the logging results. In the simulation process, to obtain information on neutron emissions during logging and the timing signal responses of both the companion α detector and the epithermal neutron detector, we propose a response time simulation method that combines Monte Carlo simulation software with MATLAB. By appropriately setting the pre-delay time and gate width, a relationship curve between the count rate and uranium content in formations with uranium levels ranging from 0 to 1.00% was established. The results from validation samples indicate that the calculated uranium content show a deviation of less than 0.1% from the actual values. When the uranium content in the formation exceeds 0.5%, the relative deviation is within 10%. This method meets the exploration requirements and demonstrates significant application value for uranium ore logging.

The in-situ leaching of uranium is influenced by deposit conditions, leaching environments, and various other factors, resulting in a low utilization rate for certain resources. To facilitate the rational development of these resources, a device for preparing leaching agents was designed to enhance the leaching process by increasing the concentration of sulfuric acid in areas where the resources is difficult to leach. The results show that the relative deviation in concentration remains below 1.5%, allowing for precise and stable preparation of acid either regionally or at individual boreholes. By using a 15~20 g/L sulfuric acid solution as a leaching agent, the unit uranium leaching rate of the refractory leaching uranium resources can be increased from 24.8% to 53.7%. The amount of sulfuric acid used and the increase in residual acid are only 11.1% of those used for strengthening leaching results in the entire mining area. This device achieves the leaching of the refractory leaching uranium resources with low consumption of sulfuric acid and has little impact on subsequent hydrometallurgical processes.

In order to improve the efficiency of uranium extraction by the moving bed adsorption tower, Fluent and Edem software were used to simulate the process of uranium extraction by the moving bed adsorption tower. By adjusting the inlet flow rate, and observing the movement of resin particles, the distribution state and the settling of saturated resin in the moving bed adsorption tower, the optimal inlet flow rate was analyzed and obtained. The accuracy of the simulation results was verified by building an experimental platform, and the comparative analysis of the experimental data with the numerical simulation results confirmed the consistency of the two conclusions, thus verifying the accuracy and applicability of the model. The results show that the optimal inlet flow rate is 4 m³/h. At this flow rate, the extraction efficiency of uranium ions is maximized, which provides an important design parameter for the future design of moving bed adsorption tower.

This article studies the pretreatment methods of solutions with high iron content, high nitrate content, and low uranium content. The results show that under the pretreatment conditions of using 4% TOPO cyclohexane solution as the extractant, a volume ratio of organic phase to water phase of 1:6, an extraction time of 2 minutes, an extraction temperature of 25℃, and a mixed complexing agent as the counter extractant, the extraction efficiency reached 99.0%, and the counter extraction efficiency was 99.0%. The 5-Br-PADAP colorimetric method can accurately determine the uranium content in the pretreated solution after extraction reverse extraction of high iron, high nitrate, and low uranium content solutions. The relative standard deviation of this method is less than 8.11%, the recovery rate of spiking is 96.0%~99.5%, and the detection limit of this method is 0.013 mg/L.