Design of a 3D vision-guided robot depalletizing system for multi-gauge materials Material Removal Robot Robot Arm Robotic Manipulator Abstract: In industrial manufacturing and logistics, material depalletization by robots is one of the common applications. Material depalletization is a scenario where goods of different gauges (i.e., goods of different sizes, weights, or textures) are loaded on pallets for delivery. Earlier robot depalletization was only applicable to the unloading of single goods and required the goods to be arranged in a fixed order, and the robot did not have perception capability; the vision-guided robot depalletization system described in this paper is equipped with real-time environment perception capability to guide the grasping action, thus solving the problems of variable sizes of objects to be unloaded and irregular placement of multi-gauge material depalletization systems.   Keywords: 3D vision recognition, robot, hybrid palletizing, object positioning, depalletizing algorithm   In industrial manufacturing and logistics, various industrial robots can be used to optimize the flow of goods, and one of the common applications is the depalletization of materials. "Robotic depalletization" usually refers to the process of sequential unloading of materials from pallets using robotic arms and can be used to replace simple but heavy manual labor. In logistics, there are scenarios where goods of different gauges (i.e., different sizes, weights, or textures) are delivered in boxes, as shown in Figure 1. However, early robotic depalletizing systems were mainly controlled manually to complete robot gripping, which was only applicable to the unloading of a single cargo and required the cargo to be arranged in a fixed order, and the robot did not have the perception capability to react to external changes. However, multi-gauge material depalletization systems require robots to have real-time environmental awareness to guide the gripping action because the objects to be unloaded are variable in size and irregularly placed. With the development of various optical sensors, computer vision technology has been gradually introduced into robot grasping tasks to improve the robot's ability to acquire external information. A vision-guided robot depalletizing system usually contains five modules, which are vision information acquisition module, object localization and analysis module, grasping position calculation module, hand-eye coordinate conversion module, and motion planning module, as shown in Figure 2. Among them, the first three modules are the main part of the vision system, responsible for acquiring and processing visual information and providing object poses. The last two modules are mainly used to provide control information to the robot and complete the grasping function. In the following, we will introduce each module, common methods and implementation cases. I. Vision information acquisition module The role of the vision information acquisition module is to capture visual information and provide input for subsequent steps. At present, the commonly used visual inputs include 2D RGB images, 3D point cloud images and combined 2D and 3D RGB-D images. Among them, vision-assisted robotic arm grasping based on 2D RGB images is currently a mature solution in industry, which transforms the robot grasping problem into the problem of doing object target detection or image segmentation on RGB images. However, 2D vision lacks absolute scale information of objects and can only be used under specific conditions, such as scenarios with fixed pallets and known material sizes. For scenarios where the material gauge is unknown, the vision module is required to provide the robot with accurate absolute size information of the object to be grasped, so only 3D point cloud images or RGB-D images with a combination of 2D and 3D can be used. Compared with RGB information, RGB-D information contains spatial distance information from the camera to the object; compared with 3D point cloud images, RGB-D information contains rich color texture information. Therefore, RGB-D images can be used as the visual information input of the multi-gauge material depalletizing system. Object positioning and analysis module The object positioning and analysis module receives the data input from the vision information acquisition module, analyzes the materials present in the scene, and obtains key information such as their position and pose, and then inputs this key information into the grasping pose calculation module. Generally speaking, the material localization problem in robotic depalletizing system can be transformed into a target detection or image segmentation problem in the vision field. The RGB-D vision-based robot grasping solution can first perform 2D target detection or 2D image segmentation on the RGB image for the material, and then fuse the depth map to output the absolute size of the object and the grasping pose; or directly do target detection or segmentation on the 3D point cloud map. The following will be a brief introduction to the related work. 1.2D target detection The input of 2D target detection is the RGB image of the scene, and the output is the class and position of the object in the image, and the position is given in the form of border or center. The methods for target detection can be divided into traditional methods and deep learning based methods. Traditional target detection methods generally use a sliding window to traverse the entire image, with each window becoming a candidate region. For each candidate region, features are first extracted using SIFT, HOG and other methods, and then a classifier is trained to classify the extracted features. For example, the classical DPM algorithm uses SVM to classify the modified HOG features to achieve the effect of target detection. The traditional method has two obvious drawbacks: firstly, it is very time-consuming to traverse the whole image with a sliding window, making the algorithm's time complexity high and difficult to apply to large-scale or real-time scenarios; secondly, the features used often need to be designed manually, making such algorithms more experience-dependent and less robust. 2. Two-dimensional image segmentation Image segmentation can be regarded as a pixel-level image classification task. Depending on the meaning of the segmentation result, image segmentation can be divided into semantic segmentation and instance segmentation. Semantic segmentation classifies each pixel in an image into a corresponding category, while instance segmentation not only performs pixel-level classification, but also differentiates different instances on the basis of specific categories. Relative to the bounding box of target detection, instance segmentation can be accurate to the edges of objects; relative to semantic segmentation, instance segmentation needs to label different individuals of similar objects on the graph. In depalletizing applications, we need to extract the edges of materials precisely to calculate the grasping position, so we need to use instance segmentation techniques. The existing image segmentation techniques can be divided into traditional methods and deep learning based methods.   Most of the traditional image segmentation methods are based on the similarity or mutation of gray values in an image to determine whether pixels belong to the same class. The commonly used methods include graph theory-based methods, clustering-based methods and edge detection-based methods.   Deep learning-based methods have substantially improved the accuracy of 2D image segmentation compared to traditional methods. Typical deep neural network frameworks, such as AlexNet, VGGNet, GoogleNet, etc., add a fully connected layer at the end of the network for feature integration, followed by softmax to determine the category of the whole image. To solve the image segmentation problem, the FCN framework replaces these fully-connected layers with deconvolution layers, making the output of the network from a one-dimensional probability into a matrix with the same resolution as the input, which is the pioneering work of applying deep learning to semantic segmentation. 3. 3D target detection 3D target detection enables robots to accurately predict and plan their behavior and paths by directly computing the 3D position of objects to avoid collisions and violations. 3D target detection is divided into monocular camera, binocular camera, multiocular camera, line surface LIDAR scan, depth camera and infrared camera target detection according to the type of sensor. In general, stereo/multi-vision systems consisting of multi-vision cameras or LiDAR enable more accurate 3D point cloud measurements, where multi-view-based methods can use parallax from images of different views to obtain depth maps; point cloud-based methods obtain target information from point clouds. In comparison, since the depth data of points can be measured directly, the point cloud-based 3D target detection is essentially a 3D point delineation problem and is therefore more intuitive and accurate.   Third, the capture pose calculation module The gripping posture calculation module uses the position posture information of the target object output from the second module to calculate the gripping posture of the robot. Since there are often multiple graspable targets in a multi-gauge material depalletizing system, this module should solve the two problems of "which one to grasp" and "how to grasp". The first step is to solve the "which" problem. The goal of this problem is to select the best crawl target among many crawl targets, and the "best" here often needs to be defined by the actual requirements. Specifically, we can quantify some indicators that have an impact on the crawling judgment according to the actual situation, and then prioritize these indicators. The second step is to solve the problem of "how to catch". We can choose to analyze and calculate the grasping posture by mechanical analysis, or we can first classify the object by learning method, and then select the grasping point according to the classification, or directly regress the grasping posture.   Fourth, the hand-eye coordinate conversion module With the third module, we have obtained a feasible gripping pose. However, the gripping pose is based on the pose in the camera coordinate system, and the gripping pose needs to be converted to the robot coordinate system before motion planning can be performed. In depalletizing systems, hand-eye calibration is usually used to solve this problem. Depending on the camera fixation position, the hand-eye calibration method can be divided into two cases. One is that the camera is fixed on the robot arm and the camera moves together with the arm, called Eye-in-hand, as shown in Figure 3. In this relationship, the position relationship between the robot base and the calibration plate remains constant during the two movements of the robot arm, and the solved quantity is the position relationship between the camera and the robot end coordinate system. The other type of camera is fixed on a separate stand, called Eye-to-hand, as shown in Figure 4. In this case, the attitude relationship between the end of the robot and the calibration plate remains the same during the two movements of the arm, and the solution is the attitude relationship between the camera and the coordinate system of the robot base. Both cases are eventually transformed into a solution problem with AX=XB, and the equation can be transformed into a linear equation using Lie group and Lie algebra to solve for the rotation and translation quantities, respectively. Fifth. Motion planning module This module mainly considers the kinematics, dynamics, mechanical analysis, and motion planning of the robot to plan a feasible motion path that does not collide with the environment. By multiplying the grasping pose in the camera coordinate system obtained by the grasping pose calculation module with the conversion matrix calibrated by the hand-eye coordinate conversion module, we can get the grasping pose in the robot arm coordinate system. Based on this posture, the motion planning can be carried out and the robot arm can be guided to complete the depalletizing task. Therefore, the input of the motion planning module is the starting and target positions of the robot arm, and the output is the motion path of the robot arm.   The complete motion planning algorithm can be split into the following three steps. Step 1: Inverse kinematic solving. In order to avoid problems such as singularities, robotic arm motion planning is generally performed under joint space. Therefore, we should first perform the inverse kinematic solution based on the input poses to obtain the joint values corresponding to the poses. Step 2: Path planning. With the path planning algorithm, we can get the motion path of the robotic arm. The goal of this step is twofold: one is obstacle avoidance, to ensure that the robotic arm does not collide with other objects in the scene during its movement; the second is to improve the operation speed in order to increase the operation efficiency of the system. By planning a reasonable motion path, the running time of a single grasp of the robotic arm can be made shorter, thus improving efficiency. Step 3: Time interpolation. Although we can already get a feasible motion path through path planning, however, this path is composed of one location point after another. When the robot arm is running along this path, it needs to keep acceleration and deceleration, so it will have an impact on the running speed. For this reason, we need to perform temporal interpolation to obtain the velocity, acceleration, and time information for each point on the path as the robot arm moves to that point. In this way, the robot arm can run continuously and smoothly, thus improving efficiency.   Sixth. Implementation Example Based on the above research, a complete vision system consisting of 3D depth camera, lighting system, computer, and vision processing software can be used in the piece box material identification scenario to obtain some special information about real objects, and the information obtained through this system can be used to accomplish some special tasks, such as obtaining the box position through the vision system, which can guide the robot to grasp and obtain the box quantity information as a calibration for the task. The main components of this system, as shown in Figure 5. The 3D camera and light system are mainly used for photo imaging, where the 3D camera can obtain depth data within a certain range. And the digital image imaging is related to the illumination system. The computer, on the other hand, includes general-purpose computing and storage devices for saving images, processing images through specialized vision software, and also for network communication with other systems. The image display facilitates the operator to operate the vision processing software and monitor the system operation. Large-capacity storage is used for permanent or temporary storage of images or other data. Specialized vision software, on the other hand, includes digital image processing, image data analysis, and some special functions.   Generally speaking, a 3D depth camera has a frame rate of 1 to 30 fps, RGB image resolution of 640×480, 1280×960, special 1920×1080, 2592×1944, and a depth range of about 500mm to about 5000mm.   And depending on the price, there are different precision and range. Here is an example of a brand of 3D camera with parameters as shown in Figure 6 and accuracy as shown in Figure 7. With the 3D camera, you can get RGB images and depth images of special scenes, and according to the processing and analysis of these images (see Figure 8), you can get some information about the position, number, and information of objects in the scene. The rectangular box in Figure 9 is the box grabbing position map identified after processing. The order of upper left, lower left, upper right and lower right is "2, 3, 3, 2" respectively, that is, the robot hand will grasp two boxes on the left, three boxes on the left, three boxes on the right and two boxes on the right according to the position information given by the image recognition system. Seventh. Summary In this paper, we have introduced the framework and common methods of 3D vision-guided multi-gauge material robot depalletizing system, and defined several basic modules that the framework needs to have, namely, vision information acquisition module, object localization and analysis module, grasping position calculation module, hand-eye coordinate conversion module, and motion planning module, and explained the main tasks and common methods of each module. In practical applications, different methods can be used to implement these modules as needed without affecting the functions of other modules and the system as a whole.    

Robotics and automation: New robots improve material handling efficiency The advent of robotics means that warehouse workers can spend less time on material handling and more time digging in and serving customers. It also creates opportunities for workers to manage and "train" new equipment. For years, warehouses have been quietly incorporating robots into their material handling operations, but over the past 18 months, the trend has moved into high gear due to a surge in e-commerce demand and a general labor shortage during the new crown epidemic.   This demand is not expected to stabilize anytime soon. The surge in automation could have a ripple effect on the workforce, namely rewriting the job descriptions of many workers in manufacturing, logistics and retail. Not only will robots speed up operations, especially in areas with severe labor shortages, they will also create new jobs for workers who can manage, maintain and "train" automated equipment, suppliers say.   How are robots changing jobs in the warehouse material handling industry? How are people "training" robots to do certain jobs in DC? Currently, most robots are programmed by people who either write the software code or physically guide the robot's arm into the right position. But the next generation of robotics increasingly relies on artificial intelligence (AI) to guide the direction, giving workers complete freedom to perform other DC tasks. How is robotics changing the material handling process in the mine? In the process of mining and transportation, ores are often mixed with wood, steel nails, rags, plastic parts, waste filling pipes and other sundries. These sundries has seriously affected the safety and effectiveness of equipment in the transportation, crushing, grinding and beneficiation. In the past, manual sorting was usually used to remove it, but there are serious safety and occupational health risks in manual sorting, as well as problems such as incomplete manual sorting. The robot for mining can effectively solve the above problems. Through a multi angle, multi-dimensional stereo high-precision camera, the intelligent material removal robot quickly scans the ore on the conveyor The self-developed CRM-CNN foreign object recognition algorithm accurately locates the 3D position of the debris, controls the robot to grab the The self-developed CRM-CNN foreign object recognition algorithm accurately locates the 3D position of the debris, controls the robot to grab the foreign object, and puts it into the foreign object collection box.  

Increase digitalization and intelligent construction, mining sites to achieve the mine into a "gold mine"   Copper smelting enterprises are producing red-hot, so how about the upstream mining situation? To see the reporter's report.     　　Located in the city of Dexing, Jiangxi Province, this copper mine is a very large open-pit copper mine, the reporter saw several electric shovels are loaded ore to the electric wheel, mining site director Xie Wenbo told reporters that after the beginning of the year, the mining site has been maintaining the momentum of high productivity and efficiency, a number of indicators are more than planned to complete.     　　Xie Wenbo, director of a copper mine in Dexing, Jiangxi: In January, the total amount of ore stripped from the mine was 10 million tons, the highest level in the past five years.     　　The reporter noticed that the staff in the central control room issued an automatic driving command, a few kilometers away from the electric wheel will immediately enter the driverless mode, the copper mine's electric wheel driverless project, is the key to the construction of digital mine.     　　Xie Wenbo, director of a copper mining site in Dexing, Jiangxi province, said that the mining vehicle with a capacity of more than 200 tons can complete the whole process of loading, transporting and unloading independently. The successful trial of this project will accumulate valuable experience for us to promote the unmanned driving of the whole process of mining and further enhance the intrinsic safety level of "less people, no one" in mines.     　　Yulong Copper Mine, another copper mine located in the Changdu region of the Tibet Autonomous Region, is the highest-grade open-pit copper mine in China. By the end of 2022, the retained copper metal reserves reached 5,751,600 tons. After the start of the year, the local temperature remains cold, and the company has made sufficient preparations to guarantee positive production mining in the open pit in winter.     　　Fan Wentao, chairman of a copper company in Tibet: After the Spring Festival, we took a series of winter production safety measures to strengthen the frequency of equipment overhaul and maintenance to ensure the normal supply of ore in extreme weather. Compared with the past, the "intelligent mine" construction is put into operation to effectively improve the mine supply index.     　　Ltd. Chairman Liang Yanbo: The company has a copper ore processing capacity of 22.3 million tons per year, and the mines are currently producing at full capacity.     　　The reporter also learned in the interview, copper prices, for some of the copper companies have their own mines in response to market price fluctuations will have more advantages.     　　A copper company in Qinghai general manager Zhou Xuan: 70% of our main source of raw materials is relying on its own mines, our production of stable operation has a great guarantee.     　　Fan Xiaohui, deputy general manager of the futures department of the trading division of a copper company in Jiangxi, China: the processing fees of the enterprise is a relatively limited source of profit, but with the rise in asset prices, the benefits that can be produced from the mine is the most core source of profit.   Translated with www.DeepL.com/Translator (free version)

    What is the working principle of color sorter?   1、Selected materials from the top of the hopper into the machine, through the vibration of the vibrator device, selected materials along the channel transmission, into the sorting chamber of the observation area, and from the sensor and the background plate between the passage.   2、Under the action of light source, according to the intensity of light and color change, the system produces output signal to drive the solenoid valve work to blow out the different color particles to the waste hopper, and the good selected materials continue to fall to the finished product hopper, so as to achieve the purpose of selection.

Application of Artificial Intelligence in Coal Mine Robotics   Abstract With the rapid development of artificial intelligence technology, its application in coal mines has become more and more extensive. In the process of coal mine production, the urgency of the demand for robot replacement has accelerated the industrial application of coal mine robots and the application of artificial intelligence technology in coal mine robots. The application of artificial intelligence technology in coal mine robots is analyzed and explored, the main research contents of artificial intelligence technology and its application in industry are introduced, the current situation of the application of artificial intelligence in coal mine production is analyzed, the concept of applying artificial intelligence technology to coal mine robots effectively is elaborated, and the prospect of the development of artificial intelligence in coal mine robots is prospected.   Keywords artificial intelligence, coal mine robot, intelligent perception, intelligent decision making, intelligent monitoring，material removal robot 0 Introduction The underground production and operation process of coal mine has the problems of many people going down the shaft, high risk of disaster, high accident rate, harsh operating environment and serious environmental pollution [1]. Facing the high-risk underground operations, coal mine robots become one of the important ways to achieve the goal of safe and efficient underground coal mine production. Coal mine robots can assist or replace people to complete some dangerous mining operations and achieve safe and efficient production in coal mines. In order to achieve "no one is safe", robots are the trend to replace miners in underground operations.   With the strategy of "Made in China 2025", "German Industry 4.0" and "American Industrial Internet", 5G communication, Internet of Things, big data, cloud computing and artificial intelligence The gradual maturity of technologies such as 5G communication, Internet of Things, big data, cloud computing and artificial intelligence has greatly promoted the transformation and upgrading of China's traditional manufacturing industry [2]. As an emerging science and technology, artificial intelligence can make computer technology more accurate, fast, and convenient to complete complex scientific calculations that the human brain is incapable of undertaking, and achieve partial replacement, extension, and enhancement of the human brain, thus creating intelligent machines that can complete complex and dangerous operations instead of humans [3].   Future coal mine production will develop toward unmanned, autonomous, intelligent, and efficient, in which artificial intelligence technology will play an irreplaceable role and diverse artificial intelligence technologies will be applied to coal mine robots [4]. Although the current application of artificial intelligence in the field of industrial coal mines is still in the fumbling period, however, with the increasingly widespread application of artificial intelligence technology in the field of coal mines, the construction of unmanned operation mines is inevitable [5].   1 The urgent problems in the coal industry China's coal industry has experienced more than 40 years of development, and the mining of coal mineral resources gradually tends to be intelligent, but there are still some bottlenecks that need to be solved.   1.1 Technology and equipment need to be upgraded Although the mining and transportation of coal in China have gone through the stages of digitalization, automation and informatization, the overall technical level and production equipment are still lower than those of developed countries [6].In 2019, the former State Administration of Coal Mine Safety Supervision proposed to accelerate the industrialization and application of coal mine robots for digging, coal mining, transportation, safety control, support and rescue. The current coal mine robot is no longer just completing simple repetitive operations, it can sense the surrounding environment and give real-time feedback to the outside world, but it does not yet have independent thinking, identification, reasoning, judgment and decision-making capabilities, and still needs human participation to complete some complex work tasks.   1.2 Serious safety hazards The coal industry is a high-risk industry, and there are various hazards in every step of production, water, fire, gas, coal dust, geological formations and other disasters are frequent, and the unknown complex underground environment seriously threatens the life safety of underground operators. Although the intelligent monitoring and early warning technology of coal mines based on the Internet of Things, big data and cloud computing has largely reduced the occurrence of accidents and guaranteed the safe production of coal mines, there are still many problems. The poor accuracy and sensitivity of sensors lead to incomplete and untimely collection of precursor information; monitoring systems are independent of each other and have a single function, and the integration and application integration depth of the cloud platform is not deep enough; monitoring system database security is weak; monitoring equipment lacks deep learning as well as self-adaptive capability [7].   1.3 Serious environmental pollution Coal mines produce coal dust during the mining process, and also produce harmful gases such as carbon monoxide and carbon dioxide to pollute the atmosphere [8]. At the same time, the production effluent from coal mining contains a large amount of heavy metals and acidic substances, which can easily seep into the soil or enter the groundwater to pollute the geology and water sources. Coal mining projects will encroach on a large amount of vegetation and agricultural land, and the land is prone to collapse after mining leading to the destruction of the surface layer [9].   2 The main research content of artificial intelligence 2.1 Pattern recognition Pattern recognition in artificial intelligence technology uses the powerful data collection, analysis and processing functions of advanced computer technology to simulate human perception and recognition of the external environment by setting the corresponding programs in advance. Intelligent robots incorporating pattern recognition can better simulate human sensory abilities, recognize characters, sounds, images, scenes and their fused information with high accuracy, and accurately perceive and model the surrounding environment through the acquisition of multi-source information [10].   Machine vision in artificial intelligence technology, as one of the most important environmental perception modalities, simulates human visual capabilities to improve the robot's understanding of the downhole environment, operational processes, and feedback phenomena. Intelligent robots incorporating machine vision are, first, able to adapt well to the downhole operating environment and collaborate well with other artificial devices; second, able to capture more information about the external landscape and to understand and dig deeper into the content of images through stereo vision, visual inspection, and dynamic image analysis techniques; and third, able to judge the underground feedback phenomena of the operating process and feed back information about the robot's status to the motion control system [11].   2.2 Expert system Expert systems are technologies that model the knowledge and experience of human experts and are used to solve problems such as system decisions, processes, and failures. Through artificial intelligence techniques, knowledge systems are created for downhole systems that simulate humans to solve practical problems encountered during operations. Human experts can predict system failures, determine failure points and generate troubleshooting solutions based on the current state of the system, such as equipment displays and sounds, operational data parameters, and the state of the product, when solving real-world problems. Therefore, expert systems are commonly used for fault prediction, diagnosis and troubleshooting. In addition, in the manufacturing industry, expert systems are also used for production planning decisions, production process optimization, production coordination, and optimization of equipment parameters.   2.3 Machine Learning Machine learning in artificial intelligence technologies mimics human learning capabilities through model frameworks and algorithms to automatically extract intrinsic laws through training data, environmental information, and feedback to improve system performance and enhance environmental adaptation and robustness. Robots that incorporate machine learning have human-like law extraction and knowledge summarization capabilities to identify effective information from the large amount of information resources collected and learn to improve their own intelligence. Machine learning technology can effectively solve a series of problems in unexpected situations and largely reduce labor and production costs [12].   2.4 Distributed Artificial Intelligence Distributed artificial intelligence system coordinates the scheduling and control of heterogeneous multi-intelligent body systems by scientifically and rationally combining artificial intelligence and computer technology, so as to enhance the performance of the artificial intelligence system, improve the task execution capability, and increase the efficiency of the cooperative work of each independent system in the intelligent robot. When the intelligent robot encounters some unexpected situations, it can still guarantee each subsystem to carry out normal work. The current distributed artificial intelligence system is still in the initial stage of research and development, and the technical difficulty lies in how to coordinate the operation rules of different systems [13].   3 Status of application of artificial intelligence in coal mine robots 3.1 Application of artificial intelligence in the motion control of coal mine robots In order to ensure that coal mine robots can operate properly in complex underground environments, research scholars have applied artificial intelligence technologies such as expert systems and artificial neural networks to robot motion control methods, algorithms and collaborative operations. By simulating human expert thinking and knowledge level, coal mine robots can solve some complex multidimensional nonlinear problems, reduce the amount of operations for dynamical system analysis, parameter setting and data processing, and improve control efficiency and accuracy.   Wang Nian et al [14] researchers designed an intelligent mine robot based on embedded ucos and used GSM network to realize remote control of the device; Zhang Chuancai et al [15] researchers used BP neural network to establish a measurement method to determine the robot turning angle based on motor speed and running time, which can provide angle parameters for robot path planning; Wang Xuesong et al [16] researchers personnel approximated the kinetic uncertain parameters based on the improved Elman neural network and sent control commands for the coal mine robot servo system using a neuro-fuzzy controller; Song Xin et al [17] researchers applied neural networks in the field of robot control to accomplish actions such as robotic arm multi-joint coupling control, end trajectory planning, and hydraulic valve control.     3.2 Application of Artificial Intelligence in Intelligent Perception and Danger Prediction of Coal Mine Robot Mining inspection robots realize all-round perception of underground environment information by carrying various sensors, real-time monitoring of instrument and equipment failure, personnel safety and disaster information such as gas, coal dust, water and fire, and timely issuance of early warning to reduce the occurrence of coal mine accidents. For several technical difficulties such as inaccurate identification and untimely monitoring in complex underground environments, researchers use deep learning, pattern recognition, and expert system technologies to further enhance the robot's accurate identification and real-time monitoring of underground emergent hazards.   Researchers in Lu Wanjie et al [18] used deep learning algorithms based on convolutional neural networks to model and train coal mine equipment so that the underground inspection robot could accurately identify the type of coal mine equipment; researchers in Zhang Fan et al [19] proposed a mining image reconstruction method based on residual neural networks for the disturbing effects of underground noise on the visualized operating environment, which effectively improved the clarity of monitoring images and Nie Zhen et al [20] used a genetic algorithm based on BP neural network to build a tunnel gas environment intelligent detection system and obtain real-time data of gas concentration distribution on different tunnel sections in the path of coal mine inspection robots; Pan Yue et al [21] used BP neural network to establish a diagnostic model for fan faults and establish a mapping between fan fault types and fan rotor vibration frequency bands, thus realizing fan fault diagnosis. relationship, and then achieve fan fault diagnosis; Yan Junjie et al [22] researchers based on artificial neural network to establish a diagnostic model for coal mine machinery gear faults, using the input signal to train the neural network model, classify the output signal, and then determine the gear fault.   3.3 Application of artificial intelligence in autonomous positioning navigation and map construction for coal mine robots Achieving autonomous positioning and navigation in complex unstructured coal mine environments requires consideration of both the inability of GPS technology to be applied directly downhole and the need to overcome interference from external factors such as dust, temperature, humidity, noise, and airflow, which places higher demands on autonomous and accurate positioning and navigation technology for robots in restricted and closed environments downhole. Map construction, positioning navigation, path planning, and real-time obstacle avoidance of coal mine robots based on artificial intelligence technology have become hot spots for applied research.   Bai Yun [23] proposed variable structure fuzzy neural network and applied it to the environment sensing process of snake underground rescue robots, fusing multi-source sensor data to achieve obstacle recognition and environment modeling of snake robots in harsh environments; Fu Hua et al [24] researchers used artificial neural network model to model and dynamically describe the workspace of intelligent coal mine monitoring system, using neural network model for robot obstacle avoidance path planning; Zhang Yaofeng et al [25] researchers used Elman network-based compensation for ultrasonic sensor measurement error of underground robot, which greatly improved the accuracy of ultrasonic ranging and obstacle detection; Zhai Guodong et al [26] researchers summarized binocular vision technology in coal mine rescue robot to obtain accident scene information and achieve autonomous obstacle avoidance and path planning, including pattern classification and recognition, visual measurement and 3D reconstruction, combined measurement and localization, and visual servo control; Ma Hongwei et al [27] researchers constructed a depth camera-based machine vision system and proposed a depth vision-based navigation method, in which the robot is equipped with an RGB-D depth camera for data acquisition to achieve map creation and autonomous navigation.   4 Research on intelligent coal mine robots There are various kinds of artificial intelligence technologies, and the main research contents applied to the field of coal mine robots include multimodal fusion intelligent perception, knowledge learning and intelligent decision making, and intelligent control cooperative operation. Through perception, learning, decision making, and collaborative control, the intelligent development of coal mine robots is realized.   4.1 Multi-modal fusion intelligent perception The coal mine robot is equipped with various explosion-proof, high-precision and high-reliability sensors to build an intelligent perception system with multimodal fusion of vision, hearing, smell, touch, etc., to complete intelligent recognition and analysis, abnormal sound recognition, abnormal temperature monitoring, smoke detection, harmful gas concentration detection, autonomous obstacle avoidance, autonomous grasping and other operations.   (1) Research on machine vision recognition and visual detection technologies in coal mine application scenarios. Through image processing and understanding, the robot is able to, firstly, identify and monitor equipment digital meters, LCD screens, indicators, valves, etc.; secondly, detect pipeline liquid dripping, tape running and cracking; thirdly, carry out personnel intrusion, personnel on duty, personnel dressing detection; fourthly, identify and track foreign objects such as gangue, anchor rods, road logs, iron pipes, etc. that appear on the tape.   (2) Research on technologies such as robot hearing i.e. sound detection and recognition in coal mine application scenarios. Using high-sensitivity sound pickup sensor, high-speed DSP digital signal processor, combined with adaptive dynamic noise reduction processing technology, audio feature extraction and detection model algorithm recognition technology to identify abnormal sound in the mine.   (3) Research on intelligent recognition technology for robotic olfaction i.e. gas detection in coal mine application scenarios. Accurate detection of methane, hydrogen sulfide, carbon monoxide, oxygen and other gases in the environment and whether smoke exceeds the limit, timely detection of gas leaks and early warning of fires.   (4) Research on haptic technology for robots in coal mine application scenarios. Collect the temperature of objects such as motors, pumps, bearings, rollers, tapes, etc. through contact or non-contact and analyze the data; through force sensing equipment, real-time monitoring of contact force, gripping force, operating force, internal stress, and realize force sensing and safety control.   4.2 Knowledge learning and intelligent decision making In view of the current problems of incompatible coal mine robot system protocols and lack of information sharing and integration, we will deeply integrate coal mine robots with new generation information technology, build a generalized, standard and flexible system for mutual learning and knowledge sharing of coal mine robots, and break through the technical bottlenecks of coal mine robot scene understanding, safety detection, precise positioning, autonomous perception and efficient navigation. Realize cloud-based online services for common technologies of coal mine robots to solve the limitations of individual robots and improve the intelligent decision making of coal mine robots.   (1) Establish a learning and generalization framework that integrates the individual and the whole. At the individual level, a single robot integrates sensing, decision-making, control, collaboration, and human-robot interaction information during operation, and conducts incremental, real-time, online training through an artificial intelligence learning framework represented by neural networks to dynamically adjust the robot's operational state and achieve full-cycle optimal control and decision-making. At the overall level, multi-robots upload and distribute their learned knowledge among themselves through the new generation of information technology, so that when a robot faces a brand-new operational task, it can quickly familiarize itself with the operational characteristics with the knowledge results of other robots, reduce re-learning time, and enhance the overall system's task flexibility and adaptability.   (2) Establish an operation mode in which the robot body and the cloud are integrated. Breakthrough the traditional robot R&D and integration model, and realize a new robot R&D and integration route that integrates the local lightweight robot body with the high-performance data processing capability in the cloud with the help of "5G + cloud computing". The algorithms that require strong computing power, such as intelligent environment perception, pattern recognition, map construction, and autonomous navigation, are moved to the cloud, and the local robot uploads the data of on-board sensors and actuators to the cloud in real time, and optimizes the calculation of perception, modeling, and execution through the powerful data processing and computing power of the cloud. The result is sent to the local robot in real time, which reduces the computational burden of the local robot and shifts more hardware resources to the sensor and execution side to achieve a lightweight, streamlined, and high-performance operational robot design.       4.3 Intelligent control of cooperative operation Integrating deep learning and laser/visual SLAM technologies into coal mine robots, combined with intelligent sensing system of multimodal fusion, realizes the functions of autonomous movement, precise positioning, position adjustment, intelligent operation planning, autonomous operation and intelligent disaster sensing of coal mine robots in complex mine environment, and realizes intelligent collaborative control of detection, digging and support operation processes.   (1) Integrating neural network technology into the collaborative operation control and planning of multiple coal mine robots. Self-organization, self-grouping and self-coordination of mobile robots in mines to achieve integration of heterogeneous equipment. Through intelligent task decomposition, task assignment, and load balancing technologies, a swarm of robots in complex environments in mines is formed, and technologies such as autonomous navigation in underground space, multi-sensor state sensing, intelligent operation planning, and multi-robot collaborative control are applied to realize efficient collaborative operations among multiple robots in workface digging, drilling, extraction, transportation, and support.   (2) Extending the mode of human interaction with a single robot to human interaction with multiple robot groups, and realizing the intervention and collaboration of operators on robot groups. During the operation of coal mine robots, each heterogeneous robot with different functions forms a complex multi-robot collaborative swarm. At the same time, the multi-robot collaborative swarm needs to be able to collaborate with the operator in depth. Through AI technology, we can break through the simple "command-execution-display" mode of existing human-robot interaction technology, and integrate human intervention into the control cycle to realize a new mode of human-robot interaction with "human in the loop", and realize the "unmanned underground system group + unmanned underground system group + unmanned underground system group". underground unmanned system group + underground operator" mode of operation, to improve the overall system's operational efficiency, task flexibility and robustness.   In order to achieve the goal of smart coal mine, we will carry out research on "coal mine robot +", "coal mine robot + 5G" to realize comprehensive sensing and interconnection, full domain information sharing and multi-channel human-robot interaction; "coal mine robot + cloud computing "Coal mine robot + cloud computing" realizes the compatibility of lightweight and low-cost robot ontology and high-performance learning computing capability; "coal mine robot + big data" realizes dynamic prediction, information integration, and provides data basis for robot evolutionary learning; "coal mine robot + AI "Mine robot + AI" realizes intelligent autonomous perception, optimal analysis and decision making, and knowledge learning evolution, thus forming a complete intelligent system of three-dimensional perception, autonomous learning and cooperative control in the mine.   5 Future Outlook Artificial intelligence has been widely applied in the field of coal mine robotics, and more research results have been achieved. However, as an emerging frontier technology, artificial intelligence still has limitations.   (1) The current AI technology is mainly oriented to a single task, and a general AI framework that can face multiple tasks has not yet been realized. For example, the models trained for image recognition cannot be used for sound detection and recognition; the algorithm framework for recognizing a specific target object cannot be extended to the recognition of arbitrary target objects, and the data set needs to be constructed and retrained when a new classification target appears. This feature limits the application of AI in complex task scenarios.   (2) Artificial intelligence algorithms need to rely on a large amount of data, and operations such as data collection, processing, calibration and alignment need to be done manually, which is less efficient. How to use a smaller amount of data to achieve higher performance has become one of the current research hotspots of artificial intelligence methods.   (3) There are many types of coal mine robots, and there exist a large number of sensing devices, driving devices and actuating devices. The data formats of each device are diverse, and it is difficult to form a unified data interface, making the data between each system independent of each other. The incompatible data makes it difficult for the AI system to coordinate the robots in each part of the coal mine production process, and to obtain enough data to form a closed-loop unified plan for the entire production process.   (4) The environment in which coal mine robots operate is extremely dangerous, so current AI systems alone cannot guarantee a high level of safety and stability. How to integrate the AI system with the operator's manual intervention and integrate human intervention into the whole AI system's operation loop becomes one of the key elements to be addressed in the next step.   In the future, AI systems applied to coal mine robots will develop toward generalization, low overhead, unification, and human-machine collaboration, with the emergence of a general AI algorithm framework for multiple tasks that continuously learns and evolves online using small amounts of data and low-cost training methods, capable of integrating key data from all aspects of coal mine production for integrated computing and scheduling, and able to collaborate with each other and humans to achieve It is capable of collaborating with humans to achieve efficient, safe, and autonomous coal mine production.   6 Conclusion With the development of artificial intelligence technology, the coal mining industry will see a major change. With the efficient model building, parallel computing, and planning capabilities of AI, the intelligence and automation of coal mine robots will reach a new level, truly realizing the unmanned and safe requirements of coal mine production. At the same time, artificial intelligence will enable a significant increase in coal mine production efficiency and promote the safe, healthy and sustainable development of the coal mine industry.  

Helping wollastonite intelligent sorting, extending the development of the enterprise industry chain!   Wollastonite as a new industrial mineral raw materials, mainly produced in the acidic and contact metamorphic zone, for the composition of the main mineral components, wollastonite in China's distribution, from the north to Heilongjiang, south to Hainan Province, and Xinjiang have deposits distribution. 19 provinces, autonomous regions found wollastonite ore production 86 (of which 8 super-large, large 13, medium-sized 24, small 32, 9 ore points). Among the 77 deposits, 244 million tonnes of wollastonite ore were identified, and 227 million tonnes of wollastonite ore were retained, with Huojiadian in Lishu County, Jilin Province having the largest retained reserves, accounting for 40% of the country's total retained ore reserves; the remaining four provinces, Yunnan, Jiangxi, Qinghai and Liaoning, accounted for 49% of the country's retained ore reserves; five provinces, Zhejiang, Hunan, Anhui, Inner Mongolia and Guangdong, accounted for The remaining four provinces are Yunnan, Jiangxi, Qinghai and Liaoning, accounting for 49% of the country's reserves; Zhejiang, Hunan, Anhui, Inner Mongolia and Guangdong, accounting for 10% of the country's reserves; Jiangsu, Guangxi, Hubei and Heilongjiang, accounting for 1% of the country's reserves.     China's wollastonite resource reserves   The consumption structure of wollastonite in China: glazed bricks and ceramic blanks mainly, accounting for about 50%; metallurgical protection slag and welding treaties account for 20%; paints and coatings account for about 10%; plastics, rubber and paper fillers account for about 10%; construction materials, asbestos replacement and other applications account for about 10%.     For a long time China's wollastonite resources mining and sorting processing has been in the rough development, wollastonite mineral processing level and scale compared with Western countries is still relatively backward. Performance in the application area is narrower, the product grade is lower. In recent years, with the regulation of national industrial policies and market mechanisms, the development of mining enterprises has gradually entered the track of standardised and healthy development, and the production scale, product categories and economic benefits of wollastonite enterprises have been substantially improved. In particular, there has been great progress and development in mining and beneficiation technology.   With the development of time, wollastonite mine resources are constantly facing depletion, easy to mine stone reduction, lower ore grade, increased sorting costs and other issues, as well as national restrictions on the approval of new tailing ponds, has been the existing tailing pond capacity reduced, resulting in a significant increase in tailing disposal costs, while external environmental protection also brings huge pressure on mining enterprises. How to reduce costs, improve the economic efficiency of the mine, but also in line with green environmental protection, the introduction of new equipment, new technology is a good choice.   Recently a large domestic wollastonite enterprise has improved its sorting process by quoting Mingde artificial intelligence sorting equipment. The raw wollastonite ore enters the sorting equipment after crushing and dissociation, and according to pre-learning and modelling, the concentrate and tailings are accurately identified, enriching the low burn loss wollastonite concentrate. The overall sorting effect, concentrate yield and stable sorting of the equipment all meet the customer's psychological expectations.   Guangxi a wollastonite enterprise original sorting process mainly to large particle size manual hand-selected mainly, the follow-up by understanding the use of Mingde artificial intelligence ore sorting machine, after investigation to understand through the Mingde artificial intelligence sorting machine, can fully sort out the wollastonite original ore mixed with black stone, calcite and a small amount of quartzite, through sorting to achieve the purpose of electing wollastonite.     Tailings                                                     Finished product   Mingde artificial intelligence sorting equipment, no use of pharmaceuticals, no grinding, equipment operation mainly electric drive, through the compressed air for blowing sorting, sorting cost is low, basic pollution-free, equipment intelligence degree is high, simple operation, advanced technology, fully able to meet the enterprise sorting requirements. Standing in the new era, green mines, intelligent ore sorting has belonged to the development of new trends, new directions, through the introduction of new technologies to speed up the construction process of intelligent, automated mining enterprises, can greatly enhance the use of ore resources and economic benefits of enterprises.

Potential Economic Value Exceeds Trillion Yuan, Large-Scale Deposits Announced! Top 10 Major Results of 2022 Geological Search Announced   The mining industry was also deeply affected by the epidemic emanating from many places, the real estate slump and the difficult economic downturn in 2022. In a letter of reply from General Secretary Xi Jinping to all geologists of the Sixth Geological Brigade of Shandong Province Geological and Mining Bureau, which rekindled the enthusiasm of mining industry people across the country to find mines, what kind of answer sheet will be delivered to the people of the country with the efforts of many geologists? Recently, the official website of the Chinese Geological Society announced the selection results of the major achievements in geological prospecting for 2022.   I. High-grade and high-quality large-scale iron ore deposits found in Baijian area, Shahe City, Hebei Province The Ninth Geological Brigade of the Hebei Geological and Mineral Exploration and Development Bureau has discovered a high-grade large-scale iron ore deposit in Baijian, Shahe City, which is the largest smectite iron ore deposit ever discovered in Hebei Province and one of the few high-grade unexplored iron ore deposits in China. The Ninth Geological Brigade's technicians have continuously innovated the theory and method of finding iron ore in the Hand Xing style, and have refined and summarised the "five-in-one" method of finding iron ore, which has effectively guided the search for iron ore in the depths and periphery of the Hand Xing style. The project submitted 104,427,000 tonnes of proven + controlled + inferred iron ore resources with an average grade of 48.02% total iron (TFe), 44.15% magnetic iron (mFe) and 4412.8 tonnes of magnetite associated cobalt, with a potential economic value of over RMB 100 billion. The iron ore can be used as raw material for refined steel and high-quality steel. The economic and social benefits arising from its development and utilization are of great significance to the local economic development and the security of national energy resources.   II. Significant breakthrough in the search for heavy rare earth ore in Shi Ping, Anyuan County, Jiangxi Province   The team, led by Wang Xianguang from the Jiangxi Mineral Resources Security Service Centre, has detected a super-large weathered crust ionic heavy rare earth deposit after five years of comprehensive exploration in the area where the South Ridge and Wuyi Mountain tungsten-tin rare earth polymetallic mineralisation zones meet in southern Jiangxi. A total of seven heavy rare earth sections were circled in the mine area, of which XX.XX million tons of inferred full-phase heavy rare earth resources with an average grade of 0.088% and XX.XX million tons of leached-phase heavy rare earth resources with a grade of 0.060% were detected in the root-back section in 2022; a "five elements in one" model for ionic rare earths and a "geological survey + weathering crust" model were constructed. The "geological survey + weathering crust trapping + Gannan drilling + shallow drilling + rapid field analysis + experimental testing" is a green, efficient and economical exploration method for ionic rare earths, which is of universal significance to the exploration and evaluation of ionic heavy rare earth deposits. The results of the survey have been applied to other sections such as Xinfeng in the Shishiping mining area and Xunwu County and Gan County in the Nanling area, and a number of large scale heavy rare earths have been found in Xiahu and other areas with significant results.    3,A major breakthrough in silver polymetallic exploration in Erdaokan Village, Nengjiang City, Heilongjiang Province   The Heilongjiang Natural Resources Survey Institute and the Heilongjiang Geophysical and Geochemical Survey Institute have made a major breakthrough in the exploration for silver polymetallic ores in Erdokan Village, Nengjiang City, submitting a total of 1,777 tonnes of silver metal with an average grade of 431.10 g/t. The size of the silver ore is large and the manganese ore is medium. The silver polymetallic ore body is hosted in marine sedimentary rocks of the Upper Silurian-Middle Devonian Neihuohe Formation, closely syngenetic with gabbroic veins, with moderately acidic sub-volcanic rocks, and the ore is a tectonic breccia cemented by quartz veins. This deposit is the first large independent silver deposit in Heilongjiang Province, filling the gap of independent silver ore in Heilongjiang Province, and is the largest deposit in the national silver ore search during the 13th Five-Year Plan. Through the ore processing and metallurgy technology performance and economic overview study, it is believed that after the mine is put into operation, the average annual profit is about 340 million yuan, which can serve nearly 12 years, and the average annual income tax paid is about 84 million yuan, which will greatly improve the local financial revenue and effectively drive the regional GDP.   4. Innovation in ore search technology and major breakthrough in ore search for the super-large gold deposit in Dayingzhuang, Jiaodong   The Sixth Geological Brigade of Shandong Geological and Mineral Exploration and Development Bureau and Zhaoguang Mining Co., Ltd. jointly detected the largest gold resource of over 100 tons in the middle section of the Zhaoping Fracture Zone. The project clarified the output pattern of the ore body and solved the problem of what location is conducive to ore formation; proposed a new method for finding ore by segmentation and enrichment, solving the problem of where to find ore; proposed a quantitative prediction method for the trend extrapolation + body ore content, solving the problem of how much resources are available at depth. It has achieved a major breakthrough in ore search, extended the service life of the mine, helped the Zhaoping metallogenic belt to become a 1,000-ton gold belt, enriched and improved the gold metallogenic theory in the Jiaodong region, and played a positive role in promoting the gold metallogenic law and ore formation prediction in the region, which will play a leading role in the new round of strategic action of ore search and breakthrough, and drive the region to make new breakthroughs in ore search, and provide a new base for building a "10,000-ton" gold industry. The project will play a leading and exemplary role in the new round of strategic action to find and breakthrough in ore, drive new breakthroughs in regional ore search, provide favorable support for building a 10,000-ton gold industry base in Shandong, China, and make significant contributions to the security of national energy resources.   5. Large fluorspar mine discovered in Cilimiao mine, Siziwangqi, Inner Mongolia Autonomous Region   Inner Mongolia Geological Exploration Co., Ltd. has identified a large fluorspar deposit in the Cilimiao mining area of Siziwang Banner, with a total identified fluorspar ore volume of 8,493,000 tonnes and mineral volume of 4,681,000 tonnes, with an average grade of 55.12% CaF2. Manganese ore volume was 102,800 tonnes with an average grade of 21.95% Mn. The fluorite ore body is produced in the crystalline tuff of the Dashi Zhai Formation II section of the Middle Permian and is strictly controlled by carbonate rock stratigraphy and interstratigraphic structure, and is a stratigraphically controlled (modified) type of stratiform fluorite deposit in carbonate rocks. The discovery of this deposit has, to a certain extent, changed the status quo of the small proportion and scale of stratified fluorite ore in China, making a positive contribution to the security of national strategic mineral resources, driving local economic development, showing a good prospect for fluorite prospecting in the area, and playing a role as a model and guide for exploration.   6,Ultra-large graphite ore was found in the area of Tullahai River in Golmud, Qinghai   The first super-large scale crystalline graphite deposit in Qinghai was detected by the Qinghai Geological Survey in the area of Tullahai River in Golmud City, with an inferred graphite mineral resource of 16,564,900 tonnes, with an average grade of 4.86% fixed carbon and 91.14% +100 mesh large scale graphite, achieving a major breakthrough in graphite prospecting in the shallow coverage area of the Qinghai-Tibet Plateau. The deposit is hosted in the gneiss and dacite of the Jinshukou Group in the Lower Palaeogene, and is a layer-controlled regional metamorphic graphite deposit. The ore is of excellent selectivity, with a recovery rate of 96% or more and a fixed carbon grade of 95.26% or more in concentrate, which meets the quality standard of high carbon graphite and has high economic value.   7. Largest hidden monolithic phosphorus-rich deposit in Asia discovered in Zhenxiong, Yunnan   The Yunnan Geological Survey Institute (YGSI) has made a major breakthrough in the search for deep-seated phosphate ores by discovering the largest occulted monolithic phosphorus-rich deposit in Asia, the Yangchang Super Large Phosphate Deposit, in the Zhenxiong area. The Yangchang phosphate deposit is a shallow marine sedimentary phosphate massive deposit in the Lower Cambrian, characterised by "large reserves, high quality ore and concentrated resource distribution". The inferred resource of phosphate ore is 1,197 million tonnes, with 51.87% of Grade I+II, and the potential resource of phosphate ore at depth and periphery is predicted to be over 10 billion tonnes, with a potential economic value of over RMB 1 trillion. The discovery and evaluation of the deposit provides a solid resource base for Yunnan Province to build a 100-billion-dollar nationally important phosphorus chemical industry base, and provides a resource guarantee for national energy resource security and food security.   8,Over 600 million tonnes of oil discovered in the Permian Fengcheng Formation in the Mahu Depression of the Junggar Basin   For the first time, PetroChina Xinjiang Oilfield Branch made a major discovery in the ancient alkali lake hydrocarbon source formation of the Permian Fengcheng Formation in the Mahu Depression of the Junggar Basin, adding 656 million tonnes of tertiary petroleum geological reserves and opening up a new field of basin-scale exploration. The results have established an innovative lower limit sequence for the pore throat structure of all types of reservoirs in the Fengcheng Formation, revealing the full-scale reservoir formation mechanism of conventional and unconventional oil reservoirs; and established a "source-storage coupling" based on the orderly formation of conventional oil - tight oil - shale oil. The results have established a "source-storage coupling" based on the formation model of conventional oil - tight oil - shale oil in an orderly manner, and implemented a 2500km2 exploration favorable zone; integrated and innovated a comprehensive exploration technology for all types of reservoirs, and increased the success rate of oil test layers in the Fengcheng Formation from 35% to 91%; guided the new discovery of extra-large unconventional oil reservoirs within 600 million tonne class sources. The results achieved the first successful exploration of a full oil and gas system in the world, enriching and developing the geological theory of oil and gas system, which is of great significance to safeguard national energy security, promote the implementation of the "Belt and Road" strategy and social stability in the Xinjiang region.       9, The first deep shale gas field in a complex tectonic zone on the basin margin of China discovered in the Qijiang area The deep shale gas exploration innovation team of China Petroleum & Chemical Corporation Exploration Branch has discovered the first deep shale gas field in the complex tectonic zone of China's basin margin, the Qijiang shale gas field, and submitted the first phase of proven geological reserves of 145,968 million cubic metres of shale gas in the Dingshan District block, marking the birth of another large, fully assembled shale gas field of over 100 billion cubic metres in China. The project has discovered the development mechanism of "highly porous" high-quality reservoirs for deep shale gas, formed a new understanding of "overpressure and gas-rich", effectively broke through the "sweet spot" prediction technology and engineering It has achieved a major breakthrough in deep shale gas exploration, and the implementation of a trillion cubic meter gas-rich zone in the complex tectonic zone on the southeast Sichuan basin margin, laying the theoretical and technical foundation for the expansion of shale gas to ultra-deep depths greater than 4,500m. The discovery of the Qijiang shale gas field is of great significance to the national green and low-carbon development strategy, the achievement of "carbon peak" and "carbon neutral" emission reduction targets, and the protection of national energy security.   10. A major breakthrough in coal resource search in the Jiazhai-Tangjie coal mine area in Baofeng County, Henan Province     The Henan Provincial Land and Space Survey and Planning Institute has made a major breakthrough in coal prospecting in Jazhai-Tangjie coal mine in Baofeng County, identifying a total of 143,335,000 tonnes of coal resources, including 292,638,000 tonnes of proven resources, 627,087,000 tonnes of controlled resources and 513,627,000 tonnes of inferred resources. The project has identified in detail the stratigraphic sequence, lithology and physical characteristics of the strata in the survey area, and the coal-bearing strata are the Permian Shanxi Group and the Lower Shi Box Group, with 7 recoverable coal seams, mainly 1/3 coking coal, coking coal and fertilizer coal, and the coal quality is characterized by low ash, low sulphur and high calorific coal, with excellent coal quality, which is a good coking and power coal. The technical conditions of mining such as hydrogeology, engineering geology and environmental geology have been identified in detail. The project exploration results provide a reliable geological basis for the later exploration to mining, which is of great significance to the sustainable and stable development of the mining enterprise and provides a strong guarantee for the succession of resources for the construction of Henan coal energy base.   Source: Geological Society of China

Introduction to the three beneficiation processes of re-election, flotation and magnetic separation   China's vast territory, rich and productive, has been proven mineral resources of many kinds, the total amount of more, but the actual development was found that a variety of mineral resources rich ore less, more poor ore, less single ore, more associated ore, in order to make full use of these mineral resources, the country vigorously develop the beneficiation process.   Today, re-election, flotation, magnetic separation process to carry the majority of mineral beneficiation, let us take a look at them!   A, Gravity  separation process 1、Preparation of raw materials The raw materials for re-election should go through the process of crushing, screening and grinding in order to meet the feed size of the re-election equipment.   2、Water washing and desliming The re-elected material should be washed with water to remove the excess silt and stone powder on the ore, so as not to mix into the re-elected concentrate and affect the grade of the ore.   3、Gravity separation classification The Gravity separation method commonly used Gravity separation equipment jigger, shaking table, chute, etc., the use of different specific gravity of the particles in the medium (usually water, air, heavy liquid or suspension) in the different settling speed, will be useful minerals and associated with the separation of mineral particles.   Second, flotation beneficiation process 1、Preparation of raw materials Firstly, the raw material should be crushed and ground to a fineness of 0.2mm or less, and then added to the flotation chemicals and stirred well in a mixing drum.   Grinding can make the useful mineral particles embedded in the ore and associated vein as far as possible to separate; flotation agent is to strengthen the difference of floatability between useful minerals and associated vein; stirring is to let the agent and the mineral particles fully function.   2、Flotation The slurry is charged into the flotation machine, which relies on mechanical agitation or air charging to produce a large number of bubbles in the slurry.   After the role of flotation chemicals, the hydrophobicity of useful mineral particles more obvious, easier to attach to the bubbles, while other minerals with hydrophilic stay in the pulp, completing an important step in the separation of minerals.   The mineral particles attached to the bubbles float to the surface of the pulp with the bubbles, and then are scraped out by the rotating scraper, which is the concentrate we want, and the product left in the pulp is the "tailings".   Three, magnetic separation beneficiation process 1、Preparation of raw materials The raw material preparation stage of the magnetic separation process also requires the process of crushing and grinding, first grinding the ore into fine particles.   2、Grading Use classifier to classify the ore particles, which can remove most of the useless ore particles in advance and reduce the working pressure of magnetic separation equipment.   3、Magnetic separation Using the characteristics of different ores with or without magnetism, the magnetic separator can draw out the useful ore particles with magnetism from the non-magnetic associated vein, to achieve the purpose of separating the two.   In actual production, these three beneficiation processes can not only be independent beneficiation, encounter with a variety of types of associated ore, but also can be used jointly, the various types of concentrates in the ore one by one, to understand their process flow, to facilitate our processing of hundreds of ore separation.    

Intelligent sorting of tailings, get rid of "useless", Meide Optoelectronics realize the value of tailings regeneration!   In order to implement the "14th Five-Year Plan" and respond to the 20th National Congress report, it is pointed out that the green transformation of the development mode, establish and practice the concept of green water and green mountain is the silver mountain. In order to respond to national policies and development needs, mining enterprises to enhance the use of mineral resources and economic benefits, reduce environmental damage and impact, and ultimately achieve the green mine, intelligent mine construction, and ultimately become the direction of future development of mineral resource-based enterprises.     Among them, as mining resources mining, beneficiation process of industrial solid waste tailings, is a mining enterprises the most headache a difficult point. How to turn waste into treasure? Get rid of industrial solid waste "useless" title, to achieve the recovery of sorting and reuse, sorting is particularly important.     For the tailings generated by different beneficiation methods, the use of targeted ways to deal with, can effectively improve the utilization of ore resources, such as magnetic separation, re-election of the particles after the tailings, such as metal ore can be pre-enriched by way of sorting higher than the economic grade; for non-metallic ore for the composition of the minerals, through the way of sorting, according to the type of ore sorting, and finally achieve the purpose of use. In this regard, sorting has become an essential part of the process.       Today, we will mainly talk about a representative kind of tailings - tungsten tailings. Due to the low grade of tungsten ore, mostly around 0.1%~0.7%, a large amount of tailings are generated during the beneficiation process, accounting for up to 90% of the original ore, most of which are not effectively utilized and are mainly stored in tailing ponds or mine backfills, which not only waste resources, but also occupy land, pollute the environment and endanger health. The annual output of tungsten tailings in China alone is about 400,000t. Improving the overall utilization of tungsten tailings is conducive to promoting the construction of tailings-free mines, which not only improves the added value of tungsten ore resources but also improves the mine environment, and is the future development direction of comprehensive utilization of tungsten tailings.     Tungsten Tailings       The main components in tungsten tailings are oxides of silicon and aluminum, and contain calcium, which are more similar to traditional construction materials and can be used as construction aggregates. However, there are some quartz and flint with alkali activity inside, and there is a risk of alkali aggregate reaction. After sorting and processing, throwing out the quartz and flint in it, it can be used as high standard construction aggregate for large projects, which greatly enhances the utilization and value of tungsten tailings. At the same time, there is also a part of the aggregate, due to the influence of the site water, there will be organic matter, mud lumps, silt and other substances, if made into aggregate, will also affect the overall quality, also need to be sorted, the organic matter and high mud content of the ore thrown out, to reduce the impact of such impurities on the quality of the aggregate, to improve the overall quality and value of the aggregate.         Promoting the recycling of tailings resources and reducing the amount of tailings is a must to promote the development of green mines. It is also a historic development opportunity for mining enterprises, through the utilization of tailings, the utilization rate of mining resources can be effectively improved.         For a long time, Mingde Photoelectric has been around the ore sorting technology research and development, to improve the utilization of ore resources, has been working hard to move forward. In order to expand the scope of application of the equipment, breakthrough photoelectric separation limitations, greatly enhance the scope of ore sorting, for the progress of ore sorting technology, to provide strong support, while the research and development of equipment to fully take into account the complexity of the mining environment, the overall stability of the sorting, yield have a great guarantee. For the green mine,  Mingde provide the wisdom.

China Mining News: The establishment of a long-term, stable, efficient and diversified resource security system should be accelerated   The government work report of this year's national two sessions proposed to adhere to the general keynote of seeking progress in a stable manner, promote the overall improvement of economic operation, and achieve effective improvement of quality and reasonable growth of quantity. At present, China's metal mineral consumption accounts for more than 40% of the total global consumption, of which, iron ore consumption accounts for 62%. The huge consumption makes China's metal minerals import quantity is increasing day by day, the degree of foreign dependence is rising, the import price is climbing. How to effectively maintain the balance of demand and supply over a period of time is still the focus of great concern in the industry. Recently, Fan Tiejun, Deputy Secretary General of China Iron and Steel Industry Association and President of Metallurgical Industry Planning and Research Institute, gave an in-depth interpretation of the current supply and demand situation and development trend of iron ore industry and made relevant suggestions. I. Global iron ore supply will exceed demand for a period of time Fan Tiejun said in his analysis of the current supply and demand situation and development trend of the global iron ore industry, global steel consumption in 2022 will be 1.795 billion tons, down 2% year-on-year. Based on the analysis of global and regional economic development and steel demand, it is predicted that in the second half of the 14th Five-Year Plan, major steel consuming regions will achieve economic growth, leading to a slight growth trend in global steel consumption. In this context, from the demand side, the global iron ore consumption in 2022 is estimated at 2.258 billion tons, down 3% year-on-year, according to pig iron production. It is expected that the future of China's pig iron production will continue in the reduction adjustment area for a longer period of time, iron ore demand and pig iron production will remain synchronized; other countries around the world new iron ore demand basically just to make up for China's share of decline, the global demand for iron ore overall show a stable trend, the recent decline is still possible. From the supply side, global iron ore production will be about 2.4 billion tons in 2022. In recent years, the four major mines still have some iron ore capacity released to increase capacity or to supplement the capacity to be closed, and along with the production of some iron ore mines in Africa, the global iron ore supply shows an overall growth trend. Looking to the medium to long term, as global iron ore demand decreases and high cost and low quality mines gradually exit, future iron ore supply will gradually decrease. Overall, the global iron ore supply exceeds demand will exist for a long time in a certain period of time. Second, the demand for high-grade iron ore in China will gradually increase The central economic work conference in December 2022 clearly put forward, "strengthen the important energy, mineral resources domestic exploration and development and increase storage on production, accelerate the planning and construction of a new energy system, and enhance the national strategic material reserve security capacity. Recently, a number of national policies have pointed out the direction for a new round of mineral exploration. From the domestic iron ore market, in 2022, the lack of demand-side led to a 3% year-on-year decline in China's steel consumption. But China's long-term economic fundamentals have not changed, the relevant experts expect that the "14th Five-Year" period, China's steel demand will fluctuate downward trend, but still remain at a high level. In this regard, Fan Tiejun said, under the guidance of industrial policy, China's steel industry to production capacity double control as the basis, to ultra-low emission transformation as a grip, to energy consumption double control as the driver, to intelligent manufacturing initiatives, low-carbon transformation as the guide, and unswervingly take the road of green low-carbon quality development, to promote the transformation of development and structural adjustment. Process structure adjustment, "double carbon" target, with China's steel industry to the peak area in the middle and late development, scrap resources, electricity and other support conditions gradually improved, to a certain extent will accelerate the steel process structure adjustment, accelerate the proportion of electric furnace steel to enhance, but the adjustment cycle may be longer. Material structure adjustment, environmental protection policy, industrial policy, blast furnace large-scale and other factors, will help push China's blast furnace furnace structure to "pellet increase, sintering down" direction optimization adjustment; with low-carbon smelting process and technological innovation, blast furnace process with traditional iron ore raw material demand will gradually reduce, the demand for high-grade ore resources will also gradually Increase. For the current situation and trend of China's iron ore demand, Fan Tiejun has analyzed and predicted from 4 dimensions. In terms of domestic ore, in recent years, China's iron ore production has been falling before rising, and the national iron ore production in 2022 will be 968 million tons of raw ore, or about 300 million tons of finished ore. The future of domestic mineral production will be to stabilize growth and adjust the structure, maintaining a certain level of supply capacity and resource security. Imported ore, in recent years, China's iron ore imports are fluctuating upward trend, reaching 1.107 billion tons in 2022; China's external dependence on iron ore is rising and then falling trend, accounting for about 79.8% of the total consumption of iron ore in 2022, although imports have declined, but still at a high level. As China's scrap steel utilization rises, the overall demand for iron ore is downward, coupled with the successful implementation of the "Keystone Plan" for domestic ore production, the amount of imported ore will drop significantly. In terms of equity mining, China's overseas equity mining capacity will be about 62.9 million tons in 2022. Considering the current progress of major overseas iron ore projects, with the implementation of the "Keystone Plan", the amount of China's overseas equity ore is expected to further increase during the "Tenth Five-Year Plan". In terms of iron ore prices, iron ore prices will rise and then fall in 2022, with huge oscillations. Recent iron ore prices by the "strong expectations" and the market speculation has climbed sharply, but the current end demand is expected to continue to be in doubt, the high price support is not enough. In the long run, "strong expectations" will eventually return to rational reality, iron ore prices will fall back to a reasonable range. Third, the overall supply of steel scrap resources is on the rise Steel scrap as recycled steel raw materials, is an important source of iron, is a green raw material for the iron and steel industry, iron ore has a certain alternative role. The use of scrap steel and the use of iron ore as a source of iron, the structure of iron resources, energy structure and process structure, on resources, energy consumption and all types of emissions, including carbon dioxide emissions will have a significant impact. Relevant data show that by 2025, China's steel accumulation will reach 12 billion tons and the annual output of scrap steel resources will reach 270-300 million tons; by 2030, China's steel accumulation will reach 13.2 billion tons and the annual output of scrap steel resources will reach 320-350 million tons. In March this year, Vice Minister of Industry and Information Technology Xin Guobin said when talking about the industrial carbon peak and green transformation, this year will implement the comprehensive utilization of resources to improve quality and efficiency action, and strive to achieve the utilization of scrap steel to 265 million tons in 2023. China Scrap Iron and Steel Application Association analysis that, with the recent start of the infrastructure market, steel into the end demand gradually stronger, steel enterprises production enthusiasm significantly increased, profitability has also improved, to a certain extent, to support the scrap steel prices rise. But the current thin profits of steel enterprises, steel prices continue to move upward momentum is insufficient, steel enterprises scrap arrivals at a higher level, and merchants have intentions to step up shipments, or will inhibit the scrap prices continue to rise. Scrap prices are expected to rise and fall in the short term, steel enterprises will be based on inventory and other circumstances to adjust the purchase price slightly. For the development trend of the scrap market, Fan Tiejun said that through the study of the relationship between the amount of scrap resources generated and steel accumulation in China, and combined with the actual supply and demand of domestic scrap in recent years, it is estimated that as China's social steel accumulation will continue to grow rapidly, the output of scrap resources will further increase, coupled with the liberalization of renewable steel raw material import policy, it is expected that the overall supply of scrap resources in China is on the rise. At the same time, he said that in recent years, China's scrap steel processing enterprises have surged, the more intense competition. At present, domestic scrap resources are limited, not enough to support the rapid development of electric furnace steel, and steel enterprises to enhance the level of application of scrap steel. To further strengthen China's scrap steel resource security capacity, it is recommended to accelerate the creation of scrap steel resources recycling industry system, improve the construction of the management system of the scrap steel industry, and guide the efficient use of scrap steel resources in the steel industry. Fourth, accelerate the establishment of long-term stable and efficient diversified resource security system Iron ore as a national strategic mineral resources, to maintain the steel industry chain supply chain security has a ballast role. Fan Tiejun said that the essential problem of China's iron ore resource security is structural imbalance. The main manifestation is that the total amount of domestic iron ore resources is large, but mainly poor ore; imports are large, accounting for a high proportion and concentrated sources; the slow progress of investment in iron ore resources abroad, the overall effectiveness is not obvious; strong financial attributes of iron ore, the lack of pricing discourse; supply and demand sides of the market position is not equal. At present, China's iron ore security capacity is still seriously inadequate. The high degree of foreign dependence on iron ore not only seriously affects the supply chain security of the industrial chain, national strategic security, but also makes the steel industry a large number of profits captured by foreign mining enterprises. He suggested that China's steel industry should accelerate the establishment of long-term, stable, efficient and diversified resource security system. To make full and efficient use of domestic and foreign resources, while stabilizing the domestic cycle, to promote the formation of international circulation, the formation of domestic and international dual cycle of raw materials security pattern. It should also strive to build a multi-dimensional and diversified resource security channel through the joint use of domestic and foreign mines, the formation of a strategic supply chain, ensuring a certain proportion of equity mines, strengthening the construction of overseas multi-resource bases, establishing a resource reserve system, strengthening the risk prevention of iron ore transportation channels, using and improving the raw material financial trade system and related derivatives, and other paths. Note: This article is from China Iron and Steel Industry Association

Introduction of mining types and methods   Mine Mining   Mining refers to the use of artificial or mechanical mining of valuable natural mineral resources. According to the different depths of deposits and the requirements of technical and economic rationality, mining is divided into two ways: open-pit mining and underground mining. The part close to the surface and shallowly buried is mined by open-pit mining, and the deep part is mined underground.   For an ore body, whether to use open pit mining or underground mining depends on the state of the ore body's deposit. If open pit mining is used, how deep should be used is reasonable, there is a depth boundary problem, the determination of the depth boundary mainly depends on the economic efficiency. In general, the realm stripping for example is less than or equal to the economic and reasonable stripping ratio, open pit mining can be used, otherwise, underground mining methods are used. (A) open-pit mining   Open-pit mining is a mining method that uses mining equipment to strip rocks and extract useful minerals in an open-air condition, in an open-air or depressed open-air manner on a hillside, stage by stage. Compared with underground mining, open pit mining has many advantages, such as fast construction, high labor productivity, low cost, good labor conditions, safe working, high ore recovery rate, low depletion loss, etc. Especially with the development of large and efficient open pit mining and transportation equipment, open pit mining will be more widely used. At present, most of the ferrous metallurgy mines in China adopt open-pit mining.   The whole process of constructing an open pit mine generally includes: the construction of surface facilities in the mine area; the dewatering and drainage prevention of the deposit; the basic construction of the open pit and a series of preparatory work for putting into production.   Open pit infrastructure is mainly to dig the entry ditch, exit ditch and open section ditch, lay the transportation line, build the drainage field, strip the rock and build the drainage and power supply facilities.   The entry and exit trenches are to establish inclined transport roads from the ground to the working levels and between the working levels. Open section trenches are the horizontal trenches dug at each level to open up the mining workings, which are the initial workings of the opening phase.   Trenching, stripping and mining are three important parts of the open pit production process. The speed of decline of open pit mine and the length of preparation time for new levels are mainly determined by the trenching speed. In order to ensure the continuous and normal production of the open pit mine, a certain relationship must be maintained between trenching, stripping and mining in terms of space and time. Follow the principle of "mining and stripping, stripping first" to organize production.   Open pit production process, whether stripping or recovery of ore, the process generally go through perforation, blasting, loading and transportation. At present, the equipment used in China's ferrous metallurgy mines, perforation is mainly tooth wheel drill and perforation drill, impact drill has been eliminated. Most of the loading equipment use 3 ~ 4.6 m 3 electric shovel, 6 m 3 above the electric shovel also began to use. Most of the transport equipment use more than 20 tons of heavy vehicles and 80-150 tons of motor vehicles, 100 tons of electric wheel vehicles are also used in some mega mines.     (ii) Underground mining   When the deposit is buried very deep below the surface and the stripping factor is too high by open-pit mining, underground mining is used when it is considered reasonable to use underground mining after technical and economic comparison.   As the ore body is buried deep, in order to extract the ore, it is necessary to excavate tunnels leading from the surface to the ore body, such as vertical shafts, inclined shafts, ramp roads, flat tunnels, etc. The focus of underground mining capital construction is to dig these shafts and lane works.   Underground mining mainly includes three steps: exploration, mining and cutting (quarrying and cutting work) and recovery. Open up is in order to reach the ore body from the surface and open up the shaft, inclined shaft, ramp road, flat road and other shaft excavation project. Quarrying is the preparation work for ore recovery on the basis of pioneering works, including mining preparation lanes such as flat lanes, cross lanes and skylines in the digging stage. Cutting is the shaft work that must be completed before the recovery operation according to the mining method on the basis of the development and quasi-engineering, such as cutting the patio, cutting the flat lane, pulling the bottom lane, cutting the riffle, releasing the ore hopper, rock drilling chamber, etc. The backhoe is a mining operation in the quarry that includes rock cutting and crumbling of ore, transporting and moving ore and supporting the quarry. These three steps are carried out in sequence, and after the mine is in operation, the various shafts and tunnels continue to be excavated in order to maintain normal production. Such as the extension of the development of the roadway, the excavation of various prospecting   mining, back mining roadway, etc.. In time, we must follow the production law of "development ahead of mining, mining ahead of recovery, to ensure that the amount of production at all levels of preparation to reach a reasonable retention period". This is through the long-term production practice summed up more in line with the scientific laws of mine production practice.   When mining underground deposits, the upper stage is generally mined first, followed by the lower stage. In the stage, the ore block is divided into blocks along the direction of the deposit (the height of the block is generally 40-60 meters, while in foreign countries it is generally 60-120 meters, and can even reach 200 meters), and the block is generally used as the basic unit or the block is subdivided into ore houses and pillars for recovery.   There are many underground mining methods, mainly divided into the following three categories:   (1) Natural support mining method. The mining area is divided into ore houses and ore pillars. When mining back to the room, the resulting void area is supported by the pillar, therefore, the basic condition for using this type of mining method is that both the ore and the surrounding rock need to be stable.   (2) Artificial support mining method. In the mining area, as the recovery workings advance, artificial support methods are used to maintain the mining void and form the workings.   (3) Crumbling mining method. It is a method to control the management of ground pressure by filling the mining area with the crumbling surrounding rock as the ore falls. As the upper and lower plate rock crumbling will cause the collapse of the surface, so the surface allows crumbling is a necessary prerequisite for the use of this type of mining method.   Underground mining, whether it is pioneering, quantification or recovery, generally have to go through rock drilling, blasting, ventilation, loading, support and transportation and lifting processes.   At present, China's key underground mining equipment, rock drilling is mainly used rock drills, rock drills and deep hole drilling rigs used in the quarry. Loading is mainly used loaders, scrapers, electric rakes, etc.. Flat tunnel transportation and lifting generally use motor trucks to haul columns of ore cars to shafts, inclined shafts, hoisting shafts and sites, and then use cages to lift the ore cars to the ground, and large underground mines are unloading the ore cars into bins and then loading them into skips to lift them to the ground.

Raw ore, concentrate, tailings|  unknown ore testing and identification   The ore just extracted from the mine is the raw ore, which is crushed and then goes into the first flotation process, i.e. rougher separation, resulting in rougher concentrate and rougher tailings.   The coarse tailing is then flotation, i.e., sweeping, which produces sweeping concentrate and sweeping tailing.   The coarse concentrate is then flotation, i.e. selected, to produce a concentrate and a concentrate tailing.   After the initial separation of the ore, such as flotation, re-election or magnetic separation, the water content of some of the veins or peridotite selected, and the product higher than the original grade, called coarse concentrate, generally not up to the requirements of the quality of the concentrate, this process is called coarse separation services. The coarse concentrate will be re-elected to get qualified concentrate, this process is called selection operation. Sometimes it is necessary to select the coarse concentrate several times to get qualified concentrate, and the operation is called primary selection, secondary selection and tertiary selection in order .......      Generally, the rougher tailings cannot be discarded as the final tailings, and often need to enter the next step of the operation, which is called sweeping. In order to improve the metal recovery, it is sometimes necessary to go through several sweeping operations to arrive at the final tailings.   After the ore has been processed by the separation operation, most of the veins and impurities are removed, so that the useful minerals are enriched product is called concentrate. The concentrate is the final product of the beneficiation plant, sometimes called the final concentrate, and is generally used as the raw material for smelting. The final concentrate has to make its main components and impurity content meet the national standards in order to be called qualified concentrate.   For concentrate, medium ore and tailings, what is outside the concentrate and tailings after each flotation is called medium ore.    After a raw ore is treated by the separation operation, its main components have been enriched in the concentrate, and in some cases, after comprehensive treatment, the minor components of the ore or other associated metals are also recovered. So the remaining part of the product contains very low components, and this part of the product is called tailings, or final tailings. It should be noted that the tailings still contain useful components that are difficult to extract due to the current level of technology, but have the potential to be reused as raw materials in the future. Therefore, the tailings are generally stockpiled and preserved in tailings storage.    Analysis of unknown ore minerals Ore testing types 1, geological and chemical exploration: census samples, slot (pit) samples, drill samples, dispersion flow samples, secondary halo samples, primary halo samples, etc. 2、Ore minerals: copper, lead and zinc ore, gold ore, molybdenum ore, tungsten ore, titanium ore, tin ore, antimony ore, bismuth ore, mercury ore, cobalt ore, nickel ore, chromium ore, iron ore, manganese ore, phosphate ore, fluorite, bauxite, sulphide iron ore and rock analysis, etc. 3, concentrate products: copper concentrate, lead concentrate, zinc concentrate, gold concentrate, tin concentrate, antimony concentrate, tungsten concentrate, molybdenum concentrate, etc. 4、Mineral products: various concentrates (beneficial and harmful impurity components), imported raw materials and smelting slag materials, etc. ‍ Ore is rich, various metal types and content varies, fine detection technology reference national ore detection standards, to provide you with all kinds of ores, copper, lead, zinc, nickel, tin, cobalt, aluminum and other non-ferrous metal content analysis, detection, identification services, as well as gold, silver, palladium, platinum and other precious metal content of the assay analysis services, each element detection methods are in strict accordance with the relevant national testing standards, and can do accurate and fast.   This article is reprinted from: Intercontinental Mining