1. Which links in the mineral processing process are likely to affect efficiency? In the mineral processing technology, multiple links may affect the mineral processing efficiency, and the following links are more likely to have a significant impact on the mineral processing efficiency: (1) Pre-election preparation stage: Crushing and Screening: Ore crushing and screening are key steps before mineral processing, which directly affect the efficiency and effect of subsequent mineral processing. In the crushing operation, if the crusher is improperly selected or operated, it may lead to insufficient or excessive crushing of the ore, affecting the efficiency of subsequent grinding and mineral processing. Screening is used to classify the crushed ore according to particle size to provide suitable raw materials for the processing. Grinding and Classification: Grinding is the continuation of the ore crushing process, and its purpose is to separate various useful mineral particles in the ore into monomers for selection. The selection of grinding mills and the control of the grinding process are crucial to the efficiency of mineral processing. The classification operation affects the classification particle size and processing capacity by adjusting parameters such as the size of the classification area, the height of the overflow weir and the speed of the spiral, thereby affecting the efficiency of mineral processing. Selection stage: The properties of the ore, the selection of the beneficiation equipment and the selection of the beneficiation method will affect the efficiency of the beneficiation stage. For example, the particle size of the mineral has an important influence on the flotation efficiency. Too fine a particle size will deteriorate the flotation effect. The selection of the flotation machine speed will also affect the stirring intensity of the slurry and the flotation effect. Dehydration stage after selection: The concentrate obtained by wet beneficiation usually contains a lot of water. The efficiency of the dehydration stage directly affects the quality and output of the concentrate. The dehydration stage includes processes such as concentration, filtration and drying. The effects of these processes are affected by factors such as equipment performance, operation level and the properties of the original ore. Slurry concentration: Appropriate pulp concentration has an important impact on flotation efficiency. Within a certain range, increasing pulp concentration is conducive to the collision and contact between minerals and reagents, thereby improving flotation efficiency. However, excessive pulp concentration will increase reagent consumption, deteriorate aeration effect, and reduce flotation efficiency. Operation and management: The skill level and management level of operators also have an important impact on mineral processing efficiency. Modern and digital management methods can optimize the mineral processing process and improve production efficiency. At the same time, strengthening the management and awareness of mining companies and avoiding management and awareness deviations are also important measures to improve mineral processing efficiency. To sum up, many links in the mineral processing process may affect the efficiency, but factors such as the preparation stage before mineral processing, the separation stage, the dehydration stage after mineral processing, as well as slurry concentration and operation management have the most significant impact on mineral processing efficiency. By optimizing these links and factors, the mineral processing efficiency can be significantly improved, production costs can be reduced, and the sustainable development of the mine can be achieved. 2. In order to optimize the links that affect efficiency in the mineral processing process, we can consider and implement them from the following aspects: (1) Grinding and grading operations: Optimize grinding process parameters: According to the characteristics of the ore, study the grinding index and formulate appropriate grinding process parameters. For the ore dressing plant with "over-grinding" phenomenon, selective grinding technology can be considered. Use efficient grading equipment: Although spiral classifiers are commonly used, their grading efficiency is generally only 20% to 40%. Consider introducing efficient grading equipment such as hydrocyclones or high-frequency vibrating fine screens to improve grading efficiency. However, attention should be paid to the stability of hydrocyclones. (2) Selection of work: Select or improve mineral processing equipment: In flotation operations, the selection of flotation machines is crucial. According to the characteristics of the ore and the flotation process, select or design a suitable flotation machine. At the same time, pay attention to the development of flotation reagents and processes, and adopt the latest flotation technology and reagents. Optimize flotation conditions: According to the properties of the ore, adjust the parameters such as pulp concentration, stirring intensity, and aeration volume during the flotation process to obtain the best flotation effect. (3) Dehydration operation: Introduce advanced dehydration equipment: such as disc vacuum filter, which not only has large processing capacity and good dehydration effect, but also has low energy consumption. Optimize the dehydration process: By adjusting various links in the dehydration process, such as pre-dehydration, filter pressing, etc., the dehydration efficiency can be improved and the moisture content in the concentrate can be reduced. (4) Slurry concentration control: Real-time monitoring and adjustment: By real-time monitoring of pulp concentration, timely adjust the amount of water added during grinding and flotation to ensure that the pulp concentration is within the optimal range. Optimize the use of reagents: During the flotation process, adjust the amount and type of reagents according to the pulp concentration to obtain the best flotation effect. (5) Operation and management: Improve operator skills: Through training and skill improvement, ensure that operators have the necessary mineral processing knowledge and skills and can operate mineral processing equipment proficiently. Introduce a modern management system: Use a digital and automated management system to monitor all aspects of the mineral processing process in real time to improve production efficiency and product quality. Strictly follow the principles of comprehensiveness and pertinence to carry out equipment transformation to ensure that the transformation work can truly improve economic benefits and production efficiency. (6) Strengthen the management of mining companies: Correct the deviations in the management and cognition of mining companies, ensure that managers have geological knowledge and mineral processing experience, and avoid non-geological personnel from conducting mineral processing according to the management model of other industries. Establish a reasonable assessment mechanism, avoid taking economic benefits as the only criterion, and ensure that the basic status of geological exploration work is valued. Through the implementation of the above measures, the links that affect efficiency in the mineral processing process can be optimized, the mineral processing efficiency can be improved, the production cost can be reduced, and the sustainable development of the mine can be achieved. (7) Continuous research and innovation: Encourage and support scientific researchers to conduct research and innovation in mineral processing technology, and continuously develop new mineral processing methods and processes. Strengthen exchanges and cooperation with other countries and regions, and introduce advanced mineral processing technology and equipment. At the same time, in view of the above-mentioned problem of low mineral processing efficiency, the introduction of MINGDE mineral processing equipment can greatly improve the mineral processing efficiency. Its value is mainly reflected in the following aspects: High-precision identification and sorting: MINGDE optoelectronic beneficiation equipment, such as the MINGDE AI sorter, can accurately identify multiple characteristics of non-metallic ores, including color, texture, shape, gloss, etc. This high-precision recognition technology enables ores to be accurately classified and screened, thereby improving the accuracy and efficiency of beneficiation. High efficiency sorting: The equipment has high-speed processing capabilities and can quickly complete the sorting of a large number of non-metallic ores. For example, the heavy-duty visible light artificial intelligence sorting machine product launched by MINGDE Optoelectronic has a sorting and processing capacity of up to 100 tons/hour, greatly improving production efficiency. Energy saving: MINGDE Optoelectronic mineral processing equipment achieves more crushing and less grinding by pre-sorting the granular ore, effectively reducing energy consumption. This optimization can not only improve production efficiency, but also reduce mineral processing costs and improve the economic and ecological benefits of the mineral processing plant. Environmental friendly: Compared with traditional physical and chemical beneficiation, the only energy consumption of photoelectric beneficiation is electricity consumption, and it has zero pollution to the environment. This green beneficiation method meets the current requirements of environmental protection and contributes to the sustainable development of mining production. High level of intelligence: With the development of computer technology and artificial intelligence technology, the intelligence level of Mingde Optoelectronics' mineral processing equipment has been continuously improved. This intelligent equipment can better adapt to the sorting needs of different types and complex ore structures, and improve the flexibility and adaptability of mineral processing. In summary, Mingde Optoelectronics' mineral processing equipment provides strong support for improving mineral processing efficiency through its advantages in high-precision identification, high-efficiency sorting, energy saving and consumption reduction, green environmental protection and high intelligence level. These advantages not only help to improve the efficiency and benefits of mining production, but also help to promote the green, intelligent and sustainable development of mining production.    

The application scenarios of AI technology in mining resource sorting mainly include the following aspects: 1. Exploration of new minerals: AI technology has begun to be applied to the exploration of new minerals, such as using machine learning algorithms to analyze geological data and predict the best drilling locations. This technology has been successfully applied to gold exploration and is being used in the exploration of other minerals. 2. Unmanned mining vehicles: The application of AI technology in large mining companies is mainly to improve operational efficiency. Unmanned vehicles have been used in open-pit mines, and unmanned driving is achieved through automated transportation systems, which improves the efficiency and safety of mine operations. 3. Ore sorting optimization: AI technology can classify and identify ores through image recognition technology, improving sorting efficiency and accuracy. Data analysis and prediction models can predict the quality and composition of ores in advance, help adjust sorting parameters, and improve ore utilization. https://www.mdoresorting.com/mingde-ai-sorting-machine-separate-phosphorite-ore   4. Mineral association analysis: AI technology can predict the location and type of new mineral deposits through mineral association analysis. This method uses the combination of minerals formed under specific physical and chemical laws. For example, the formation of minerals is closely related to the chemical composition of the host rock and environmental conditions. 5. Mining resource exploration and mining: The application of AI technology in mining resource exploration and mining includes remote monitoring, automated mining, data analysis and decision support, intelligent safety monitoring, environmental monitoring, logistics management, data analysis, decision support, and automated control. These applications improve the efficiency, safety, and environmental protection of mining operations. 6. Mine management: AI technology can help mine managers analyze various production and operation data in a timely manner, provide visual data insights and intelligent decision-making support, and improve management efficiency. Automated and intelligent management AI technology can realize automated control of mining equipment and operating processes, improve operating efficiency and safety, and achieve more refined mine management. 7. Mine safety: AI technology can realize remote control and unmanned mine operations, improving the safety and work efficiency of operators. Advanced AI safety monitoring systems can analyze the mine operating environment in real time, promptly identify potential safety hazards, and warn operators, greatly improving mine safety. 8. Mine environmental monitoring: AI technology can monitor mine soil, water quality, air quality and other indicators in real time to detect environmental problems in a timely manner. Predictive analysis models can predict environmental change trends and provide a basis for formulating environmental protection measures. 9. Mining logistics: AI technology is revolutionizing mining logistics management. From automated loading and unloading to intelligent scheduling, unmanned transportation to real-time inventory monitoring, AI plays a key role in improving mining logistics efficiency, reducing costs, and enhancing safety. 10. Mine data analysis: AI technology can help mining companies quickly process and analyze massive amounts of production, environmental, safety and other data to uncover hidden value and patterns. Through AI technology, mining companies can better predict equipment failures, optimize production processes, improve resource utilization, and improve overall operational efficiency. 11. Mining decision support: AI technology can help mining companies make more intelligent and data-driven decisions. By analyzing massive production data, market forecasts, environmental monitoring and other information, AI systems can provide mine managers with more comprehensive decision-making suggestions and improve the operating efficiency and risk management capabilities of mines. 12. Mine automation: The application of AI technology in mine automation includes self-driving mining trucks, automated mining drilling, and intelligent ore sorting. These technologies improve production efficiency, reduce manual intervention, and improve operational safety. 13. Remote control of mines: AI technology can achieve real-time monitoring and automated control of mine sites through remote sensing, machine vision, machine learning and other technologies, greatly reducing the need for manual entry into dangerous environments. Remote control technology can also help mining companies improve the flexibility of production management and achieve effective management of distributed mines. These application scenarios demonstrate the wide application and huge potential of AI technology in mining resource sorting, indicating that mining will become more intelligent and efficient in the future.  

As the number of ore resources with low mining difficulty and good quality is decreasing, mining companies are gradually falling into trouble, especially low-grade mining companies. How to improve the economic value of mines? Reduce the overall mining and selection costs? It is an important problem facing its development, especially at the current stage, the mining and selection technology and production process improvements of industrial and mining enterprises are in a stagnant stage. The only best choice is to break the existing thinking mode. In view of the current situation of industrial and mining enterprises, there will be no major breakthroughs in mining and selection technology for the time being. Only by looking for external breakthroughs in the production process can new innovations be achieved. It is obvious that the best solution is to start with the sorting after the crushing and dissociation of the original ore. Some people will definitely ask why? In fact, it is very simple. We need to understand what ore sorting is and what is the difference between the sorting mentioned and the sorting at the current stage. The ore sorting mentioned here is to enrich the grade of the ore in advance before grinding and crushing, and to raise the pre-throwing waste tailings to reduce the amount of ore entering the subsequent process, saving a lot of costs for the subsequent process. At the same time, the pre-throwing waste tailings have not been ground and have certain economic value. Take an example of economic benefits. Let's do some economic calculations. Assuming that an industrial and mining enterprise mines 1 million tons per year, before using an ore sorter, the original production process is mining-crushing-grinding-flotation. According to the calculation of $6.3 per ton for grinding and flotation, the annual cost before using an ore sorter is about 6.3 million dollar. After using an ore sorter, each ore sorter sorts about 25 tons per hour (the smaller the particle size, the lower the hourly sorting output. In this example, the particle size of the ore particles is in the range of 1cm-4cm). The sorting cost is mainly electricity. The electricity cost per machine is $1.37 per hour, and the sorting cost per ton is about $0.137. According to 20% of the discarded tailings, there is no need for subsequent grinding and flotation, and the annual savings can reach about 1.1 million dollar. In addition, the discarded tailings can still be backfilled in the mine, or sold as other construction, road construction and other materials. The overall estimated annual output value is at least more than 1.37 million dollar. Among them, Mingde Optoelectronics' artificial intelligence ore sorter was born. Committed to the introduction, research and development, promotion and application of artificial intelligence ore sorting technology. AI Ore Sorting Machine AI Ore Sorting Machine is a device that uses the principle of photoelectric sorting, artificial intelligence means, and AI photoelectric sorting technology. After the original ore is crushed and before flotation, it can be sorted in a composite manner according to the different surface characteristics of the original ore, such as texture, color, texture, shape and other multi-dimensional characteristics, to achieve ore grade enrichment and pre-disposal of tailings. Intelligent sorting equipment. It also has the following advantages https://www.mdoresorting.com/mingde-ai-sorting-machine-separate-quartzmicafeldspar-from-pegmatite Adjustable parameters: Sorting models can be established according to different sorting requirements to meet personalized sorting requirements; Automatic sorting: No manual work is required to achieve intelligent ore sorting with high sorting efficiency; Intelligent: It can continuously learn through the learning mode to further improve the overall sorting effect; Application range: Mainly in the sorting of talc, wollastonite, potassium feldspar, fluorite, quartz, calcite, lithium ore, gold ore, iron ore, lead-zinc ore, high-crystalline silicon and other ores with visible differences; Applicable fields: new and old mines, historically abandoned low-grade ores and other industrial and mining enterprises.