Ore sorting efficiency directly affects resource utilization and economic benefits, and the sorting effect is affected by the comprehensive influence of ore characteristics, equipment parameters and technological innovation. By combining the ore particle size, impurity content, sorting method (dry sorting/wet sorting) and intelligent technology application, we explore how to optimize the sorting efficiency and use technology to analyze the innovation direction of modern sorting equipment.

1. Ore particle size: particle size adaptation and sorting method selection

Ore particle size is the core parameter that determines the sorting process. It is mainly precisely controlled by combining the sorting method (dry sorting or wet sorting) and equipment type:

Difference in particle size between dry sorting and wet sorting

Dry sorting: usually suitable for coarse-grained ores, such as magnetic separators that can handle ores up to 400-500mm (need to be pre-crushed to about 100mm), while photoelectric sorting equipment has more flexible requirements for particle size. For example, MINGDER Optoelectronics' intelligent sorting machine can adapt to a wide range of 5-150mm.

Wet sorting: suitable for fine-grained ores, such as the centrifugal concentrator with a suitable particle size of 0.1~2mm, and the flotation process is mostly used for micron-sized particles.

Differentiated requirements for particle size for use

Sand and gravel for construction need to be graded into 5-31.5mm crushed stone or sand less than 4.75mm, while high-purity minerals (such as phosphate rock to achieve better dissociation) need to be refined by photoelectric sorting to achieve 5-50mm.

Optimization direction: achieve particle size homogenization through pre-crushing and screening, and select an appropriate sorting method based on the characteristics of the target mineral. For example, dry sorting is suitable for rough sorting of low-water-content ores, while wet sorting is used for high-precision fine-grain sorting.

2. Ore impurity content: intelligent identification and pretreatment technology

Impurity type and content directly affect sorting efficiency and concentrate grade:

Impurities with similar physical properties (such as quartz and feldspar) are difficult to separate by traditional sorting methods, and need to rely on photoelectric or sorting technology to identify differences in optical properties.

Ores with high impurity content are prone to clogging equipment. For example, clay impurities will increase the medium pollution of the wet sorting system, and the proportion of impurities needs to be reduced by washing or pre-screening.

MINGDER Optoelectronics' Solution

Using multi-dimensional recognition technology and deep learning algorithms, it can accurately identify the surface texture, color and gloss of the ore, and achieve efficient removal of impurities. The impurity removal rate can reach more than 98%. For example: In phosphate ore sorting, MINGDER Optoelectronics' artificial intelligence sorting technology can increase the purification efficiency of low-grade ore by 26%, while avoiding chemical agent pollution.

3. Collaborative optimization of equipment parameters and structural design

The sorting efficiency depends not only on the characteristics of the ore, but also on the equipment operating parameters and structural design:

Balance between belt speed and processing capacity

If the belt speed is too fast, it will shorten the residence time of the ore in the sorting area and reduce the recognition accuracy; if it is too slow, it will limit the production capacity. At the same time, it is also necessary to adjust the machine model and belt speed in a targeted manner based on the particle size of the selected ore. A fast crawler or slow crawler design can be used to balance the processing volume and sorting accuracy.

Innovation in structural design

Optimization of sorting area: The sealed structure of the photoelectric sorting box prevents dust from interfering with the sensor.

Intelligent control: The integrated sensor monitors the ore flow and composition in real time and automatically adjusts the sorting parameters (such as light source intensity and air flow speed).

Technology trend: The modular design enables the equipment to flexibly adapt to different types of ores. For example, MINGDER Optoelectronics' artificial intelligence sorting machine can quickly switch to the processing of the same type of ore with large ore source differences through training sorting models. It can also process different ores, such as phosphate, talc, fluorite, magnesite, etc.

IV. Technical comparison and application scenarios of dry sorting and wet sorting

Sorting method

Dry sorting: No water source is required, low energy consumption, suitable for large particle size roughing, large output;

Wet sorting: high sorting accuracy, environmentally friendly, high water resource consumption, and high subsequent dehydration cost.

Application scenario: Dry sorting is mostly used in arid areas or roughing stages, while wet sorting is suitable for high-precision sorting and areas with abundant water resources. At the same time, it is also necessary to dynamically select dry or wet sorting methods based on the situation of the mined ore and subsequent uses.