There are many factors that affect the effect of ore sorting, mainly including the following aspects:

1. Ore properties: The physical properties (such as hardness, density, humidity, particle size distribution) and chemical properties (such as mineral composition, chemical activity) of the ore are the key factors affecting the sorting effect. Different ores require sorting methods suitable for their characteristics.

2. Ore grade: The higher the content of valuable minerals in the ore, the better the quality of the concentrate obtained after sorting. Conversely, low-grade ore may require more complex sorting processes to reach the standard of economic utilization.

3. Sorting equipment: The performance, maintenance and operation level of the equipment directly affect the sorting effect. Efficient and stable equipment can improve sorting accuracy and processing capacity.

4. Process parameters: The setting of parameters such as feed rate, water flow rate, vibration frequency, etc. during the sorting process has a significant impact on the sorting effect. Reasonable process parameters can optimize the sorting effect.

5. Environmental conditions: Environmental factors such as temperature and humidity may also affect the sorting results, especially for minerals that are sensitive to the environment.

6. Complexity of ore: If the ore contains multiple minerals, the interaction between them may make sorting more difficult, and comprehensive sorting technology is needed.

7. Ore uniformity: Ore uniformity affects the stability of the sorting process. Inhomogeneous ore may lead to unstable sorting results.

8. Type and rate of impurities: The type and rate of impurities in the ore will also affect the sorting effect, especially those impurities that interfere with the sorting process.

9. Operator skills: The operator's experience and skills have an important impact on the sorting effect. Skilled operators can better control the sorting process.

10. Pretreatment before sorting: Pretreatment processes such as crushing and grinding have an important influence on the particle size distribution and surface properties of the ore, which in turn affects the sorting effect.

MINGDE AI intelligent sorting machine takes the lead in using artificial intelligence means such as deep convolutional neural network (CNN) to analyze and process material images in the field of visible light photoelectric sorting, and automatically extracts multi-dimensional features of materials to establish a database through CNN local connection, weight sharing, multi-convolutional kernel and other methods in the training process, and the sorting effect is far better than that of traditional sorting methods,and it has outstanding performance in ore pretreatment, low-grade ore enrichment, and complex ore sorting.