▲ ▲ Kaolinite mineral sorting solutions

Sorting for kaolinite raw materials

I. Key prerequisites

1. Mineralogical differences: Kaolinite and associated non-kaolinite materials such as quartz and sandstone do not differ significantly in chemical composition and density (density is similar). Using density to distinguish kaolinite from non-kaolinite sandstone or quartz-rich gangue can allow for the selection of kaolinite, but accuracy often falls short of requirements
2. Identification strategy: Utilize DRX X-ray imaging to separate materials by high, medium, and low density (removing high/low density materials while retaining medium-density kaolinite). If DRX sortor accuracy fails to meet requirements, use DRXV X-ray imaging combined with industrial camera imaging to combine material density and color texture for differentiation (removing high/low density materials and non-kaolinite in the medium-density range)
3. Sample testing: Since the gangue from different coal mines is different, it is necessary to conduct specific sample testing. After data collection and modeling sorting tests, the decision on whether to proceed with the project can be made based on the test results

II. Using X-ray image sorting logic

1. Data acquisition • X-ray Imaging: Detects the X-ray attenuation characteristics of the material. Used to distinguish clearly low-density materials (coal, interbedded coal, and black rock) from clearly high-density materials (sandstone and high-density gangue)
2. Computational identification
• High-density identification: Ignores sandstone, and high-density gangue
• Low-density identification: Ignores coal, interbedded coal, and black rock
• Medium-density Identification: Marks kaolinite
3. Separation execution
• High-pressure gas is used to eject the marked kaolinite for sorting.

III. Using X-ray + industrial camera image sorting logic

1. Data acquisition • X-ray imaging: Detects the X-ray attenuation characteristics of the material. Used to distinguish clearly low-density materials (coal, interbedded coal, black rock) from clearly high-density materials (sandstone, high-density gangue). • Industrial camera imaging: Identifies the surface spectral characteristics of kaolinite, assisting in distinguishing kaolinite from non-kaolinite. 2. Computational identification • High-density identification using X-ray imaging: Ignores sandstone, and high-density gangue. • Low-density identification using X-ray imaging: Ignores coal, black rock, and interbedded coal. • Further identification of medium-density materials using industrial camera imaging: Ignores non-kaolinite and marks kaolinite. 3. Separation execution • High-pressure gas ejects the marked kaolinite for sorting.

IV. Project preparation

1. Sample preparation: 200-500 kg of kaolinite-containing coal gangue sample
2. Data Acquisition: First, classify the sample to separate kaolinite and non-kaolinite samples, then acquire X-ray images and industrial camera images
3. Sorting modeling: Develop a sorting model using X-ray images and a sorting model using X-ray image + industrial camera image fusion
4. Sorting tests: Conduct multiple sorting tests and refine the model and parameters based on the sorting results. Finally, generate sorting results samples, including X-ray image sorting and X-ray image + industrial camera image fusion sorting (including kaolinite and non-kaolinite samples, respectively)
5. Sorting verification: Test samples from various sorting results to determine the chemical composition of the sorted kaolinite and non-kaolinite. Use the test results to verify the sorting performance. Based on the test results, determine whether the sorting model parameters can be improved. If so, conduct further sorting tests and retest. Only after the final test results confirm a favorable sorting performance will the project be considered for advancement
6. Process flow determination: Based on the sorting performance, select a sorting process flow: DRX X-ray imaging sorting, DRXV X-ray + camera imaging sorting

V. Sorting process using DRX equipment

Raw materials → (particles larger than 350mm require a pre-crusher + vibrating screen) → particle size 20-350mm → X-ray image recognition → high-pressure gas injection to select kaolinite

VI. Sorting process using DRXV equipment

Raw materials (particles larger than 350mm require a pre-crusher + vibrating screen, and cleaning and dehumidification are required) → particle size 20-350mm → X-ray imaging + industrial camera image recognition → high-pressure gas injection to select kaolinite

VII. Conclusion

DRX sorting equipment uses X-ray imaging to sort kaolinite. Sorting accuracy varies among coal mines, with some mines achieving high kaolinite sorting accuracy from gangue, while others experience lower accuracy. DRXV utilizes a fusion of X-ray imaging and industrial camera image recognition to select high-purity kaolinite and eliminate non-kaolinite. However, industrial camera imaging requires a relatively clean surface for the raw materials, potentially requiring additional cleaning and dehumidification equipment.
Because gangue from different coal mines varies, specific sample data collection and modeling and sorting tests are necessary. Based on the test results, a cost-benefit analysis is conducted before a decision is made on whether to proceed with the project. If it is necessary to recover the coal and gangue contained in the raw materials, it is recommended to first remove the high-density sandstone and gangue to select kaolinite, black stone, coal, and gangue, then select the kaolinite, and finally select the coal and gangue from the remaining materials.

Sorting for calcined kaolin finished products

I. Solution background

Because coal-bearing kaolinite raw materials are impure, calcined kaolinite can contain mixed rocks of high and low density, gangue (white on the outside but with mixed colors inside), and low-quality kaolinite with low whiteness. To obtain kaolinite products with relatively high whiteness or products graded by whiteness, the calcined kaolinite requires further sorting. Manual sorting is inefficient, costly, and poses safety risks, especially for materials with a white surface but non-white interiors. The DRXV intelligent mineral sorting equipment, which utilizes X-rays and industrial camera image recognition, can efficiently, cost-effectively, safely, and reliably sort kaolinite products with high whiteness or graded by whiteness.

II. Using X-ray + Industrial camera image sorting logic

1. Data acquisition
• X-ray Imaging: Using X-ray penetration imaging of the material, distinguish high-density debris and gangue with a white exterior and mixed colors inside
• Industrial camera imaging: Using kaolinite camera imaging, distinguish non-white raw materials
2. Computational identification
• Using X-ray Imaging to Identify High and Low Density: Ignore high- and low-density debris, especially non-kaolinite with a white exterior and mixed colors inside
• Using industrial camera image color identification: Ignore non-white, low-whiteness kaolinite and mark high-whiteness kaolinite; or classify by whiteness to mark kaolinite with a desired whiteness level
3. Separation execution
• Using high-pressure gas to eject: Marked high-whiteness kaolinite, or marked kaolinite with a desired whiteness grade

III. Test using DRXV equipment

1. Sample preparation: 50-200 kg of calcined kaolinite
2. Data collection: First, classify the sample to separate white kaolinite and non-white mixed stone samples. Samples with white exterior and mixed stone inside are identified by breaking the particles apart. Then, obtain X-ray images and industrial camera images
2. Sorting modeling: Develop a sorting model using X-ray imaging and a sorting model using X-ray imaging + industrial camera image fusion
3. Sorting tests: Conduct multiple sorting tests and refine the model and parameters based on the sorting results. Finally, generate sorting results samples, including X-ray image sorting and X-ray image + industrial camera image fusion sorting (including white kaolinite and non-white mixed stone, respectively)
4. Sorting verification: All sorted samples are broken open and inspected again to verify the sorting results. This will determine whether the sorting model parameters can be improved. If so, further sorting tests and verification are conducted to finalize the model parameters
Also, if sorting by whiteness grade is required, samples are selected at different whiteness grades, images are acquired, and sorting parameters are proposed. Sorting tests are then conducted to finalize the model parameters for sorting by whiteness grade.

IV. Sorting process using DRXV equipment

1. High whiteness kaolinite sorting
Calcination (particles over 350mm require a pre-crusher and vibrating screen) → Particle size 20-350mm → X-ray imaging + industrial camera image recognition → High-pressure gas injection to select high-whiteness kaolinite
2. Sorting by whiteness grade
White kaolinite → X-ray imaging + industrial camera image recognition → High-pressure gas injection to select kaolinite of the required whiteness grade

V. Test case reference

VI. Conclusion

DRXV intelligent mineral sorting equipment uses X-ray + industrial camera image fusion recognition technology to remove the debris in the calcined kaolinite, especially the gangue that is white on the outside and mixed on the inside, and sort out the high-whiteness kaolinite. It can also be graded according to the whiteness. If necessary, it can use DRXV intelligent mineral sorting equipment to sort for coal kaolinite raw materials before calcination, remove high-density and low-density miscellaneous stones and non-kaolinite materials, improve the purity of kaolinite raw materials, reduce the calcination task and the sorting workload after calcination, so as to reduce costs and improve production efficiency and benefits.