Although my country's bauxite ore has the characteristics of high aluminum (Al2O355%-88%) and low iron (Fe2O31%-6%), it is restricted by the high natural mixed grade rate and uneven distribution of ferrosilicon impurities (SiO23%-25%). Traditional sorting is difficult to break through the bottleneck of low aluminum-silicon ratio (A/S) (≤5) and large quality fluctuations, making it difficult to meet the strict standards of alumina smelting (A/S≥8) and high-end refractory materials (Al2O3≥90%). Under the dual constraints of the sharp decline in high-grade mineral resources and the tightening of environmental emission limits, mining companies need to take iron reduction, silicon reduction and aluminum-silicon ratio optimization as the core technical path, and achieve accurate purification of low-grade ores (A/S≤3) through intelligent sorting and process coordination, promote resource value enhancement (Al2O3 grade increase ≥15%) and low-carbon transformation (carbon emissions per ton of ore reduced by 30%)

1. The core impact of bauxite quality on downstream applications

Quality threshold of downstream applications

Aluminum electrolysis field: aluminum grade (Al2O3 content) ≥55%: determines the efficiency of alumina extraction and energy consumption level. Iron (Fe2O3) ≤5%: Excessive iron elements lead to decreased conductivity of electrolytic aluminum and increased anode consumption. Aluminum-silicon ratio (A/S) ≥7: High aluminum-silicon ratio can reduce alkali consumption and red mud emissions.

Refractory field: Al2O3 ≥65%: ensure the high-temperature stability of refractory materials. Aluminum-silicon ratio (A/S) ≥ 4: determines the efficiency of mullite phase generation and the thermal shock resistance of the material.

Traditional sorting bottleneck: low physical sorting efficiency, high chemical cost and high pollution.

2. Demand for quality improvement of bauxite ore and advantages of AI sorting technology

The breakthrough value of AI sorting technology

Accurate identification: Through deep learning and computer vision, it captures multi-dimensional features such as ore color, texture, and gloss, distinguishes aluminum minerals (such as gibbsite) and silicate impurities (such as quartz and kaolinite), and the identification accuracy reaches 98%.

Silicon polishing and aluminum extraction: For ores with A/S>8, AI sorting can efficiently separate silicon minerals, increase the aluminum-silicon ratio to more than 10, and reduce red mud emissions by 30%-50%.

Iron reduction synergy: Simultaneously identify iron oxides (such as hematite), and remove impurities through air valve blowing, and the iron content can be reduced from 8%-12% to 3%-6%.

3. Quality improvement path of AI sorting technology integration

(I) Pre-sorting stage: AI-driven efficient impurity removal

Ore pretreatment

Intelligent classification: Using MINGDER AI intelligent sorting machine series, wet sorting of 3-8cm particle size ores, with a processing capacity of 35-50 tons/hour and a pre-discarding rate of 30%-50%.

Targeted impurity removal: Based on transfer learning technology, a sorting model is constructed under a small amount of sample training to accurately separate silicon and iron impurities and reduce the subsequent chemical leaching process load.

(II) Deep purification stage: AI and chemical/physical process collaboration

AI sorting-flotation combined process

Coarse grain tailings: After AI sorting pretreatment, the amount of flotation reagents used is reduced by 30%, and the positive flotation desiliconization efficiency is increased to more than 90%.

Tailings re-selection: AI sorting machine sorts and reuses tailings accumulated in tailings ponds or low-value ores stripped from the edge of the vein, and can recover 30-50% of high-value bauxite.

Roasting-AI sorting coupling

During the production process of electrolytic aluminum, black carbon particles mixed in the anode material (mainly from anode consumption or raw material impurities) will affect the purity of the aluminum liquid. By selecting a color sorter, such impurities can be removed efficiently and the quality of electrolytic aluminum can be improved.

4. Economic Benefit and Strategic Value Analysis

Direct Benefits

Concentrate premium: After AI sorting, the grade of Al2O3 increases from 55% to 65%-70%, the grade increases by 10-15 percentage points, and the price of refractory grade ore increases by about 30%-50%.

Cost optimization: The processing cost per ton of ore is reduced by 15%-20%, mainly from energy saving and reagent reduction.

Extension of the industrial chain

Customized concentrate supply: Jointly develop special concentrates with A/S>10 with electrolytic aluminum enterprises, and jointly build an integrated base of "mine-alumina-electrolytic aluminum". Shorten the alumina extraction process and reduce alkali consumption by 20%.

Solid waste resource utilization: sorting tailings (high silicon/high iron) for the production of ceramsite or cement admixture to achieve zero waste.

5. Implementation suggestions for mining enterprises

Technology selection strategy

Equipment configuration: Select MINGDER AI intelligent sorting machine and color sorting machine series according to the characteristics of the ore, which can take into account the processing capacity and sorting accuracy requirements.

Process integration: Construct a three-level process of "AI pre-sorting → flotation/roasting → comprehensive utilization of tailings", and increase the comprehensive utilization rate of resources to more than 95%.

Green intelligent upgrade

Data-driven optimization: Use real-time data training models in the sorting process to dynamically adjust sorting parameters to adapt to ore grade fluctuations.

Carbon neutrality path: AI sorting reduces red mud emissions and the use of chemical agents, helping to reduce carbon emissions by 15%-25%.