Although Chinese bauxite has the advantages of "high aluminum, low iron", its disadvantages are also very obvious, that is, "low aluminum silicon ratio and large quality fluctuations".This makes it difficult to meet the strict standards for 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 enterprises need to focus on reducing iron, silicon, and optimizing the aluminum silicon ratio as the core technological path.

一、The impact of bauxite quality on two major application fields

（一）In the field of electrolytic aluminum

       Aluminum grade (Al2O3 content) ≥ 55%: determines the efficiency and energy consumption level of alumina extraction.

       Iron (Fe2O3) ≤ 5%: Excessive iron element leads to a decrease in the conductivity of electrolytic aluminum and increases anode consumption.

      Aluminum silicon ratio (A/S) ≥ 7: High aluminum silicon ratio can reduce alkali consumption and red mud discharge.

（二）In the field of refractory materials

         Al2O3≥65%： Ensure high temperature stability of refractory materials.

Aluminum silicon ratio (A/S) ≥ 4: determines the efficiency of mullite phase formation and the material's thermal shock resistance.

Traditional sorting bottlenecks: low physical sorting efficiency, high cost and pollution of chemical methods.

二、The Importance of AI Optoelectronic Sorting Technology in Improving the Quality of Bauxite

     The AI photoelectric sorting technology has significant breakthrough significance in improving the grade of bauxite, as shown below: firstly, through deep learning and computer vision, it captures multidimensional features such as ore color, texture, and luster, distinguishes aluminum minerals (such as trihydrate alumina) from silicate impurities (such as quartz and kaolinite), and achieves a recognition accuracy of 98%.

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

       Thirdly,Synchronize the identification of iron oxides (such as hematite), remove impurities through air valve blowing, and reduce the iron content from 8% -12% to 3% -6%.

三、The specific application path of AI sorting technology in bauxite upgrading

（一） Pre selection stage: AI driven efficient removal of impurities

（1）Ore pretreatment

      Intelligent grading: Using the Mingde AI intelligent sorting machine series, wet sorting is carried out on ores with a particle size of 3-8cm, with a processing capacity of 35-50 tons/hour and a pre rejection rate of 30% -50%.

       Impurity targeted removal: Based on transfer learning technology, a sorting model is constructed with a small number of samples for precise separation of silicon and iron impurities, reducing the load of subsequent chemical leaching processes.

（二） Deep purification stage: AI and chemical/physical process synergy

（2）AI sorting flotation combined process

        Coarse particle discarding: After AI sorting pretreatment, the amount of flotation reagents used is reduced by 30%, and the efficiency of positive flotation desilication is increased to over 90%.

       Tailings re selection: The AI sorting machine sorts and reutilizes low value ores from tailings or vein edges accumulated in tailings ponds, and can recover 30-50% of high-value bauxite ore.

         In the future, with algorithm iteration and cross technology integration, AI sorting will become the core engine for sustainable development of the aluminum industry.