基于IGA-FL融合算法的有色金属选矿精矿品位优化研究

Optimization of concentrate grade in non-ferrous metals mineral processing based on IGA-FL fusion algorithm

摘要: 为优化有色金属选矿过程的精矿品位，将模糊逻辑算法与免疫遗传算法进行融合，设计出了一种IGA-FL融合算法，并基于该融合算法构建有色金属选矿检测模型，对有色金属选矿的精矿品位进行检测和优化。对比试验结果显示，IGA-FL融合算法的数据查全率为99.7 %，计算速度为16.7 bps；基于该算法的检测模型平均检测准确率为97.3 %，检测耗时1.8 s。应用基于IGA-FL融合算法的检测模型后，有色金属选矿精矿品位达到70.5 %，说明该检测模型能够对有色金属选矿的精矿品位进行优化。

Abstract: To optimize the concentrate grade in the beneficiation process of non-ferrous metals,a fusion algorithm combining fuzzy logic(FL) and immune genetic algorithm(IGA) was developed.Based on this fusion algorithm,a detection model for the beneficiation of non-ferrous metals was constructed to detect and optimize the concentrate grade.Comparative test results show that the data recall rate of the IGA-FL fusion algorithm is 99.7 %,with a computation speed of 16.7 bps.The average detection accuracy of the model based on this algorithm is 97.3 %,with a detection time of 1.8 s.After applying the detection model based on the IGA-FL fusion algorithm,the concentrate grade of non-ferrous metal beneficiation reached 70.5 %,indicating that this model can optimize the concentrate grade effectively.