距离幂次反比法在矿点品位估算中的运用

Application Of Inverse Distance Power Method In Ore Point Grade Estimation

矿点品位预测中常用的平均系数法虽计算量小便于操作,但具有局限性,有时无法准确地估算出矿石的品位,导致出矿点品位波动较大,增加精矿生产成本。为解决这一问题,可以采用地质学领域中常用的距离幂次反比法代替平均系数法。距离幂次反比法引入预估点品位的影响与距离相关,距离越近影响越大,反之影响较小。基于粒子群(PSO)算法确定距离幂次反比法中的最优幂次,从不同的层次和角度提出相应解决途径,解决矿点品位估计值与实际值相差较大这一现象,合理研究配矿方法,从而降低品位波动,降低生产成本。

Although the average coefficient method commonly used in the prediction of ore point grade is easy to operate,it has limitations and sometimes it can not accurately estimate the ore grade,leads to a large fluctuation of ore point quality,increases concentrate pithy ore cost.In order to solve this problem,the inverse distance power method commonly used in the field of geology can be used instead of the average coefficient method.The inverse distance power ratio method introduces the influence of prediction point grade and distance correlation,the closer the distance is,the greater the influence is,on the contrary,the impact is small.Based on the particle swarm optimization(PSO)algorithm to determine the optimal power,from different levels and angles,the corresponding solutions are proposed to solve the problem that the estimated value of ore point grade is quite different from the actual value,so as to reasonably study the ore distribution method,thus reduce the grade fluctuation and production cost.