摘要
为综合衡量配矿效果,基于多目标优化理论,以磨浮入选原矿组分指标、入选原矿品质稳定及最大限度利用原矿为目标,构建磷矿堆场多目标优化配矿模型,并采用改进的多目标遗传算法求解该模型。经约束多目标优化算例测试结果表明:改进的多目标遗传算法可以找到多目标优化问题分布广泛、均匀的Pareto最优解集。并针对磷矿浮选堆场开展多目标优化配矿,现场测试结果显示,矿石混配后P2O5含量23.052%,MgO含量4.195%,混配原矿30 654 t,比常规优化方案资源利用率提高0.31%。研究结果表明,该多目标优化配矿技术可实现稳定矿石品质的同时最大限度地利用原矿。
In order to evaluate the effect of mine ore blending comprehensively, a new ore blending model was proposed for phosphate rock stacking yard, based on the multi-objective optimization the- ory. The stability of ore quality and ore component indexes requirements for mineral processing and maximum utilization of ore were chosen as the objective function of the multi-objective mathematical model. Multi-objective genetic algorithm was improved to solve the ore blending model. It was tested by two constrained multi-objective optimization examples, and the results indicated that the improved genetic algorithm could find the Pareto optimal solutions set in a multi-objective optimization problem and the Pareto optimal solutions are scattered extensively and uniformly. This method was applied to ore blending for phosphate rock stacking yard. The field test results showed that compared with the normal method the utilization rate of raw ore was increased by 0. 31% by the new method, and a to- tal of 30654t phosphate rock with 23.052% P2Os and 4. 195% MgO was mixed legend. This result confirms that stability and maximum utilization of ore can be realized by the multi-objective optimiza- tion ore blending method.
出处
《广西大学学报（自然科学版）》
CAS
北大核心
2013年第5期1230-1238,共9页
Journal of Guangxi University（Natural Science Edition）
基金
"十二五"国家科技支撑计划资助项目(2011BAB08B00)
武汉工程大学科学研究基金资助项目(13115042)
关键词
遗传算法
多目标优化
配矿模型
genetic algorithm
multi-objective optimization
mathematical model of ore blending
