摘要
根据地下铀矿山穿孔爆破系统特点及放射性污染物的特性,应用自适应模糊推理技术构建爆破矿石块度模型,应用放射性污染物迁移机制构建氡浓度析出模型,再将其模型参数换算成与穿爆参数关联的氡浓度模型,然后构建铀矿山氡浓度控制下穿孔爆破系统的集成模型,最后运用免疫遗传优化算法对其进行优化求解。实例分析表示该方法可在满足氡浓度的要求下实现块度最优化问题,为铀矿山安全生产提供了重要决策依据。
According to the characteristics of drilling- blasting system and the features of radioactive pollutants in underground uranium mine,the fragmentation model of blasting ore was established by using the adaptive fuzzy inference technology,and the precipitation model of Radon concentration was constructed by using the migration mechanism of radioactive pollutants. The parameters of models were converted to radon concentration model which is related to drilling- blasting parameters,and the integrating model of drilling- blasting system was founded under the control of radon concentration in uranium mine. Finally,the optimization solution was conducted on the model by using the immune genetic optimization algorithm.The case study showed that the method can realize the fragmentation optimization with meeting the demand of radon concentration,and it provides important decision basis for the work safety in uranium mine.
出处
《中国安全生产科学技术》
CAS
CSCD
北大核心
2016年第4期81-84,共4页
Journal of Safety Science and Technology
基金
国家自然科学基金项目(51174116)
关键词
穿孔爆破系统
爆破块度
放射性污染物
免疫遗传算法
drilling-blasting system
blasting fragmentation
radioactive pollutants
immune genetic algorithm
