基于神经网络的深部磷矿岩体可爆性分级模型研究  

Study on Rock Blastability Classification of Deep Phosphate Ore Rock Mass based on Neural Network

作　　者：柴修伟[1] 李成镇 盛益明 徐玉萍 徐亮[3] 金胜利 CHAI Xiu-wei;LI Cheng-zhen;SHENG Yi-ming;XU Yu-ping;XU Liang;JIN Sheng-li(School of Resources and Safety Engineering,Wuhan Institute of Technology,Wuhan 430073,China;School of Environmental and Biological Engineering,Wuhan Technology And Business University,Wuhan 430065,China;Hubei Xingfa Chemical Group Co.,Ltd.,Yichang 443700,China)

机构地区：[1]武汉工程大学资源与安全工程学院,武汉430073 [2]武汉工商学院环境与生物工程学院,武汉430065 [3]湖北兴发化工集团股份有限公司,宜昌443700

出　　处：《爆破》2025年第1期71-80,共10页Blasting

基　　金：2021年湖北省安全生产专项资金科技项目(SJZX20211004);武汉工商学院科研团队支持计划专项资助(WPT2023036)。

摘　　要：目前钻爆法仍是深部磷矿开拓掘进和回采的最高效方法。而磷矿钻爆法施工掘进水平长年维持在70~80 m/月,严重制约了掘进效率,因此对深部磷矿工作面开展矿岩体可爆性分级工作至关重要。以湖北宜昌某地下磷矿为研究背景,在现场进行了岩体的纵波波速测试,开展了岩石密度、单轴抗压强度和抗拉强度等物理力学性质的测量,得到了白云质条带磷块岩、致密条带磷块岩、泥质条带磷块岩和含碳泥质白云岩4种岩石的密度、单轴抗压强度、抗拉强度和岩体完整性系数4项参数。通过调用Matlab神经网络工具箱,将岩石密度、单轴抗压强度、抗拉强度、岩体完整性系数作为输入,以可爆性等级作为输出,采用随机函数法产生大量的训练样本,构建了基于BP神经网络的可爆性评价模型,实现了深部磷矿岩体可爆性分级。分级结果为白云质条带磷块岩和泥质条带磷块岩为中等可爆,致密条带磷块岩和含碳泥质白云岩为难爆。根据分级结果,可对采场爆破参数进行优化,增强爆破效果,降低炸药单耗及矿石大块率,提高深部磷矿开采的安全性及经济效益。Drilling and blasting is still the most efficient way to explore deep phosphate mine excavation and mining.There is a severe constraint on the efficiency of phosphate mine digging as its level remained at 70 to 80 meters every month for many years.Therefore,the ore rock blastability classification is critical for the deep phosphate mine working face.The longitudinal wave velocity tests of the rock body in an underground phosphate mine in Yichang,Hubei Province,and measurements of physical and mechanical properties such as rock density,uniaxial compressive strength and tensile strength were carried out.The rock density,uniaxial compressive strength,tensile strength,and rock integrity coefficient were obtained for four types of rocks,namely,dolomitic striped phosphorite,dense striped phosphorite,argillaceous striped phosphorite,and carbon-bearing argillaceous dolomite.To complete the deep phosphorite workings of the mine rock blastability classification,a BP neural network model was established by stochastic functions to generate a large number of learning and testing samples using the Matlab neural network toolbox as taking the pre-measured rock density,uniaxial compressive strength,tensile strength and rock integrity coefficients as inputs and the rock blastability classification as outputs.The grading results show that dolomite-banded phosphorite and mud-banded phosphorite are moderately blastable,and dense-banded phosphorite and carbonaceous mud dolomite are difficult to blast.According to the classification results,the blasting parameters of the stope can be optimized to enhance the blasting effect,reduce the single consumption and the bulk rate of explosives,and improve the safety and economic benefits of deep phosphate mining.

关 键 词：深部磷矿 岩体可爆性分级 随机函数 神经网络模型 

分 类 号：TD235.3[矿业工程—矿井建设]