基于采矿工程安全的MGWO在矿井风网风量优化调节中的应用  

Application of MGWO Based on Mining Engineering Safety in Optimal Adjustment of Mine Ventilation Network

作　　者：郭建彪[1] GUO Jianbiao(Anhui Mining Vocational and Technical College,Huaibei Anhui 235000,China)

机构地区：[1]安徽矿业职业技术学院,安徽淮北235000

出　　处：《佳木斯大学学报（自然科学版）》2024年第3期161-164,共4页Journal of Jiamusi University：Natural Science Edition

基　　金：2021年度安徽高校自然科学研究项目(KJ2021A1600)。

摘　　要：针对当前采矿工程中矿井风网风量优化调节效果不够理想的缺陷,研究提出一种风量优化调节模型,并采用优化的灰狼优化算法(Grey Wolf Optimization,GWO)对其进行求解,完成矿井风网风量智能优化调节。结果显示,MGWO算法求解的风量优化平均值为216.4kW,平均运行时间为132.6s,均优于其他两种算法。上述结果说明,研究提出的MGWO对矿井风网风量优化调节模型的求解效率与精度更高,对风网风量优化调节的效果更好,能够有效调节风网风量,保证采矿工程安全性。In response to the deficiency of insufficient optimization and regulation effect of mine air network air volume in current mining engineering,a model for air volume optimization and regulation is proposed,and an optimized Grey Wolf Optimization(GWO)algorithm is used to solve it,achieving intelligent optimization and regulation of mine air network air volume.The results show that the average air volume optimization value solved by the MGWO algorithm is 216.4kW,and the average operating time is 132.6s,both of which are superior to the other two algorithms.The above results indicate that the MGWO proposed in the study has higher efficiency and accuracy in solving the optimization and adjustment model of mine air network air volume,and has a better effect on the optimization and adjustment of air network air volume.It can effectively regulate the air network air volume and ensure the safety of mining engineering.

关 键 词：灰狼优化算法 采矿工程 矿井风网 风量优化 

分 类 号：TD724[矿业工程—矿井通风与安全]