基于小生境粒子群优化特征图的矿井通风网络设计  

Design of mine ventilation network based on niche particle swarm optimization feature map

作　　者：刘海胜 陈诚 李瑞卿 郑义 LIU Haisheng;CHEN Cheng;LI Ruiqing;ZHENG Yi(Guojiawan Coal Mine Branch,CHN Energy Yulin Energy Co.,Ltd.,Yulin 719000,Shaanxi,China;CCTEG China Coal Research Institute,Beijing 100013,China;CCRI Tongan(Beijing)Intelligent Control Technology Co.,Ltd.,Beijing,100013,China)

机构地区：[1]国能榆林能源有限责任公司郭家湾煤矿分公司,陕西榆林719000 [2]煤炭科学技术研究院有限公司,北京100013 [3]煤科通安(北京)智控科技有限公司,北京100013

出　　处：《矿山机械》2024年第10期5-9,共5页Mining & Processing Equipment

基　　金：煤炭科学技术研究院有限公司科技发展基金项目(2023CX-Ⅱ-15)。

摘　　要：传统的矿井通风系统设计忽视了系统内部微小生态环境的差异,限制了通风系统的性能和能效性,具有极大安全隐患。针对此类问题,设计基于小生境二进制粒子群算法(Niche Binary Particle Swarm Optimization,NBPSO)的融合优化通风网络。NBPSO算法以其收敛速度快、全局寻优能力强等优点,克服了传统优化算法迭代慢、寻优能力差等劣势,实现了网络模型设计快速、精准成型。对比试验引入三维通风网络测试系统和Q-H网络示意图,试验结果表明,NBPSO局部搜索精度高,复杂通风网络结构优势明显。The traditional design of mine ventilation system ignores the small ecological environment differences within the system,limits the performance and energy efficiency of the ventilation system,and has great potential safety hazards for mining workers.Aiming at such problems,a niche binary particle swarm optimization(NBPSO)algorithm was designed to integrate and optimize the ventilation network in this paper.NBPSO algorithm had the advantages of fast convergence speed and strong global optimization ability,and could overcome the disadvantages of traditional optimization algorithms such as slow iteration and poor optimization ability,so as to realize rapid and accurate network model design.Then,the three-dimensional ventilation network test system and Q-H network diagram were introduced in the comparative test.The test results showed that the NBPSO algorithm had high local search accuracy and obvious advantages in complex ventilation network structure.

关 键 词：矿井通风系统 小生境二进制粒子群算法 收敛速度 通风网络 Q-H图 

分 类 号：TD725[矿业工程—矿井通风与安全]