摘要
为快速实现井下用风地点智能按需调控优化,提出一种基于多策略融合麻雀搜索算法的矿井风流智能调控方法.以用风地点需风量为优化目标建立数学模型,采用不可微精确罚函数对模型进行去约束化;依据风网灵敏度理论分析灵敏度衰减特征,初步确定待优化风量的最优调节分支集及其风阻调节范围作为初始解集;为求解目标用风地点风量的最优可调值,利用多策略融合的改进麻雀搜索算法在初始解集内对目标函数进行寻优,依次采用Tent混沌映射初始化种群、自适应权重动态控制发现者的搜索步长、联合柯西算子与高斯算子对每代个体变异扰动,并基于贪婪规则保留优值的改进策略,根据改进后算法的寻优结果判定最优调风方案;经矿山智能通风系统实验平台测试,验证了矿井用风地点按需调控算法及方案可行可靠.
In order to quickly realize the intelligent on-demand adjustment and optimization of the mine ventilation location,an intelligent adjustment method of mine air flow was proposed based on multi-strategy fusion sparrow search algorithm.First,a mathematical model was established with the ventilation location’s air volume demand as optimization objective,and a non-differentiable precise penalty function was used to de-constrain the model.Next,sensitivity attenuation characteristics were analyzed according to sensitivity theory of the ventilation network,and the optimal adjustment branch set of the air volume to be optimized and the adjustable range of wind resistance were initially determined as the initial solution set.Then,in order to find the optimal adjustable value of the target ventilation location’s air volume,the improved sparrow search algorithm with multi-strategy fusion was used to optimize the objective function in the initial solution set.In these improvement strategies,Tent chaotic map was used to initialize the sparrow population,adaptive weight was used to control the search step size of the sparrow discoverer,Cauchy operator and Gaussian operator were combined to mutate and perturb each generation of individuals and greedy rule was used to retain the optimal value.According to the optimization result of the improved algorithm,the optimal adjustment plan of air flow was determined.Finally,the test on the experimental platform of the mine’s intelligent ventilation system verifies that the on-demand adjustment algorithm and plan of the mine ventilation location is feasible and reliable.
作者
吴新忠
韩正化
魏连江
左玉晓
许嘉琳
李昂
WU Xinzhong;HAN Zhenghua;WEI Lianjiang;ZUO Yuxiao;XU Jialin;LI Ang(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China;School of Safety Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China)
出处
《中国矿业大学学报》
CSCD
北大核心
2021年第4期725-734,共10页
Journal of China University of Mining & Technology
基金
国家重点研发计划项目(2018YFC0808100)
国家自然科学基金面上项目(52074282)。
关键词
矿井通风
风网灵敏度
麻雀搜索算法
多策略融合
智能按需调控
mine ventilation
ventilation network sensitivity
sparrow search algorithm
multi-strategy fusion
intelligent on-demand adjustment
