摘要
现有矿井通风系统风阻校准模型未严格界定摩擦阻力因数的约束情况,并缺乏风阻调整幅度的相关考虑,导致风阻校准效率低。针对上述问题,提出了一种以风阻修正量最小化及实测-解算风量误差最小化为双重目标的风阻校准优化模型。该模型严格划定风阻参数的物理可行域,通过加权法将双目标优化模型转换为单目标函数,协同实现风阻修正量最小化与实测-解算风量误差最小化,维持初始风阻参数的物理真实性,并提升网络解算的精度。在模型求解方面,将下山单纯形(NMS)算法的几何操作与蜣螂优化(DBO)算法的生物寻优行为相结合,提出NMS-DBO算法,该算法通过自适应权重融合策略、反射方向感知与滚球导航的融合策略、收缩回退与孵卵开发的融合策略使风阻校准优化模型输出最优风阻解,确保风阻参数在物理约束范围内实现高精度校准。以含有35个分支的某矿井通风网络为研究对象,建立风阻校准模型,并采用NMS-DBO算法、鲸鱼优化算法(WOA)、粒子群优化(PSO)算法及DBO算法分别进行求解,结果表明:NMS-DBO算法求解的目标函数值为0.454,优于WOA(0.853)、PSO算法(0.525)及DBO算法(0.521),表明NMS-DBO算法在收敛精度上优于其他3种算法;NMS-DBO算法求解的最优风阻与初始风阻的相关性系数达0.998,最大实测-解算风量误差从22.99 m^(3)/s降至3 m^(3)/s以内,实现了风阻参数与风量高精度匹配的协同优化。
Existing airway resistance calibration models for mine ventilation systems do not strictly define the constraints for the friction resistance factor and lack consideration for the magnitude of resistance adjustment,leading to low calibration efficiency.To address these issues,a dual-objective resistance calibration optimization model was proposed to minimize both the resistance correction amount and the error between measured and calculated airflow.This model strictly defined the physically feasible region for airway resistance parameters and converted the dual-objective optimization model into a single-objective function using a weighting method.This approach synergistically minimized the resistance correction amount and the error between measured and calculated airflow,maintained the physical reality of the initial resistance parameters,and improved the accuracy of the network calculation.To solve the model,the NMS-DBO algorithm was proposed by combining the geometric operations of the Nelder-Mead Simplex(NMS)algorithm with the bio-inspired optimization behavior of the Dung Beetle Optimizer(DBO).The algorithm implemented an adaptive weight fusion strategy,a fusion strategy of reflection direction perception and ball-rolling navigation,and a fusion strategy of contraction-retreat and brood-laying development to output the optimal resistance solution and ensure high-precision calibration of resistance parameters within physical constraints.A resistance calibration model was established for a mine ventilation network with 35 branches and solved using the Nelder-Mead Simplex-Dung Beetle Optimizer(NMS-DBO),Whale Optimization Algorithm(WOA),Particle Swarm Optimization(PSO),and DBO algorithms,respectively.The results showed that the objective function value obtained by the NMS-DBO algorithm was 0.454,which was superior to that of the WOA(0.853),PSO(0.525),and DBO(0.521)algorithms,indicating that the NMS-DBO algorithm outperformed the other three in terms of convergence accuracy.Furthermore,the similarity coefficient between the optimal resistance solved by NMS-DBO and the initial resistance reached 0.998,and the maximum error between measured and calculated airflow was reduced from 22.99 m^(3)/s to within 3 m^(3)/s,thereby achieving the synergistic optimization of a high-precision match between resistance parameters and airflow.
作者
闫振国
张龙成
王延平
曹豫其
刘毅涛
陈崇武
阮佳宁
YAN Zhenguo;ZHANG Longcheng;WANG Yanping;CAO Yuqi;LIU Yitao;CHEN Chongwu;RUAN Jianing(College of Safety Science and Engineering,Xi'an University of Science and Technology,Xi'an 710054,China)
出处
《工矿自动化》
北大核心
2025年第9期124-132,共9页
Journal of Mine Automation
基金
国家自然科学基金资助项目(52074214)
陕西省秦创原“科学家+工程师”队伍建设项目(2024QCY-KXJ-190)
陕西省重点研发计划资助项目(2023-YBSF-190)。
关键词
矿井通风系统
初始风阻校准
下山单纯形法
蜣螂优化算法
通风网络解算
实测-解算风量误差
风阻修正量
mine ventilation network
initial airway resistance
Nelder-Mead Simplex algorithm
Dung Beetle Optimizer
ventilation network calculation
error between measured and calculated airflow
airway resistance correction amount
