矿井通风系统的安全、稳定是矿井安全生产的基础。近年来矿井通风系统隐患自动识别技术已成为研究的热点，并取得了较多研究成果。但许多研究成果仍没有达到实用的程度，实际隐患识别时主要以人工检测和安全检查表为主。为了能建立一套贴近生产实际的矿井通风系统隐患识别模型和判定指标，以金凤煤矿安全监测监控系统的历史数据为基础，基于矿井通风理论、统计学原理，建立了一套简单、实用的通风系统隐患识别模型和判定指标，并进行现场应用试验。主要进行了如下研究：（1）主要针对试验矿井特点，构建了5类通风系统隐患的识别模型和指标。针对对风量不足和局部通风机循环风隐患，先用Logistic分布表征风速异常的概率分布，建立统一的规则对概率分布参数进行评分，结合分段线性插值方法构建了两类隐患的识别模型和指标。针对风流短路/断路和风流不稳定隐患，主要基于正态分布的3Sigma原则判别是否存在风压异常波动隐患，建立了相应的隐患识别模型和指标。（2）根据试验矿井监控系统数据，分析了所建立的各类隐患指标的演化特征，对是否存在隐患进行初步判定，结合现场监控视频、人员及车辆精准定位等信息分析引发隐患的原因，最终验证所构建模型和指标的有效性。（3）针对已经识别出的试验矿井通风系统隐患，提出了两个优化方案。通过Ventsim软件对矿井通风系统进行建模，并模拟了两个方案的实施效果，从中优选出更好的方案，实现了对试验矿井通风系统的优化。

The safety and stability of mine ventilation system is the basis of mine safety production. In recent years, the automatic identification technology of hidden dangers in mine ventilation system has become a research hotspot, and many research results have been achieved. However, many research results have not reached the practical level, and the actual hidden dangers are mainly identified by manual detection and safety checklist. In order to establish a set of hidden danger identification model and judgment index of mine ventilation system close to the actual production, based on the historical data of jinfeng coal mine safety monitoring system, based on mine ventilation theory and statistical principle, a set of simple and practical hidden danger identification model and judgment index of ventilation system was established, and the field application test was carried out. The main research is as follows.(1)According to the characteristics of the test mine, the identification models and indexes of five kinds of hidden dangers in ventilation system are constructed. Aiming at the hidden danger of insufficient air volume and circulating air of local ventilator, the probability distribution of abnormal wind speed is characterized by Logistic distribution, and unified rules are established to grade the parameters of probability distribution. Combined with piecewise linear interpolation method, the identification models and indicators of two kinds of hidden dangers are constructed. Aiming at the hidden dangers of short/open circuit and unstable airflow, this paper mainly determines whether there are hidden dangers of abnormal fluctuation of wind pressure based on the 3Sigma principle of normal distribution, and establishes the corresponding hidden danger identification model and index.(2)According to the data of the monitoring system of the test mine, the evolution characteristics of all kinds of hidden danger indicators are analyzed, and the existence of hidden dangers is preliminarily judged. Combined with the information of on-site monitoring video, accurate positioning of personnel and vehicles, the causes of hidden dangers are analyzed, and finally the effectiveness of the model and indicators is verified.(3)Aiming at the hidden dangers of the ventilation system in the test mine, two optimization schemes are put forward. The mine ventilation system is modeled by Ventsim software, and the implementation effects of the two schemes are simulated, from which the better scheme is selected, and the optimization of the experimental mine ventilation system is realized

[1] 王显政. 中国煤炭工业发展面临的机遇与挑战[J]. 中国煤炭, 2010, 36(07):5-7+10.

[2] 谢和平, 吴立新, 郑德志. 2025年中国能源消费及煤炭需求预测[J]. 煤炭学报, 2019, 44(07):1949-1960.

[3] 周少统. 我国煤炭供需趋势及开发强度研究[J]. 煤炭科学技术, 2020, 48(S1): 85-88.

[4] 张钟毓, 柴建. 中国煤炭需求总量预测与结构变化分析[D]. 陕西师范大学, 2015.

[5] 钱鸣高, 许家林, 王家臣. 再论煤炭的科学开采 [J]. 煤炭学报, 2018, 43(01):1-13.

[6] LIN J, FRIDLEY D, LU H, et al. Has coal use peaked in China: Near-term trends in China’s coal consumption[J]. Energy Policy, 2018, 123(12):208-214.

[7] TRECHERA P, MORENO T, CORDOBA P, et al. Comprehensive evaluation of potential coal mine dust emissions in an open-pit coal mine in Northwest China[J]. International Journal of Coal Geology, 2021, 235(15):1-17.

[8] FABIANO B, CURRÒ F, REVERBERI A P, et al. Coal dust emissions: From environmental control to risk minimization by underground transport. An applicative case-study[J]. Process Safety & Environmental Protection, 2014, 92(2):150-159.

[9] KEITH W, BRIAN P, DANIEL S J. The practice of mine ventilation engineering[J]. International Journal of Mining Science and Technology, 2015, 25(02):165-169.

[10] 王欣艳. 煤矿安全隐患治理能力评估与预测方法研究[D]. 中国矿业大学（北京）, 2013.

[11] 卢新明. 矿井通风智能化技术研究现状与发展方向[J]. 煤炭科学技术, 2016, 44(07):47-52.

[12] 张庆华, 姚亚虎, 赵吉玉. 我国矿井通风技术现状及智能化发展展望[J]. 煤炭科学技术, 2020, 48(02):97-103.

[13] HARDCASTLE S G, GRENIER M G, BUTLER K C. Electronic Vane Anemometry: Finding A Suitable Replacement of Mechanical Analog Devices For Mine Airflow Assessment[J]. Proceedings of the US Mine Ventilation Symposium, 1991, 482-493.

[14] WU X S, TOPUZ E. Analysis of mine ventilation systems using operations research methods[J]. International Transactions in Operational Research, 1998, 5(4):245-254.

[15] EI-ABNOUDY A A, HASSAN S F. Air renewal times and ventilation rate calculations for underground workings using radioactive measurement[J]. International Journal of Mining Science and Technology, 2016, 26(5):843-850.

[16] 王雨. 矿井通风系统实时模拟与异常风量分析研究[D]. 西安科技大学, 2006.

[17] 王红刚, 吴奉亮, 王雨. 基于灵敏度的通风网络风量异常值分析[J]. 煤矿安全, 2008, 406(09):86-88.

[18] 汪崇鲜, 李绪国, 谭波. 矿井通风系统风量稳定性的影响因素[J]. 煤炭学报, 2008, 167(08):931-935.

[19] 冀敏俊. 高河能源东翼盘区矿井防水防火防爆隔离门位置及其安全隐患排查[J]. 煤矿安全, 2012, 43(12):210-213

[20] 潘竟涛, 赵丹, 李宗翔, 等. 大明矿通风系统故障源诊断及风速传感器的布置[J]. 煤炭学报, 2013, 38(S1):153-158.

[21] 刘剑, 郭欣, 邓立军, 等. 基于风量特征的矿井通风系统阻变型单故障源诊断[J]. 煤炭学报, 2018, 43(01):143-149.

[22] STANEK W, CZARNOWSKA L, PIKON K, et al. Thermo-ecological cost of hard coal with inclusion of the whole life cycle chain[J]. Energy, 2015, 92(3): 341-348.

[23] EULER D S. Application of ventilation management programs for improved mine safety[J]. International Journal of Mining Science and Technology, 2017, 27(04): 647-650.

[24] LEBECKI K, MAIACHOWSKI M, SOITYSIAK T. Continuous dust monitoring in headings in underground coal mines[J]. Journal of Sustainable Mining, 2016, 15(4):125-132.

[25] LOWNDES I S, CROSSLEY A J, YANG Z Y. The ventilation and climate modelling of rapid development tunnel drivages[J]. Tunnelling and underground space technology, 2004, 19(2):139-150.

[26] 孙燕, 杨胜强, 程建维, 等. 基于灰色关联分析的煤矿通风系统两级评价[J]. 煤矿安全, 2008, 402(05):44-48.

[27] 王洪德, 马云东. 基于粗糙集-神经网络的矿井通风系统可靠性评价仿真研究[J]. 系统工程理论与实践, 2005(07):81-86.

[28] 丁厚成, 黄新杰. 基于AHP-FCE的煤矿通风系统可靠性评价研究[J]. 自然灾害学报, 2013, 22(03):153-159.

[29] 吴新忠. 煤矿主通风机通风失稳控制的研究与应用[D]. 中国矿业大学, 2010.

[30] 王从陆. 非灾变时期金属矿复杂矿井通风系统稳定性及数值模拟研究[D]. 中南大学, 2007.

[31] 王从陆, 吴超, 王卫军. Lyapounov理论在矿井通风系统稳定性分析中的应用[J]. 中国安全生产科学技术, 2005(04):46-49.

[32] 王从陆, 李树清, 吴超. 矿井通风系统与耗散结构理论[J]. 中国安全科学学报, 2004(06):14-16.

[33] 张俭让, 董丁稳. WNN的矿井通风系统可靠性评价模型[J]. 西安科技大学学报, 2009, 29(05):531-535.

[34] 杨卓亚. 矿井通风系统重大隐患监测识别与安全评价方法研究[D]. 西安科技大学, 2021.

[35] 龚晓燕, 孙晓辉. 基于遗传算法的矿井局部通风故障诊断神经网络模型[J]. 煤矿安全, 2008, 402(05):35-38.

[36] 邢玉忠, 范红伟, 王彦凯, 等. 基于瓦斯监测数据的矿井通风系统合理性评价[J]. 采矿与安全工程学报, 2010, 27(04):522-526+531.

[37] 卢新明, 尹红. 一种矿井通风系统的状态识别方法[P]. 中国: CN103939123A, 2014-07-23.

[38] 胡千庭. 矿井通风系统在线监测及分析预警方法及系统[P]. 中国: CN102650214B, 2014-07-16.

[39] 史采星. 基于监测监控系统的通风异常分析与调节分析研究[D]. 中南大学, 2014.

[40] 方博, 马恒. 运用监控数据的矿井通风网络动态解算及应用[J]. 辽宁工程技术大学学报(自然科学版), 2016, 35(12):1439-1442.

[41] 李敏, 赵硕嫱. 基于实施监测数据的矿井通风仿真技术研究[J]. 煤炭技术, 2021, 40(05):145-148.

[42] 王保齐, 郭念波. 矿井全风量降温技术研究与工程实践[J]. 中国安全生产科学技术, 2016, 12(S1): 57-60.

[43] 吴国珉. 典型有色金属矿山矿井通风系统优化与防尘技术研究[D]. 中南大学, 2008.

[44] LEE R D, LONGSON I. Controlled recirculation of mine air in working districts[J]. 1987.

[45] HARDCASTLE S G, KOLADA R J, STOKES A W. Studies into the wider application of controlled recirculation in mine ventilation[J]. 1984.

[46] 严鹏. 二道沟金矿深井开采可控循环通风系统研究[D]. 中南大学, 2013.

[47] 龚开福, 李夕兵, 李国元, 等. 大型机械化金属矿山通风系统优化[J]. 金属矿山, 2015, 463(01):122-127.

[48] 林安栋. 矿井通风安全监测监控系统关键技术研究[D]. 辽宁工程技术大学, 2008.

[49] 胡穗延. 煤矿自动化和通信技术现状与发展趋势[J]. 煤炭科学技术, 2007, 393(08):1-4.

[50] 韩慧. 矿井通风安全自动监测报警系统[D]. 山东科技大学, 2006.

[51] 吴丽春. 矿井通风监测系统的研究与设计[D]. 中南大学, 2012.

[52] 刘佳季. 矿井智能通风与实时监测系统[J]. 能源与节能, 2021, 192(09):157-158.

[53] 曹旭辉. 基于WIFI通信技术的矿井通风监测系统研究[D]. 西安建筑科技大学, 2013.

[54] 李潇. 长距离矿井通风优化及阻力信息可视化管理研究[D]. 山东科技大学, 2018.

[55] 梁军. 基于GIS的矿井通风在线监控与预警系统[J]. 能源技术与管理, 2012, 147(05):139-140+168.

[56] 郭年琴, 黄国平, 王海宁. 矿井通风网络三维仿真系统的开发[J]. 中国钨业, 2005(05):45-48.

[57] 沈澐, 王海宁. 矿井通风网络三维仿真与优化系统研究[J]. 矿业安全与环保, 2009, 36(02): 10-12+15+92.

[58] FENG W, ZHU F P, LV H Q. The Use of 3D Simulation System in Mine Ventilation Management[J]. Procedia Engineering, 2011, 26:1370-1379.

[59] STEFOPOULOS E K, DAMIGOS D G. Design of emergency ventilation system for an underground storage facility[J]. Tunnelling & Underground Space Technology, 2007, 22(3):293-302.

[60] KIM K N, KIM D W, JEONG M A, et al. Comparison of pressure-controlled ventilation with volume-controlled ventilation during one-lung ventilation: a systematic review and meta-analysis[J]. Bmc Anesthesiology, 2016, 16(1): 72-82.

[61] 季现伟. 矿井通风监测系统与三维通风仿真系统信息融合研究与应用[J]. 金属矿山, 2017, 495(09): 186-190.

[62] 孙海交, 班亚, 杨威, 等. 基于GIS的矿山通风安全信息系统设计与实现[J]. 矿山机械, 2009, 37(07):21-25.

[63] 李成, 谭海樵. 基于GIS的矿井通风网络可视化仿真模拟研究[J]. 金属矿山, 2009, 402(12):103-106.

[64] ZHANG X Q, CHENG W M, ZHANG Q, et al. Partition airflow varying features of chaos-theory-based coalmine ventilation system and related safety forecasting and forewarning system[J]. International Journal of Mining Science and Technology, 2017, 27(02):269-275.

[65] 李晓荣. 基于综合集成评价方法的矿井通风系统安全评价研究及应用[D]. 太原理工大学, 2012.

[66] 韩艳杰, 张志军, 李亚俊. 基于新型综合集成法的矿井通风系统安全评价[J]. 中国安全生产科学技术, 2014, 10(02):75-80.

[67] WANG Y J, MUTMANSKY J M. Modeling mine ventilation networks using five basic network elements[J]. Mining Engineering, 1997, 49(12): 65-69.

[68] BARNES R J, JOHNSON T B. Out-of-lilter algorithm and its applications in mining engineering[J]. 1983.

[69] ONDER M, SARAC S, CEVIK E. The influence of ventilation variables on the volume rate of airflow delivered to the face of long drivages[J]. Tunnelling and Underground Space Technology, 2006, 21(5): 568-574.

[70] LOWNDES I S, YANG Z Y. The application of GA optimisation methods to the design of practical ventilation systems for multi-level metal mine operations[J]. Mining Technology, 2004, 113(1):43-58.

[71] 兰林, 陈日辉, 王孝东, 等. 基于模糊综合评价的深井通风系统评价研究[J]. 化工矿物与加工, 2019, 48(08):15-19.

[72] ANDREWS J D. Quantitative safety assessment of the ventilation recirculation system in an undersea mine[J]. Quality and Reliability Engineering, 2010, 7(6): 497-510.

[73] 张发涛, 蒋仲安, 焦向东. 基于AHP-FPR模型的矿井通风系统优化方案评价[J]. 中国安全生产科学技术, 2012, 8(10):125-130.

[74] 张景钢, 金庆利, 索诚宇. 基于未确知测度理论及层次分析法矿井通风系统安全性评价研究[J]. 华北科技学院学报, 2014, 11(10):34-40.

[75] 胡春亚, 杨胜强, 胡新成, 等. 矿井通风系统新指标体系的建立与应用[J]. 煤炭技术, 2016, 35(10):171-173.

[76] 黄光球, 陆秋琴, 郑彦全. 存在固定风量分支的通风网络解算新方法[J]. 金属矿山, 2004(10):52-54.

[77] 高军军. 复杂通风网络优化评判指标体系确立及应用[D]. 辽宁工程技术大学, 2017.

[78] SHI C P, ZHOU D Y. Research on Index for Assessment of Mine Ventilation Capability[J]. Advance Journal of Food Science & Technology, 2015, 7(10):780-785.

[79] 陈宙. 矿井通风阻力测定平差分析及系统优化技术的研究[D]. 北京科技大学, 2007.

[80] 程磊, 党海波, 彭信山. 矿井通风网络分析研究现状及趋势[J]. 煤炭工程, 2011, 389(03):90-92.

[81] 段东. 矿井通风系统拓扑关系自动生成的研究及风网解算[D]. 辽宁工程技术大学, 2005.

[82] 苏清政, 刘剑. 矿井通风仿真理论与实践[M]. 煤炭工业出版社, 2006.

[83] 苟红松. 基于Eclipse RCP的煤矿通风信息系统研究[D]. 河南理工大学, 2009.

[84] 姜雪英. 基于Web Service的煤矿通风网络解算系统结构与实现研究[D]. 西安科技大学, 2008.

[85] 袁梅. MATLAB在矿井通风网络解算中的应用[J]. 矿业工程, 2009, 41(05):63-65.

[86] 任晋娟. 高河能源矿井通风系统优化研究[D]. 太原理工大学, 2012.

[87] 曲宗波, 王春耀. 矿井通风系统优化[J]. 煤矿现代化, 2006(S1):67-68.

[88] 李燕. 矿井通风系统安全可靠性的模糊综合评价方法[J]. 现代矿业, 2009, 25(12):16-18.

[89] 王振宇, 牛国庆. 煤矿通风系统经济合理性评价指标权值的确定及应用[J]. 山东煤炭科技, 2016, 188(04):70-72.

[90] 杨俊生, 杨雪风. 矿井通风系统可靠性评价方法研究及软件实现[J]. 内蒙古煤炭经济, 2016, 205(Z1):21+25.