Article(id=1322594796061962336, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322594503278580030, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1709049600000, receivedDateStr=2024-02-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793260772004, onlineDateStr=2026-10-29, pubDate=1737734400000, pubDateStr=2025-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793260772004, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793260772004, creator=13701087609, updateTime=1793260772004, updator=13701087609, issue=Issue{id=1322594503278580030, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='1', pageStart='1', pageEnd='276', issueExtLink='null', onlineDate='null', pubDate='1737734400000', pubDateStr='2025-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793260702199, creator='13701087609', updateTime=1793260702199, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=204, endPage=211, ext={EN=ArticleExt(id=1322594796238123105, articleId=1322594796061962336, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Intelligent Control of Air Flow for Dust Reduction in Different Areas of Personnel Activity of Fully Mechanized Heading Face, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

At present, the traditional total ventilation control method on the fully mechanized heading face fails to realize dynamic control of dust reduction, and the ventilation environment in the area of personnel activity is not considered in the dust reduction process, resulting in dust accumulation. Therefore, a finite element calculation model was established for the coupling of air flow and dust gas-solid, and underground verification was conducted. According to the dust migration and distribution law and the activity range of underground personnel, different areas of personnel activity were divided. The dust mass concentration sample data under different airflow intelligent control schemes were obtained by simulation, and the data were discretized by K-means algorithm. Based on the immune genetic algorithm, the dust reduction scheme was used as the antibody, and the dust mass concentration in different areas of personnel activity was used as the antigen. The acquisition algorithm of the intelligent control of air flow and dust reduction scheme for the different areas of personnel activity were compiled. Taking a mine in northern Shaanxi Province as the research object, the optimal dust reduction scheme for the personnel in the dust accumulation area (in the range of 5−15 m) is 1.2 m outlet aperture, 5° horizontal deflection, and 5 m from the head. The dust reduction effect of the scheme was tested and verified. The result show that, after the application on the experimental platform, the average dust mass concentration in dust accumulation area is reduced from 163.77 mg/m3 to 91.32 mg/m3, which is reduced by 44.24%; the dust concentration at the driver's position is reduced from 161.94 mg/m3 to 78.90 mg/m3, which is reduced by 51.28%. After the application in underground, the average dust mass concentration in dust accumulation area is reduced from 182.81 mg/m3 to 113.94 mg/m3, which is reduced by 37.67%; the dust mass concentration at the driver's position is reduced from 179.65 mg/m3 to 101.27 mg/m3, which is reduced by 43.63%.

, authors=Xiaoyan GONG1, Yujie LI1, Huqiang CHANG2, Zhuangzhuang LIU1, Long CHEN1, Haoran FU1, Xinyu WANG1, Shuai WANG3, authorsList=Xiaoyan GONG, Yujie LI, Huqiang CHANG, Zhuangzhuang LIU, Long CHEN, Haoran FU, Xinyu WANG, Shuai WANG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322594798469492858, articleId=1322594796061962336, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=综掘面不同人员活动区降尘风流智能调控研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

目前传统的综掘面“通风总量”控制方式不能动态调控降尘,而且降尘过程中没有考虑人员活动区通风环境,造成了工作区粉尘聚集。因此,建立了风流与粉尘气固耦合的有限元计算模型并进行井下验证。根据粉尘运移分布规律及井下人员活动范围,划分了不同的人员活动区。模拟获取不同风流智能调控方案的粉尘质量浓度样本数据,利用K-means算法对数据进行离散化处理。基于免疫遗传算法,将降尘方案作为抗体,不同人员活动区粉尘质量浓度作为抗原,开发了不同人员活动区的风流智能调控降尘方案获取算法。以陕北某矿为研究对象,获得了人员处于粉尘聚集区(5~15 m)的最优降尘方案:出风口口径1.2 m、水平偏转5°、距迎头5 m。对该方案的降尘效果进行测试验证,结果表明:在试验平台应用该方案后,粉尘聚集区的平均粉尘质量浓度由原来的163.77 mg/m3降至91.32 mg/m3,降低了44.24%,司机位置处粉尘质量浓度由原来的161.94 mg/m3降至78.90 mg/m3,降低了51.28%。在井下应用该方案后,粉尘聚集区的平均粉尘质量浓度由原来的182.81 mg/m3降至113.94 mg/m3,降低了37.67%,司机位置处粉尘质量浓度由原来的179.65 mg/m3降至101.27 mg/m3,降低了43.63%。

, authors=龚晓燕1, 李宇杰1, 常虎强2, 刘壮壮1, 陈龙1, 付浩然1, 王新雨1, 王帅3, authorsList=龚晓燕, 李宇杰, 常虎强, 刘壮壮, 陈龙, 付浩然, 王新雨, 王帅, authorCompany=null, correspAuthors=null, authorNote=

龚晓燕(1966—),女,甘肃临洮人,博士,教授,主要从事综掘面通风安全、瓦斯和粉尘防治研究。E-mail:

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龚晓燕(1966—),女,甘肃临洮人,博士,教授,主要从事综掘面通风安全、瓦斯和粉尘防治研究。E-mail:

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龚晓燕(1966—),女,甘肃临洮人,博士,教授,主要从事综掘面通风安全、瓦斯和粉尘防治研究。E-mail:

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Journal of Safety and Environment, 2023, 23(2): 424-434., articleTitle=Development of air flow monitoring and adaptive intelligent control system for heading face, refAbstract=null)], funds=[Fund(id=1322594807424331995, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, awardId=52374226, language=CN, fundingSource=国家自然科学基金面上项目(52374226), fundOrder=null, country=null), Fund(id=1322594809106247900, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, awardId=2021JLM-01, language=CN, fundingSource=陕西省自然科学基础研究计划(企业陕煤联合基金项目)(2021JLM-01), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322594800260460667, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, xref=1., ext=[AuthorCompanyExt(id=1322594800268849276, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, companyId=1322594800260460667, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China), AuthorCompanyExt(id=1322594800277237885, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, companyId=1322594800260460667, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.西安科技大学 机械工程学院,陕西 西安 710054)]), AuthorCompany(id=1322594800390484098, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, xref=2., ext=[AuthorCompanyExt(id=1322594800394678403, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, companyId=1322594800390484098, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.Shaanxi Coal Group Shenmu Ningtiaota Mining Co., Ltd., Shenmu, Shaanxi 719300, China), AuthorCompanyExt(id=1322594800415649924, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, companyId=1322594800390484098, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.陕煤集团神木柠条塔矿业有限公司,陕西 神木市 719300)]), AuthorCompany(id=1322594800486953093, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, xref=3., ext=[AuthorCompanyExt(id=1322594800491147398, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, companyId=1322594800486953093, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China), AuthorCompanyExt(id=1322594800499536007, tenantId=1146029695717560320, journalId=1225396423026438145, 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journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=图8, caption=井下风流调控装置, figureFileSmall=2qqMYCGcrt1l8UWzsYH4vA==, figureFileBig=g35V0deMU+tTY73FUQW8jg==, tableContent=null), ArticleFig(id=1322594806157652170, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=EN, label=Table 1, caption=

Parameters of boundary conditions

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风筒巷道
入口类型入口速度/(m·s-1)湍流强度/%水力直径/m巷道出口壁面
速度入口8.493.041自由流出地板trap其他reflect
), ArticleFig(id=1322594806291869899, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=表1, caption=

边界条件参数

, figureFileSmall=null, figureFileBig=null, tableContent=
风筒巷道
入口类型入口速度/(m·s-1)湍流强度/%水力直径/m巷道出口壁面
速度入口8.493.041自由流出地板trap其他reflect
), ArticleFig(id=1322594806354784460, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=EN, label=Table 2, caption=

Parameters of discrete phase

, figureFileSmall=null, figureFileBig=null, tableContent=
粉尘材料粒径分布规律质量流率/(kg·s-1)最大粒径/m最小粒径/m中位粒径/m粒径个数
Coal-mvRosin-Rammler0.0081E-041E-066.03E-0510
), ArticleFig(id=1322594806426087629, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=表2, caption=

离散相参数

, figureFileSmall=null, figureFileBig=null, tableContent=
粉尘材料粒径分布规律质量流率/(kg·s-1)最大粒径/m最小粒径/m中位粒径/m粒径个数
Coal-mvRosin-Rammler0.0081E-041E-066.03E-0510
), ArticleFig(id=1322594806493196494, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=EN, label=Table 3, caption=

Attribute sets and segmentation points selection for intelligent air flow control and dust reduction

, figureFileSmall=null, figureFileBig=null, tableContent=
条件属性集候选分割点值
出风口口径大小/m{0.8,1.0,1.2}
出风口水平偏转角/(°){0,5,10,15,20,25}
出风口距迎头距离/m{5,10}
决策属性集候选分割点值确定算法
司机处粉尘质量浓度/(mg·m-3)K-means算法
L0区粉尘质量浓度/(mg·m-3)
L1区粉尘质量浓度/(mg·m-3)
L2区粉尘质量浓度/(mg·m-3)
L3区粉尘质量浓度/(mg·m-3)
), ArticleFig(id=1322594806560305359, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=表3, caption=

风流智能调控降尘属性集及分割点选取

, figureFileSmall=null, figureFileBig=null, tableContent=
条件属性集候选分割点值
出风口口径大小/m{0.8,1.0,1.2}
出风口水平偏转角/(°){0,5,10,15,20,25}
出风口距迎头距离/m{5,10}
决策属性集候选分割点值确定算法
司机处粉尘质量浓度/(mg·m-3)K-means算法
L0区粉尘质量浓度/(mg·m-3)
L1区粉尘质量浓度/(mg·m-3)
L2区粉尘质量浓度/(mg·m-3)
L3区粉尘质量浓度/(mg·m-3)
), ArticleFig(id=1322594806635802832, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=EN, label=Table 4, caption=

Control schemes of air flow and partial sample data

, figureFileSmall=null, figureFileBig=null, tableContent=
方案口径大小/m水平偏转角度/(°)距迎头距离/m司机位置无人隐患区粉尘悬浮区
风速/(m·s-1)粉尘质量浓度/(mg·m-3)风速/(m·s-1)粉尘质量浓度/(mg·m-3)风速/(m·s-1)粉尘质量浓度/(mg·m-3)
10.8050.62238.151.33360.340.75103.95
20.8550.58232.251.55321.180.5887.41
30.81050.61227.861.25228.570.6368.62
40.81550.69192.651.05198.920.8292.56
50.82050.57118.340.85183.350.9493.38
60.82550.55238.260.96384.580.6183.31
71.0050.66246.581.09292.670.6375.86
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
351.220100.83141.280.91204.710.6265.66
361.225100.63138.670.71148.440.8171.49
), ArticleFig(id=1322594806707106001, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=表4, caption=

风流调控方案及部分样本数据

, figureFileSmall=null, figureFileBig=null, tableContent=
方案口径大小/m水平偏转角度/(°)距迎头距离/m司机位置无人隐患区粉尘悬浮区
风速/(m·s-1)粉尘质量浓度/(mg·m-3)风速/(m·s-1)粉尘质量浓度/(mg·m-3)风速/(m·s-1)粉尘质量浓度/(mg·m-3)
10.8050.62238.151.33360.340.75103.95
20.8550.58232.251.55321.180.5887.41
30.81050.61227.861.25228.570.6368.62
40.81550.69192.651.05198.920.8292.56
50.82050.57118.340.85183.350.9493.38
60.82550.55238.260.96384.580.6183.31
71.0050.66246.581.09292.670.6375.86
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
351.220100.83141.280.91204.710.6265.66
361.225100.63138.670.71148.440.8171.49
), ArticleFig(id=1322594806774214866, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=EN, label=Table 5, caption=

Candidate segmentation value of dust mass concentration sample data

, figureFileSmall=null, figureFileBig=null, tableContent=
区段候选分割点值/(mg/m3)
司机位置{126,179,206,243}
无人隐患区{173,286,356,366}
粉尘聚集区{159,221,314,349}
粉尘沉降区{146,186,244,289}
粉尘悬浮区{83,92,106,126}
), ArticleFig(id=1322594806837129428, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=表5, caption=

粉尘质量浓度样本数据候选分割点值

, figureFileSmall=null, figureFileBig=null, tableContent=
区段候选分割点值/(mg/m3)
司机位置{126,179,206,243}
无人隐患区{173,286,356,366}
粉尘聚集区{159,221,314,349}
粉尘沉降区{146,186,244,289}
粉尘悬浮区{83,92,106,126}
), ArticleFig(id=1322594806895849685, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=EN, label=Table 6, caption=

Optimal schemes of intelligent air flow control and dust reduction for different areas of personnel activity

, figureFileSmall=null, figureFileBig=null, tableContent=
不同人员活动区风流智能调控降尘方案
粉尘聚集区(5~15 m)方案1:出风口口径1.2 m、水平偏转5°、距迎头5 m。
粉尘沉降区(15~25 m)方案2:出风口口径1.0 m、水平偏转10°、距迎头10 m。
), ArticleFig(id=1322594806967152854, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=表6, caption=

不同人员活动区最优风流智能调控降尘方案

, figureFileSmall=null, figureFileBig=null, tableContent=
不同人员活动区风流智能调控降尘方案
粉尘聚集区(5~15 m)方案1:出风口口径1.2 m、水平偏转5°、距迎头5 m。
粉尘沉降区(15~25 m)方案2:出风口口径1.0 m、水平偏转10°、距迎头10 m。
), ArticleFig(id=1322594807051038935, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=EN, label=Table 7, caption=

Measured results of dust mass concentration under personnel in dust accumulatation area (5-15 m)

, figureFileSmall=null, figureFileBig=null, tableContent=
测点粉尘质量浓度/(mg·m-3)降尘率/%
应用前应用后
1#196.51127.9634.88
2#183.39115.2437.16
3#168.5498.6241.49
4#172.31109.4836.46
5#151.19101.3432.97
6#167.27114.7331.46
7#129.5898.7423.75
8#102.6282.3619.74
9#179.65101.2743.63
), ArticleFig(id=1322594807130730712, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=表7, caption=

人员处于粉尘聚集区(5~15 m)时的实测粉尘质量浓度

, figureFileSmall=null, figureFileBig=null, tableContent=
测点粉尘质量浓度/(mg·m-3)降尘率/%
应用前应用后
1#196.51127.9634.88
2#183.39115.2437.16
3#168.5498.6241.49
4#172.31109.4836.46
5#151.19101.3432.97
6#167.27114.7331.46
7#129.5898.7423.75
8#102.6282.3619.74
9#179.65101.2743.63
), ArticleFig(id=1322594807197839577, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=EN, label=Table 8, caption=

Measured results of dust mass concentration under personnel in dust deposition area (15 - 25 m)

, figureFileSmall=null, figureFileBig=null, tableContent=
测点粉尘质量浓度/(mg·m-3)降尘率/%
应用前应用后
1#211.65135.9435.77
2#218.34119.6245.21
3#169.54108.6535.53
4#152.9884.1944.97
5#131.1676.2341.88
6#125.6279.3636.83
7#142.3583.4241.40
8#92.6850.2945.74
9#209.41124.6440.48
), ArticleFig(id=1322594807306891482, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322594796061962336, language=CN, label=表8, caption=

人员处于粉尘沉降区(15~25 m)时的实测粉尘质量浓度

, figureFileSmall=null, figureFileBig=null, tableContent=
测点粉尘质量浓度/(mg·m-3)降尘率/%
应用前应用后
1#211.65135.9435.77
2#218.34119.6245.21
3#169.54108.6535.53
4#152.9884.1944.97
5#131.1676.2341.88
6#125.6279.3636.83
7#142.3583.4241.40
8#92.6850.2945.74
9#209.41124.6440.48
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综掘面不同人员活动区降尘风流智能调控研究
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龚晓燕 1 , 李宇杰 1 , 常虎强 2 , 刘壮壮 1 , 陈龙 1 , 付浩然 1 , 王新雨 1 , 王帅 3
矿业研究与开发 | 矿山安全与环保 2025,45(1): 204-211
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矿业研究与开发 |矿山安全与环保 2025 , 45 (1) : 204 -211
综掘面不同人员活动区降尘风流智能调控研究
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龚晓燕(1966—),女,甘肃临洮人,博士,教授,主要从事综掘面通风安全、瓦斯和粉尘防治研究。E-mail:

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龚晓燕(1966—),女,甘肃临洮人,博士,教授,主要从事综掘面通风安全、瓦斯和粉尘防治研究。E-mail:

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龚晓燕1 , 李宇杰1, 常虎强2, 刘壮壮1, 陈龙1, 付浩然1, 王新雨1, 王帅3
作者信息
  • 1.西安科技大学 机械工程学院,陕西 西安 710054
  • 2.陕煤集团神木柠条塔矿业有限公司,陕西 神木市 719300
  • 3.西安科技大学 安全科学与工程学院,陕西 西安 710054
作者简介:

龚晓燕(1966—),女,甘肃临洮人,博士,教授,主要从事综掘面通风安全、瓦斯和粉尘防治研究。E-mail:

Research on Intelligent Control of Air Flow for Dust Reduction in Different Areas of Personnel Activity of Fully Mechanized Heading Face
Xiaoyan GONG1 , Yujie LI1, Huqiang CHANG2, Zhuangzhuang LIU1, Long CHEN1, Haoran FU1, Xinyu WANG1, Shuai WANG3
Affiliations
  • 1.College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
  • 2.Shaanxi Coal Group Shenmu Ningtiaota Mining Co., Ltd., Shenmu, Shaanxi 719300, China
  • 3.College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
出版时间: 2025-01-25
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目前传统的综掘面“通风总量”控制方式不能动态调控降尘,而且降尘过程中没有考虑人员活动区通风环境,造成了工作区粉尘聚集。因此,建立了风流与粉尘气固耦合的有限元计算模型并进行井下验证。根据粉尘运移分布规律及井下人员活动范围,划分了不同的人员活动区。模拟获取不同风流智能调控方案的粉尘质量浓度样本数据,利用K-means算法对数据进行离散化处理。基于免疫遗传算法,将降尘方案作为抗体,不同人员活动区粉尘质量浓度作为抗原,开发了不同人员活动区的风流智能调控降尘方案获取算法。以陕北某矿为研究对象,获得了人员处于粉尘聚集区(5~15 m)的最优降尘方案:出风口口径1.2 m、水平偏转5°、距迎头5 m。对该方案的降尘效果进行测试验证,结果表明:在试验平台应用该方案后,粉尘聚集区的平均粉尘质量浓度由原来的163.77 mg/m3降至91.32 mg/m3,降低了44.24%,司机位置处粉尘质量浓度由原来的161.94 mg/m3降至78.90 mg/m3,降低了51.28%。在井下应用该方案后,粉尘聚集区的平均粉尘质量浓度由原来的182.81 mg/m3降至113.94 mg/m3,降低了37.67%,司机位置处粉尘质量浓度由原来的179.65 mg/m3降至101.27 mg/m3,降低了43.63%。

综掘面  /  风流智能调控  /  人员活动区  /  粉尘质量浓度  /  免疫遗传算法

At present, the traditional total ventilation control method on the fully mechanized heading face fails to realize dynamic control of dust reduction, and the ventilation environment in the area of personnel activity is not considered in the dust reduction process, resulting in dust accumulation. Therefore, a finite element calculation model was established for the coupling of air flow and dust gas-solid, and underground verification was conducted. According to the dust migration and distribution law and the activity range of underground personnel, different areas of personnel activity were divided. The dust mass concentration sample data under different airflow intelligent control schemes were obtained by simulation, and the data were discretized by K-means algorithm. Based on the immune genetic algorithm, the dust reduction scheme was used as the antibody, and the dust mass concentration in different areas of personnel activity was used as the antigen. The acquisition algorithm of the intelligent control of air flow and dust reduction scheme for the different areas of personnel activity were compiled. Taking a mine in northern Shaanxi Province as the research object, the optimal dust reduction scheme for the personnel in the dust accumulation area (in the range of 5−15 m) is 1.2 m outlet aperture, 5° horizontal deflection, and 5 m from the head. The dust reduction effect of the scheme was tested and verified. The result show that, after the application on the experimental platform, the average dust mass concentration in dust accumulation area is reduced from 163.77 mg/m3 to 91.32 mg/m3, which is reduced by 44.24%; the dust concentration at the driver's position is reduced from 161.94 mg/m3 to 78.90 mg/m3, which is reduced by 51.28%. After the application in underground, the average dust mass concentration in dust accumulation area is reduced from 182.81 mg/m3 to 113.94 mg/m3, which is reduced by 37.67%; the dust mass concentration at the driver's position is reduced from 179.65 mg/m3 to 101.27 mg/m3, which is reduced by 43.63%.

Fully mechanized heading face  /  Intelligent control of air flow  /  Area of personnel activity  /  Dust mass concentration  /  Immune genetic algorithm
龚晓燕, 李宇杰, 常虎强, 刘壮壮, 陈龙, 付浩然, 王新雨, 王帅. 综掘面不同人员活动区降尘风流智能调控研究. 矿业研究与开发, 2025 , 45 (1) : 204 -211 .
Xiaoyan GONG, Yujie LI, Huqiang CHANG, Zhuangzhuang LIU, Long CHEN, Haoran FU, Xinyu WANG, Shuai WANG. Research on Intelligent Control of Air Flow for Dust Reduction in Different Areas of Personnel Activity of Fully Mechanized Heading Face[J]. Mining Research and Development, 2025 , 45 (1) : 204 -211 .
目前综掘面智能掘进技术快速发展,但局部通风系统智能化发展相对滞后,降尘效果较差,粉尘爆炸事故发生率及尘肺病发病率居高不下,随着“双碳”目标的提出,矿工职业健康安全问题备受关注[1-2]。传统“通风总量”控制方式不能根据实际需求动态调控风流场,无法有效地降低不同人员活动区粉尘质量浓度[3]。众多学者研究表明,风流分布状态对风速及粉尘场运移规律有明显影响。ZHOU等[4]通过数值模拟研究发现,随着出风口距迎头距离的增加,综掘面风速先增大后减小,然后趋于稳定。李雨成等[5]分析了出风口距迎头距离对风流运动及巷道沿程粉尘分布的影响,确定了压入式通风方式下出风口距迎头的合理距离。王佳硕等[6]分析了不同风筒出风角度下风流和粉尘场的分布,得出不同出风角度对涡流及粉尘分布的影响。黎志等[7]研究了风筒的径向出风夹角对粉尘分布的影响,发现径向角度为15°与30°时降尘效果较好。龚晓燕等[8]、刘壮壮等[9]研究了风筒出风口参数与巷道内风速及粉尘质量浓度的关系,研制了风流智能调控装置,并提出增量式风场调控规则。上述研究都是针对巷道内整体粉尘运移分布规律及降尘效果,忽略了巷道人员活动位置,没有针对不同人员活动区进行精准智能调控降尘。
本研究综合考虑粉尘隐患区及井下人员活动范围,对人员活动区进行划分,基于免疫遗传算法,建立不同人员活动区风流智能调控降尘方案获取方法,并对降尘方案的应用效果进行了测试,以期为研制风筒出风口风流智能调控装置提供理论依据。
风流智能调控装置通过改变风筒出风口距迎头距离、方向和口径[10-11],从而改变风流场状态,优化风流分布,有效稀释巷道粉尘。结合《煤矿安全规程》[12]与井下工作人员分布情况,掘进作业过程中禁止工作人员进入距迎头5 m的范围内,掘进机司机位置距迎头7.5 m,回风侧沿程有其他辅助作业人员。
根据对人员活动区的分析及风流作用下粉尘运移沉降主要在距迎头40 m内[13-14],选取巷道几何模型长40 m,对模型进行简化,忽略巷道内电缆等物件的影响,并采用四面体非结构化网格划分模型,建立风流调控下粉尘场计算有限元模型,如图1所示。
采用Euler-Lagrange方法对风流调控下粉尘质量浓度分布规律进行求解[15]。风流属于连续相,采用Realizable k-ε湍流模型求解计算。粉尘颗粒属于离散相,采用离散相DPM模型求解计算。计算模型边界条件及离散相参数设置分别见表1和表2。
使用CCZ-1000粉尘检测仪在井下实测粉尘质量浓度。选取回风侧沿程人员呼吸带高度,对粉尘质量浓度实测数据和模拟结果进行对比分析,如图2所示。模拟参数简化后,模拟值与实测值变化趋势基本保持一致,误差在5%以内,验证了风流调控下粉尘场计算有限元模型的准确性。
利用建立的粉尘场计算有限元模型,模拟分析风筒出风口距迎头最近5 m和最远10 m时,行人呼吸带高度1.5 m及司机位置呼吸带高度2.0 m截面的粉尘质量浓度分布,如图3所示。
在掘进作业过程中,迎头产生的粉尘大部分在风流的作用下向后运移扩散,司机位置附近属于高粉尘质量浓度区域。由于截割臂及掘进机机身的影响,回风侧靠近迎头处风流处于紊流状态,不利于稀释粉尘,粉尘聚集严重。在风流的作用下粉尘向后运移扩散,回风侧沿程粉尘质量浓度较高,在扩散过程中部分粉尘开始沉降,随着距迎头距离的增加,粉尘质量浓度逐渐降低。结合对井下作业人员分布情况及粉尘质量浓度分布规律,将巷道划分为无人隐患区及不同人员活动区。风流智能调控降尘系统及不同人员活动区的划分如图4所示。
按照《煤矿安全规程》规定,距迎头0~5 m为无人区,但粉尘聚集严重,将该区域划分为无人隐患区L0; 5~40 m为不同人员活动区,5~15 m的范围内粉尘聚集严重,将该区域划分为粉尘聚集区L1;15~25 m的范围内粉尘逐渐沉降,但粉尘质量浓度仍然较高,将该区域划分为粉尘沉降区L2;25~40 m的范围内粉尘基本完成沉降,但部分小颗粒粉尘在巷道中悬浮,将该区域划分为粉尘悬浮区L3。
风流智能调控降尘的原始条件包括掘进方式、煤质指标、巷道断面尺寸、通风方式和风筒布局位置等。本文通过改变风筒出风口参数优化风场,包括出风口距迎头的距离、口径大小和水平偏转角度等风流智能调控降尘条件参数[16]。将不同人员活动区的粉尘质量浓度作为风流智能调控降尘的决策目标。
根据巷道实际工况,利用Fluent建立粉尘场计算有限元模型,设计数值模拟试验方案并进行模拟计算,以风速符合《煤矿安全规程》为约束,删除不符合规范的数据,获取不同人员活动区的样本数据。对风流智能调控参数及粉尘质量浓度连续性数据进行离散化处理。
基于免疫遗传算法较强的全局搜索能力和适应性,将降尘方案作为抗体,不同人员活动区粉尘质量浓度作为目标函数的抗原。基于抗体适应度值对交叉和变异概率进行调整,通过计算抗体之间的亲和度对其进行促进或抑制,选取优秀抗体对记忆库进行更新。通过遗传操作产生新抗体,将记忆库中适应度值最大的抗体作为最优降尘方案。利用Matlab开发风流智能调控降尘方案获取算法的参数化程序,获取最优降尘方案,并结合风流智能调控装置,对降尘方案进行应用表达。
以降低不同人员活动区域的粉尘质量浓度为目标,初步设计获取降尘方案的免疫遗传算法目标函数F(x)为:
式中,d1为无人隐患区粉尘质量浓度,mg/m3;d2为司机位置处粉尘质量浓度,mg/m3;d3为不同人员活动区粉尘质量浓度,mg/m3;下标max和min分别表示最大值和最小值。
粉尘质量浓度越低,适应度值越大,个体越优秀,故取适应度函数为目标函数的倒数,则适应度函数f(x)为:
根据交叉概率Pc和变异概率Pm的特点,引入一种自适应调整Pc和Pm的方法[17]。确保不断靠近最优解区域进行搜索,并避免陷入局部最优解而影响计算效率,构造自适应调整交叉和变异概率公式为:
式中:fmax为种群中适应度的最大值;fm为参与交叉的两个染色体中较大的适应度值;favg为种群中适应度的平均值;f为参加变异染色体的适应度值。为避免保存下来的较优个体在种群中呈指数级快速繁殖而导致早熟,取k1=1.0,k3=1.0;k2=0.5,k4=0.5。
根据风流智能调控装置调控参数范围:出风口距迎头距离为5~10 m、出风口口径为0.8~1.2 m及出风口水平偏转角为0~25°,对风流智能调控参数进行等分离散,采用K-means算法[18]对粉尘质量浓度连续性数据进行离散化处理,对预处理后的样本数据进行二进制编码。风流智能调控降尘属性集及候选分割点值见表3。
通过计算抗体之间的亲和度对其进行促进或抑制。选取适应度值大小排名前10的个体对记忆库进行更新,设定迭代次数为10,通过遗传操作产生新一代抗体,达到最大迭代次数后将记忆库中适应度值最大的抗体作为最优降尘方案。具体实现步骤如下。
步骤1:风流智能调控降尘样本数据获取。获取风流调控方案及风速、粉尘质量浓度样本数据。
步骤2:样本数据预处理。以风速符合《煤矿安全规程》为约束,对调控参数进行等分离散,采用K-means算法对粉尘质量浓度连续性数据进行离散化和合并处理,并进行二进制编码。
步骤3:算法初始化。设定算法参数,包括抗体种群规模、迭代次数、初始化交叉概率及变异概率。
步骤4:抗原识别及初始种群产生。以降低不同人员活动区的粉尘质量浓度为目标,将初始样本数据处理后得到的抗体组成初始种群。
步骤5:计算适应度值。对种群中每个抗体的适应度、支持度及置信度值进行计算。
步骤6:基于抗体适应度值对交叉及变异概率进行自适应调整,增加种群多样性,提高算法搜索效率。
步骤7:记忆库更新。通过遗传操作,删除适应度值较低的抗体,实现种群的更新。
步骤8:风流智能调控降尘方案获取。判断是否满足终止条件,若满足将记忆库中适应度值最大的抗体进行解码,作为最优风流智能调控降尘方案,否则返回步骤5。
利用Matlab开发风流智能调控降尘方案获取的免疫遗传算法参数化程序。设定初始种群大小为20,迭代次数为10,初始交叉概率为0.7,初始变异概率为0.01。根据抗体适应度值对交叉和变异概率进行自适应调整。
以陕北某矿综掘面为研究对象,巷道宽5.9 m、高4.4 m,采用压入式通风方式,风筒直径为1 m,出风口距迎头5~10 m。根据风流智能调控装置调控范围,设计风流调控方案,结合巷道实际数据,利用建立的风流调控下粉尘场计算有限元模型,获取不同调控降尘方案下各区段的风速及粉尘质量浓度样本数据。各区段分别是:无人隐患区L0(X=5 m,Y=1.5 m,Z=0~5 m)、粉尘聚集区L1(X=5 m,Y=1.5 m,Z=5~15 m)、粉尘沉降区L2(X=5 m,Y=1.5 m,Z=15~25 m)、粉尘悬浮区L3(X=5 m,Y=1.5 m,Z=25~40 m),以及司机位置处(X=2.5 m,Y=2 m,Z=7.5 m)。部分样本数据见表4。
以0.25~4 m/s的风速为约束条件对样本数据进行预处理,剔除不符合规范的数据。采用K-means算法对样本数据中的粉尘质量浓度进行离散化处理,粉尘质量浓度样本数据候选分割点值见表5。
将初始样本数据处理后获得的抗体共同组成初始种群,在风速符合规范的前提下,以降低不同人员活动区的粉尘质量浓度为目标。将设计目标函数的倒数作为适应度函数,交叉概率和变异概率基于适应度值进行自适应调节,选取适应度值大小排名前10的个体对记忆库进行更新,设定迭代次数为10。通过免疫遗传算法操作,选取适应度值最大的个体作为风流智能调控降尘方案。以人员处于粉尘聚集区(5~15 m)及粉尘沉降区(15~25 m)为例,获取了最优风流智能调控降尘方案(见表6)。
对获取的不同人员活动区最优风流智能调控降尘方案分别进行试验平台及井下测试验证,对方案应用后的降尘效果进行分析。
基于掘进面风流监测及智能调控测试平台[19],根据不同人员活动区风流智能调控降尘试验的测试需求,依据相似模拟理论,按1:5的比例搭建试验平台,如图5所示。
试验平台传感器测点布置如图6所示,1#、2#、3#为粉尘聚集区测点,4#、5#为粉尘沉降区测点,6#、7#、8#为粉尘悬浮区测点,9#为司机位置处测点。
通过试验平台获取降尘方案应用前后各测点的粉尘质量浓度并进行对比分析,如图7所示。由图7可知,人员处于粉尘聚集区(5~15 m)时,降尘方案1应用后该区平均粉尘质量浓度由原来的163.77 mg/m3降至91.32 mg/m3,降低了44.24%,司机位置处粉尘质量浓度由原来的161.94 mg/m3降至78.90 mg/m3,降低了51.28%。人员处于粉尘沉降区(15~25 m)时,应用后降尘方案2该区平均粉尘质量浓度由原来的118.95 mg/m3降至61.41 mg/m3,降低了48.37%,司机位置处粉尘质量浓度由原来的204.34 mg/m3降至115.17 mg/m3,降低了43.64%。
对不同人员活动区最优风流智能降尘方案的应用效果进行井下实测,井下风流调控装置安装如图8所示。方案应用前后各测点的粉尘质量浓度见表7和表8。由表7可知,人员处于粉尘聚集区(5~15 m)时,降尘方案1应用后该区平均粉尘质量浓度由原来的182.81 mg/m3降至113.94 mg/m3,降低了37.67%,司机位置处粉尘质量浓度由原来的179.65 mg/m3降至101.27 mg/m3,降低了43.63%。由表8可知,人员处于粉尘沉降区(15~25 m)时,降尘方案2应用后该区平均粉尘质量浓度由原来的142.07 mg/m3降至80.21 mg/m3,降低了43.54%,司机位置处粉尘质量浓度由原来的209.41 mg/m3降至124.64 mg/m3,降低了40.48%。
(1)建立了风流与粉尘气固耦合的有限元模型,并进行了井下测试验证。对粉尘运移分布规律进行了分析,将巷道划分为:0~5 m为无人隐患区,5~40 m为不同人员活动区,其中5~15 m为粉尘聚集区,15~25 m为粉尘沉降区,25~40 m为粉尘悬浮区。
(2)基于免疫遗传算法建立了不同人员活动区的风流智能调控降尘方案获取方法,开发了相应的参数化程序。以陕北某矿为研究对象,利用该参数化程序获取了不同人员活动区的最优风流智能调控降尘方案。人员处于粉尘聚集区(5~15 m)时的降尘方案为:出风口口径1.2 m、水平偏转5°、距迎头5 m。人员处于粉尘沉降区(15~25 m)时的降尘方案为:出风口口径1.0 m、水平偏转10°、距迎头10 m。
(3)对最优方案的降尘效果进行试验平台测试验证,结果表明:人员处于粉尘聚集区(5~15 m)时,降尘优化方案应用后,该区平均粉尘质量浓度降低了44.24%,司机位置处粉尘质量浓度降低了51.28%;人员处于粉尘沉降区(15~25 m)时,降尘优化方案应用后,该区平均粉尘质量浓度降低了48.37%,司机位置处粉尘质量浓度降低了43.64%。
(4)对最优方案的降尘效果进行井下实测,结果表明:人员处于粉尘聚集区(5~15 m)时,降尘优化方案应用后,该区平均粉尘质量浓度降低了37.67%,司机位置处粉尘质量浓度降低了43.63%;人员处于粉尘沉降区(15~25 m)时,降尘优化方案应用后该区平均粉尘质量浓度降低了43.54%,司机位置处粉尘质量浓度降低了40.48%。
  • 国家自然科学基金面上项目(52374226)
  • 陕西省自然科学基础研究计划(企业陕煤联合基金项目)(2021JLM-01)
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2025年第45卷第1期
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  • 接收时间:2024-02-28
  • 首发时间:2026-10-29
  • 出版时间:2025-01-25
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  • 收稿日期:2024-02-28
基金
国家自然科学基金面上项目(52374226)
陕西省自然科学基础研究计划(企业陕煤联合基金项目)(2021JLM-01)
作者信息
    1.西安科技大学 机械工程学院,陕西 西安 710054
    2.陕煤集团神木柠条塔矿业有限公司,陕西 神木市 719300
    3.西安科技大学 安全科学与工程学院,陕西 西安 710054
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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