|本期目录/Table of Contents|

[1]何帅印,黄婧,李佳林,等.基于电火花发射光谱的矿尘元素快速检测与分类算法研究*[J].中国安全生产科学技术,2025,21(4):118-124.[doi:10.11731/j.issn.1673-193x.2025.04.016]
　HE Shuaiyin,HUANG Jing,LI Jialin,et al.Research on fast detection and classification algorithm of mineral dust elements based on spark emission spectroscopy[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2025,21(4):118-124.[doi:10.11731/j.issn.1673-193x.2025.04.016]
点击复制

基于电火花发射光谱的矿尘元素快速检测与分类算法研究*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
21
期数:
2025年4期
页码:
118-124
栏目:
职业安全卫生管理与技术
出版日期:
2025-04-30

文章信息/Info

Title:
Research on fast detection and classification algorithm of mineral dust elements based on spark emission spectroscopy
文章编号:
1673-193X(2025)-04-0118-07
作者:
何帅印; 黄婧; 李佳林; 郑丽娜
（1.国家能源神东煤炭集团有限责任公司，陕西 神木 719300；
2.中国矿业大学 安全工程学院，江苏 徐州 221000）
Author(s):
HE Shuaiyin;  HUANG Jing;  LI Jialin;  ZHENG Li’na
(1.National Energy Shendong Coal Group Co.,Ltd.,Shenmu Shaanxi 719300,China;
2.School of Safety Engineering,China University of Mining and Technology,Xuzhou Jiangsu 221000,China)
关键词:
矿尘; 电火花发射光谱; 特征谱线; 定量分析
Keywords:
mineral dust;  spark emission spectroscopy;  characteristic spectral line;  quantitative analysis
分类号:
X964
DOI:
10.11731/j.issn.1673-193x.2025.04.016
文献标志码:
A
摘要:
为解决矿尘成分检测技术耗时较长和设备昂贵问题，提出1种电火花发射光谱(SES)新技术，用于现场快速检测矿尘成分。通过SES设备，采集锰、铝、石灰石、硅和煤5种矿尘的光谱数据，分析各矿尘元素特征峰值，建立石灰石矿尘中Ca元素的校准曲线，研究SES法对矿尘的分类效果。研究结果表明：Mn，Al，Ca和Si元素的最强谱线分别位于344.2 nm，309.27 nm，317.93 nm和288.16 nm，而煤尘中Ca，Al，Mg，Si等元素的谱线也清晰可见。石灰石矿尘中Ca元素在442.43 nm处的校准曲线决定系数R2为0.98，检测限为16 ppm。PCA分类结果显示，模型能以95%的方差解释率准确区分各类矿尘，外部验证中，锰、铝、煤尘的分类准确率达到100%。研究结果可为矿尘检测提供1种高效、低成本的新方法参考。
Abstract:
To solve the problems of long time consumption and expensive equipment for detecting the mineral dust components,a new technology of spark emission spectroscopy (SES) for rapid on-site detection of mineral dust components was proposed.The spectral data of five types of mineral dust,namely manganese,aluminum,limestone,silicon,and coal,were collected using SES equipment.The characteristic peak values of each mineral dust element were analyzed,and a calibration curve for Ca element in limestone dust was established.The classification effect of SES method on mineral dust was studied.The results show that the strongest spectral lines of Mn,Al,Ca,and Si elements are located at 344.2 nm,309.27 nm,317.93 nm,and 288.16 nm,respectively,while the spectral lines of Ca,Al,Mg,Si,and other elements in coal dust are also clearly visible.The determination coefficient R2 of calibration curve for Ca element in limestone dust at 442.43 nm is 0.98,and the detection limit is 16 ppm.The PCA classification results show that the model can accurately distinguish various types of mineral dust with a variance explanation rate of 95%.In external validation,the classification accuracy of manganese,aluminum,and coal dust reaches 100%.The research results can provide an efficient and low-cost new method for the mineral dusts detection.

参考文献/References:

［1］豆晨瑜.矿井粉尘的危害及其防治［J］.数码世界,2017,143(9):113. DOU Chenyu.The harm and prevention of mine dust［J］.Digital World,2017,143(9):113.
［2］雷雯,陈志峰.浅析近十年我国矿井粉尘防治技术的发展［J］.矿业装备,2023(7):146-148. LEI Wen,CHEN Zhifeng.Analysis of thedevelopment of mine dust prevention and control technology in China in the last decade［J］.Mining Equipment,2023(7):146-148.
［3］翟辉,宋钰.井下作业场所粉尘危害评价及防护对策分析［J］.陕西煤炭,2019,38(3):165-168. ZHAI Hui,SONG Yu.Evaluation ofdust hazards and analysis of protective measures in underground workplace［J］.Shaanxi Coal,2019,38(3):165-168.
［4］李刚,周哲,胡锦华.基于知识图谱的我国矿井粉尘防治技术研究进展与展望［J］.金属矿山,2023(7):28-39. LI Gang,ZHOU Zhe,HU Jinhua.Research progress and prospects of mine dust prevention and control technology in China based on knowledge graph［J］.Metal Mines,2023(7):28-39.
［5］白如鸿.浅谈煤尘肺病的危害及预防措施［J］.中国煤炭工业,2016,356(10):68-69. BAI Ruhong.A brief discussion on the hazards and preventive measures of coal dust pneumonia［J］.China Coal Industry,2016,356(10):68-69.
［6］汪利民,周敏,陈志亮,等.工作场所空气中铁的火焰原子吸收光谱测定方法［J］.工业卫生与职业病,2017,43(4):312-314,316. WANG Limin,ZHOU Min,CHEN Zhiliang,et al.Determination of iron in workplace air by flame atomic absorption spectroscopy［J］.Industrial Health and Occupational Disease,2017,43(4):312-314,316.
［7］MOSTERT M M R.The determination of phosphate in rocks and iron ores using inductively coupled plasma with optical emission spectroscopy (ICP-OES)［J］.Geochemistry:Exploration,Environment,Analysis,2019,19(4):465-473.
［8］LIU Y,XUE D,LI W,et al.A simple method for the precise determination of multi-elements in pyrite and magnetite by ICP-MS and ICP-OES with matrix removal［J］.Microchemical Journal,2020,158:105221.
［9］SNDERGAARD J,JRGENSEN C J.Field portable X-ray fluorescence (pXRF) spectrometry for chemical dust source characterization:investigations of natural and mining-related dust sources in Greenland (Kangerlussuaq Area)［J］.Water,Air,& Soil Pollution,2021,232:1-17.
［10］RINCHEVAL M,COHEN D R,HEMMINGS F A.Biogeochemical mapping of metal contamination from mine tailings using field-portable XRF［J］.Science of the Total Environment,2019,662:404-413.
［11］HUSSAIN T,GONDAL A M.Laserinduced breakdown spectroscopy (LIBS) as a rapid tool for material analysis［J］.Journal of Physics:Conference Series,2013,439(1):012050-012050.
［12］SRUNGARAM P K,AYYALASOMAYAJULA K K,YU-YUEH F,et al.Comparison of laser induced breakdown spectroscopy and spark induced breakdown spectroscopy for determination of mercury in soils［J］.Spectrochimica Acta Part B:Atomic Spectroscopy,2013,87:108-113.
［13］LI H,MAZZEI L,WALLIS C D,et al.Improving quantitative analysis of spark-induced breakdown spectroscopy:multivariate calibration of metal particles using machine learning［J］.Journal of Aerosol Science,2022,159:105874.
［14］ZHENG L,KULKARNI P,BIRCH M E,et al.Near real-time measurement of carbonaceous aerosol using microplasma spectroscopy:application to measurement of carbon nanomaterials［J］.Aerosol Science and Technology,2016,50(11):1155-1166.
［15］GAO J,KANG J,LI R,et al.Application of calibration-free high repetition rate laser-ablation spark-induced breakdown spectroscopy for the quantitative elemental analysis of a silver alloy［J］.Applied Optics,2020,59(13):4091-4096.
［16］蒋晶晶,金肖克,李伟松,等.基于红外光谱PCA-LDA统计分析的麻纤维鉴别研究［J］.丝绸,2024,61(7):102-108. JIANG Jingjing,JIN Xiaoke,LI Weisong,et al.Research on identification of hemp fibers based on infrared spectroscopy PCA-LDA statistical analysis［J］.Silk,2024,61(7):102-108.
［17］刘文艳,项洋,卞希慧,等.近红外光谱结合SIMCA算法对舍饲和放养牦牛的鉴别研究［J］.青海畜牧兽医杂志,2024,54(1):8-13. LIU Wenyan,XIANG Yang,BIAN Xihui,et al.Study onthe identification of domestic and free range yaks using near-infrared spectroscopy combined with SIMCA algorithm［J］.Qinghai Journal of Animal Husbandry and Veterinary Medicine,2024,54(1):8-13.
［18］刘娟,刘玉柱,储晨曦，等.利用激光诱导击穿光谱原位在线检测褐煤及煤烟［J］.光谱学与光谱分析,2021,41(3):954-960. LIU Juan,LIU Yuzhu,CHU Chenxi,et al.In situ online detection of lignite and coal smoke using laser induced breakdown spectroscopy［J］.Spectroscopy and Spectral Analysis,2021,41 (3):954-960.
［19］HOU JIAJIA,ZHANG LEI,ZHAO YANG.Mechanisms and efficient elimination approaches of self-absorption in LIBS［J］.Plasma Science and Technology,2019,21(3):1-15.
［20］赵彦.基于激光诱导击穿光谱的煤质灰分测定方法研究［D］.太原：太原理工大学,2014.
［21］HE X,YANG Q,LING D,et al.Elemental analysis of V,Mo,Cr,Mn,Al,Ni,and Cu in steel alloy with femtosecond laser ablation spark-induced breakdown spectroscopy［J］.Chemosensors,2022,10(9):370.
［22］JIANG Y,LI R,CHEN Y.Elemental analysis of copper alloys with laser-ablation spark-induced breakdown spectroscopy based on a fiber laser operated at 30 kHz pulse repetition rate［J］.Journal of Analytical Atomic Spectrometry,2019,34(9):1838-1845.
［23］SUN D,MA Y,WANG Y,et al.Determination of the limits of detection for aluminum-based alloys by spatially resolved single-and double-pulse laser-induced breakdown spectroscopy［J］.Analytical Methods,2018,10(22):2595-2603.

相似文献/References:

[1]甘建,仲莎莎,朱小龙,等.基于糖苷-阴非离子表面活性剂协同效应的抑尘泡沫特性研究*[J].中国安全生产科学技术,2024,20(2):169.[doi:10.11731/j.issn.1673-193x.2024.02.023]
　GAN Jian,ZHONG Shasha,ZHU Xiaolong,et al.Study on characteristics of dust suppression foam based on synergistic effect of glycoside-anionic and nonionic surfactants[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(4):169.[doi:10.11731/j.issn.1673-193x.2024.02.023]

备注/Memo

备注/Memo:
收稿日期： 2024-05-20
* 基金项目： 中国神华能源股份有限公司神东煤炭分公司科技创新项目(E21010028501)；国家自然科学基金项目(52074274)；中央高校基本科研业务费专项资金项目(2021YCPY0107)
作者简介： 何帅印，硕士，工程师，主要研究方向为煤矿掘进面粉尘治理。
通信作者： 郑丽娜，博士，教授，主要研究方向为工作场所粉尘暴露监测、职业健康。
更新日期/Last Update: 2025-04-28