Cloud model-clustering analysis based evaluation for ventilation system of underground metal mine in alpine region

Abstract

Ventilation system is significant in underground metal mine of alpine region. Reasonable evaluation of ventilation effectiveness will lead to a practical improvement for the maintenance and management of ventilation system. However, it is difficult to make an effective evaluation of ventilation system due to the lack of classification criteria with respect to underground metal mine in alpine region. This paper proposes a novel evaluation method called the cloud model-clustering analysis (CMCA). Cloud model (CM) is utilized to process collected data of ventilation system, and they are converted into cloud descriptors by CM. Cloud similarity (CS) based Euclidean distance (ED) is proposed to make clustering analysis of assessed samples. Then the classification of assessed samples will be identified by clustering analysis results. A case study is developed based on CMCA. Evaluation results show that ventilation effectiveness can be well classified. Moreover, CM is used alone to make comparison of evaluation results obtained by CMCA. Then the availability and validity of CMCA is verified. Meanwhile, difference of CS based ED and classical ED is analyzed. Two new clustering analysis methods are introduced to make comparison with CMCA. Then the ability of proposed CMCA to meet evaluation requirements of ventilation system is verified.

Keywords

ventilation system / underground metal mine / alpine region / cloud model / cloud similarity / Euclidean distance

References

[1] LiZ, ShaoS, ShiX P, SunY P, ZhangX L. Structural transformation of manufacturing, natural resource dependence, and carbon emissions reduction: Evidence of a threshold from China [J]. J Clean Prod, 2019, 206: 920-927

[2] YaoX W, ZhouZ C, LiJ S, ZhangB H, ZhouH D, XuK L. Experimental investigation of physicochemical and slagging characteristics of inorganic constituents in ash residues from gasification of different herbaceous biomass [J]. Energy, 2020, 198: 1-14

[3] DongL J, DengS J, WangF Y. Some developments and new insights for environmental sustainability and disaster control of tailings dam [J]. J Clean Prod, 2020, 269: 122270

[4] QianD W, YanC Z, XingZ P, XiuL N. Monitoring coal mine changes and their impact on landscape patterns in an alpine region: A case study of the Muli coal mine in the Qinghai-Tibet Plateau [J]. Environ Monit Assess, 2017, 189(11): 1-13

[5] WidiatmojoA, SasakiK, SugaiY, SuzukiY, TanakaH, UchidaK, MatsumotoH. Assessment of air dispersion characteristic in underground mine ventilation: Field measurement and numerical evaluation [J]. Process Saf Environ Protect, 2015, 93173-181

[6] TANG Z X, YANG P. Research on oxygen-increasing ventilation in alpine region [C]//3rd International Symposium on Modern Mining & Safety Technology. Fuxin, China, 2008.

[7] ZhangQ, ZhouG, QianX M, YuanM Q, SunY L, WangD. Diffuse pollution characteristics of respirable dust in fully-mechanized mining face under various velocities based on CFD investigation [J]. J Clean Prod, 2018, 184: 239-250

[8] DongL J, SunD Y, HanG J, LiX B, HuQ C, ShuL. Velocity-free localization of autonomous driverless vehicles in underground intelligent mines [J]. IEEE Trans Veh Technol, 2020, 69(9): 9292-9303

[9] DongL J, HuQ C, TongX J, LiuY F. Velocity-free MS/AE source location method for Three-dimensional hole-containing structures [J]. Engineering, 2020, 6(7): 827-834

[10] NIE X X, WEI X B, LI X C, LU C W. Heat treatment and ventilation optimization in a deep mine [J]. Adv Civ Eng, 2018: 1–12. DOI: https://doi.org/10.1155/2018/1529490.

[11] GhoshdastidarA J, HuZ Z, NazarenkoY, AriyaP A. Exposure to nanoscale and microscale particulate air pollution prior to mining development near a northern indigenous community in Quebec, Canada [J]. Environ Sci Pollut Res, 2018, 25(9): 8976-8988

[12] CaoR H, CaoP, LinH. A kind of control technology for squeezing failure in deep roadways: A case study [J]. Geomat Nat Hazards Risk, 2017, 8(2): 1715-1729

[13] ChengJ W, YangS Q. Data mining applications in evaluating mine ventilation system [J]. Safety Sci, 2017, 50(4): 918-922

[14] HuangJ X, LiuH Q, WangH Q. Mine ventilation safety evaluation based on artificial neural network-fuzzy control theory [J]. Oxid Commun, 2016, 39(2A): 2026-2033

[15] LeeD K. Optimal design of mine ventilation system using a ventilation improvement index [J]. J Min Sci, 2016, 52(4): 762-777

[16] NelA J H, VoslooJ C, MathewsM J. Evaluating complex mine ventilation operational changes through simulations [J]. J Energy South Afr, 2018, 29(3): 22-32

[17] LiangW Z, ZhaoG Y, LuoS Z. Selecting the optimal mine ventilation system via a decision making framework under hesitant [J]. Symmetry-Basel, 2018, 10(7): 283

[18] ZhangL M, WuX G, DingL Y, SkibniewskiM J. A novel model for risk assessment of adjacent buildings in tunneling environments [J]. Build Environ, 2013, 65: 185-194

[19] LiY X, QiL H, SongY S, HouX H, LiH J. Quantitative characterization of carbon/carbon composites matrix texture based on image analysis using polarized light microscope [J]. Microsc Res Tech, 2015, 78(10): 908-917

[20] WangD, LiuD F, DingH, SinghV P, WangY K, ZengX K, WuJ C, WangL C. A cloud model-based approach for water quality assessment [J]. Environ Res, 2016, 14824-35

[21] LiuZ B, ShaoJ F, XuW Y, XuF. Comprehensive stability evaluation of rock slope using the cloud model-based approach [J]. Rock Mech Rock Eng, 2014, 47(6): 2239-2252

[22] ZhangW J, LiuS L, SunB, LiuY, PechtM. A CM-based method for the analysis of accelerated life test data [J]. Microelectron Reliab, 2015, 55(1): 123-128

[23] YanF, XuK L. A set pair analysis based layer of protection analysis and its application in quantitative risk assessment [J]. J Loss Prev Process Ind, 2018, 55: 313-319

[24] YanF, XuK L. Methodology and case study of quantitative preliminary hazard analysis based on cloud model [J]. J Loss Prev Process Ind, 2109, 60: 116-124

[25] XuQ W, XuK L, YaoX W. Safety assessment of petrochemical enterprise using the cloud model, PHA-LOPA and the bow-tie model [J]. R Soc Open Sci, 2018, 5: 2239-2252

[26] LiD Y, LiuC Y, GanW Y. A new cognitive model: Cloud model [J]. Int J Intell Syst, 2019, 24(3): 357-375

[27] ZangT L, WangY, HeZ Y, QianQ Q. Harmonic pollution level assessment in distribution system using extended cloud similarity measurement method [M]. Communications in Computer and Information Science, 2017, Singapore, Springer Singapore, 388400

[28] XuQ W, XuK L. Quality evaluation of Chinese red wine based on cloud model [J]. J Food Biochem, 2019, 43(10): e12787

[29] LuB B, CharltonM, BrunsdonC, HarrisP. The Minkowski approach for choosing the distance metric in geographically weighted regression [J]. Int J Geogr Inf Sci, 2016, 302351-368

[30] XianS D, SunW J, XuS H, GaoY Y. Fuzzy linguistic induced OWA Minkowski distance operator and its application in group decision making [J]. Pattern Anal Appl, 2016, 19(2): 325-335

[31] KumarV, CardiffB, FlanaganM F. User-antenna selection for physical-layer network coding based on Euclidean distance [J]. IEEE Trans Commun, 2019, 67(5): 3363-3375

[32] MontechiesiL, CocconcelliM, RubiniR. Artificial immune system via Euclidean distance minimization for anomaly detection in bearings [J]. Mech Syst Signal Proc, 2016, 76–77: 380-393

[33] GomezD, HernandezL A, YaborL, BeemsterG T S, TebbeC C, PapenbrockJ, LorenzoJ C. Euclidean distance can identify the mannitol level that produces the most remarkable integral effect on sugarcane micropropagation in temporary immersion bioreactors [J]. J Plant Res, 2018, 131(4): 719-724

[34] YanF, XuK L, LiD S, CuiZ K. A novel hazard assessment method for biomass gasification stations based on extended set pair analysis [J]. PLoS One, 2017, 12(9): e0185006

[35] LiD Y, DuYArtificial intelligence with uncertainty [M], 2017, Boca Raton, Chapman & Hall/CRC

[36] WangJ Q, PengJ J, ZhangH Y, LiuT, ChenX H. An uncertain linguistic multi-criteria group decision-making method based on a cloud model [J]. Group Decis Negot, 2015, 241171-192

[37] WangJ Q, LuP, ZhangH Y, ChenX H. Method of multi-criteria group decision-making based on cloud aggregation operators with linguistic information [J]. Inf Sci, 2014, 274: 177-191

[38] YanF, JinC, LiZ J, CaoR H, XuK L. Research and development of field theory-based three-dimensional risk assessment Part I: Optimization of risk reduction [J]. Saf Sci, 2019, 120: 312-322

[39] ZhangJ J, XuK L, ReniersG, YouG. Statistical analysis the characteristics of extraordinarily severe coal mine accidents (ESCMAs) in China from 1950 to 2018 [J]. Process Saf Environ Protect, 2020, 133: 332-340

[40] YangP, LvW SResearch on ventilation and dust control technology of deep underground mine in alpine region [M], 2012, Beijing, Metallurgical Industry Press(in Chinese)

[41] GB 16423-2006. Safety regulations for metal and nonmetal mines [S]. (in Chinese)

[42] AQ 2013–2008. Ventilation technical standards for metal and nonmetal underground mines [S]. (in Chinese)

[43] GBZ/T 192.2-2007. Determination of dust in the air of workplace, Part 2: Respirable dust concentration [S]. (in Chinese)

[44] LiC H, SunL, JiaJ X, CaiY P, WangX. Risk assessment of water pollution sources based on an integrated k-means clustering and set pair analysis method in the region of Shiyan, China [J]. Sci Total Environ, 2016, 557: 307-316

[45] RodriguesE O. Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier [J]. Pattern Recognit Lett, 2018, 110: 66-71

[46] WangB, WuC, KangL G, ReniersG, HuangL. Work safety in China’s Thirteenth Five-Year plan period (2016–2020): Current status, new challenges and future tasks [J]. Saf Sci, 2018, 104: 164-178

[47] GeJ, XuK L, ZhengX, YaoX W, XuQ W, ZhangB H. The main challenges of safety science [J]. Saf Sci, 2019, 118: 119-125