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黄金科学技术 ›› 2019, Vol. 27 ›› Issue (5): 740-746.doi: 10.11872/j.issn.1005-2518.2019.05.740

• 采选技术与矿山管理 • 上一篇    

基于改进PCA与有序多分类Logistic的充填管道磨损风险评估

王石(),汤艺(),冯萧   

  1. 江西理工大学资源与环境工程学院,江西 赣州 341000
  • 收稿日期:2018-08-14 修回日期:2018-12-11 出版日期:2019-10-31 发布日期:2019-11-07
  • 通讯作者: 汤艺 E-mail:stonersxx@126.com;tylwtfglsm@163.com
  • 作者简介:王石(1987-),男,河南济源人,讲师,从事采矿工艺与充填技术研究工作。stonersxx@126.com
  • 基金资助:
    国家自然科学基金项目“APAM强化絮网结构后全尾砂料浆流动性能演化机制研究”(51804134);国家自然科学基金项目“渗流—蠕变耦合作用下全尾砂胶结充填体力学性能演化规律及损伤破坏机制”(51804135);江西省自然科学基金项目“远距离输送过程中添加絮凝剂的高浓度全尾砂浆颗粒分散机理研究”(20181BAB216013);江西理工大学博士启动基金项目“阴离子型聚丙烯酰胺对似膏体管道输送稳定性的影响机理研究工作”(jxxjbs17011)

Risk Assessment of Filling Pipeline Wearing Based on Improved PCA and Ordered Multi-class Logistic

Shi WANG(),Yi TANG(),Xiao FENG   

  1. School of Resources and Environmental Engineering,Ganzhou 341000,Jiangxi,China
  • Received:2018-08-14 Revised:2018-12-11 Online:2019-10-31 Published:2019-11-07
  • Contact: Yi TANG E-mail:stonersxx@126.com;tylwtfglsm@163.com

摘要:

为准确预测矿山充填管道磨损风险,建立改进PCA与有序多分类Logistic回归组合的充填管道磨损风险评估模型。结合实际经验,选取12项指标(9项定量指标和3项定性指标)建立评估模型。依据改进PCA算法,筛除影响力指数小的充填料浆密度和充填料浆腐蚀性2项指标,将优选出的主要指标代入有序多分类Logistic回归模型,依照相应概率大小进行风险性等级判定,最后预测矿山充填管道磨损风险等级概率。该方法摒弃了关联性较低的指标,得到可靠的充填管道磨损风险概率分布,为类似矿山科学预测管道磨损风险及采取有效防护措施提供了理论依据。

关键词: 充填管道磨损, 改进PCA, 有序多分类Logistic, 磨损风险, 风险等级概率

Abstract:

The filling mining method is mainstream mining method used in major mines today.The safe implementation of filling technology depends on the construction of a good filling pipeline transportation system.Considering the complexity of the filled pipeline system and the large number and variety of influencing factors,in order to accurately predict the wear risk of the filling pipeline of the mine,a wear risk assessment of the filling pipeline based on the improved PCA and the ordered multi-class Logistic regression combination model was built.On the basis of practical experience,a total of 12 items including the volume fraction of the filling slurry,the filling doubled line,the corrosiveness of the filling slurry,and the material of the pipe were selected.Reasonable risk levels were divided according to the characteristics of each indicator and the corresponding evaluation model was constructed.The filling production data of four mines such as Jinchuan Longshou mine and Dahongshan copper mine were taken as samples.The improved PCA algorithm was used to analyze the correlation of each index,and the three principal components with a cumulative contribution rate of 91.125% and factor load of the principal component of each indicator were obtained.In the end,the two indicators with low correlation and small influence index,the density of the filling slurry(27.75) and the corrosiveness of the filling slurry(27.60) were deleted.The preferred main indicators were substituted into the ordered multi-class Logistic regression model,the continuous indicator values were linearly fitted to the discrete risk levels,and the regression coefficients,standard errors and significance levels of each index were calculated,and the equations of probability fluctuations were solved. Finally,the probability of four mines corresponding to different wear risks Ⅰ(not easy to wear),Ⅱ(relatively easy to wear),Ⅲ(easy to wear),Ⅳ(very easy to wear) were obtained. They were: Jinchuan Longshou mine is 0.247,0.440,0.154,0.153; Dahongshan copper mine is 0.179,0.240,0.323,0.258; Hedong gold mine is 0.181,0.227,0.345,0.247; Xincheng gold mine is 0.181,0.227,0.345,0.247.From a theoretical point of view,the risk level corresponding to the probability of the largest value was used as the final judgment level of the wear risk of the mine filling pipeline,and Jinchuan Longshou mine is Ⅱ,Dahongshan copper mine is Ⅲ,Hedong gold mine is Ⅲ,Xincheng gold mine is Ⅲ. The mine also makes corresponding level protection and maintenance measures based on this.From the actual production application,it is said that the probability of the Ⅳ risk level should be paid special attention,and the normal operation of the filling pipeline should be guaranteed to the greatest extent within the scope permitted by technology and funds. The mathematical method combining improved PCA and ordered multi-class Logistic regression avoids the collinearity between the various indicators,reduces the interference of the weak indicators on the evaluation results,and obtains the accurate risk of wear risk of filling the pipeline.It provides a theoretical basis for scientific prediction of pipeline wear risk and the implementation of effective and economical protection measures for similar mines.The mine can construct an appropriate filling pipeline protection system according to its own development.

Key words: filling pipeline wearing, improve principal component analysis, ordered multi-class Logistic regression, wear risk, probability of risk level

中图分类号: 

  • TD85

表1

定量指标评估等级取值范围"

磨损等级充填料浆体积分数/%充填料浆密度/(t·m-3)粗颗粒占比/%管道内径/mm管道铺设不平整度/%管道绝对糙度/μm充填倍线料浆流速与临界流速之比管道使用年限/a
I1<30I2<1.5I3<20I4>200I5<1.0I6≤100I7≥7.0I8<1.0I10<2
30≤I1<401.5≤I2<1.720≤I3<40150< I4≤2001.0≤I5<3.0I6≤1005.0≤I7<7.01.0≤I8<1.22≤I10<5
40≤I1<501.7≤I2<1.940≤I3<60100< I4≤1503.0≤I5<5.0300≤I6<5003.0≤I7<5.01.2≤I8<1.55≤I10<10
I1≥50I2≥1.9I3≥60I4≤100I5≥5.0I6≥5001.0≤I7<3.0I8≥1.5I10≥10

表2

定性指标评估等级取值范围"

磨损等级充填骨料形状充填料浆腐蚀性管道材料
圆形或椭圆形中性并且不含有能与充填管道发生反应的成分双金属耐磨复合管
方形特定条件下pH值发生变化与充填管道发生反应钢塑复合管
多棱角形弱酸、弱碱以及一系列能与充填管道发生轻微反应的成分陶瓷复合管
极不规则强酸、强碱或一系列容易与充填管道发生反应的成分单一复合材料

表3

矿山充填管道磨损影响指标调查数据"

矿山名称I1I2I3I4I5I6I7I8I9I10I11I12
金川龙首矿561.98381992.723003.21.38122
大红山铜矿331.69421600.985009.63.07332
河东金矿241.6852820.561005.21.610323
新城金矿521.94331071.272005.83.56431

表4

主成分因子载荷及影响力指数"

主成分I1I2I3I4I5I6I7I8I9I10I11I12
Z1-0.65-0.180.229-0.39-0.460.5510.7170.2020.2340.3750.0930.262
Z2-0.140.840.2240.4110.2370.2060.7910.437-0.23-0.39-0.100.137
Z30.54-0.010.3710.6460.2260.0850.0970.3010.1380.5990.4350.239
Bi42.0627.7537.4435.7534.6139.7738.1430.4737.8936.7127.6033.71

表5

管道磨损风险Logistic回归模型"

变量回归系数标准误差显著性水平
I10.35070.31080.1128
I30.03720.18260.3972
I4-0.10550.26860.0293
I5-0.04690.17820.2833
I60.24160.13060.0633
I70.28170.10260.0375
I80.09830.17720.3985
I90.24780.18120.1755
I10-0.29280.14280.0427
I120.27930.16420.0694
β1.78550.76340.0259

表6

4个矿山管道磨损风险等级概率"

矿山名称各风险等级概率
金川龙首矿0.2470.4400.1540.153
大红山铜矿0.1790.2400.3230.258
河东金矿0.1810.2270.3450.247
新城金矿0.1700.2300.4180.182

表7

不同模型风险评估结果比较"

矿山名称综合风险等级
基于改进PCA与多分类Logistic回归主客观组合权重与可变模糊模型未确知测度综合评价模型
金川龙首矿
大红山铜矿
河东金矿
新城金矿
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