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Mining Technology and Mine Management

Prediction Model of Soil Dump Stability Based on Principal Component Analysis and PSO-ELM Algorithm

  • Feng GAO ,
  • Xiaodong WU ,
  • Keping ZHOU
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  • School of Resources and Safety Engineering,Central South University,Changsha 410083,Hunan,China

Received date: 2020-09-21

  Revised date: 2021-04-13

  Online published: 2021-12-17

Highlights

Aiming at the stability analysis of dump slope,a PCA-PSO-ELM dump slope stability prediction model is proposed in this paper,which uses principal component analysis method to reduce data redundancy and particle swarm optimization algorithm to optimize the weight threshold of extreme learning machine. Eight prediction indexes of dump stability were determined in this model,including soil cohesion,internal friction angle,dump slope angle,foundation bearing capacity,seismic intensity,rainfall and snowfall conditions,dumping technology and random mining and digging conditions.According to 100 groups of corresponding dump data,training time,RMSE value and determination coefficient R 2 were used to evaluate and compare the validity of prediction results of PCA-PSO-ELM model,BP neural network model,ELM model and PSO-ELM model.The research results show that as the input variable to train and test the PSO-ELM network model,the dump stability sample data processed by PCA dimensionality reduction,made predicted value very close to the real value.The prediction accuracy and efficiency are not only higher than the ELM algorithm,but also far better than the traditional BP neural network algorithm.Compared with the PSO-ELM model without PCA treatment,the PSO-ELM model optimized by PCA method can significantly shorten the calculation time on the basis of little difference in efficiency,which proves that the method has certain practical value.

Cite this article

Feng GAO , Xiaodong WU , Keping ZHOU . Prediction Model of Soil Dump Stability Based on Principal Component Analysis and PSO-ELM Algorithm[J]. Gold Science and Technology, 2021 , 29(5) : 658 -668 . DOI: 10.11872/j.issn.1005-2518.2021.05.168

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矿业行业向深部进军:地下硬岩开采技术的发展趋势

地下硬岩开采占全球采矿作业的40%,相比于露天矿山,地下矿山往往更有针对性,成本更高,且生产率更低。采用哪种地下开采方法主要取决于所开采矿床的地质情况。其中,回采法是最常见的一种开采方法,并且该方法的产量在总产量中的占比也是最高的,几乎达到50%;分块崩落开采法(Block Caving)是运用最少的采矿方法之一,但是在总产量中所占的份额却非常大,接近25%。同样地,这些地下采矿方法的应用通常也是由矿床和采矿的经济效益所决定的,因此在一定程度上,采用哪种采矿方法并不是矿山运营企业所能控制的。

矿山的运营成本在很大程度上取决于所选择的采矿方法,但是也受到其他一些因素的影响。在地下硬岩采矿方法中,分块崩落开采法的运营成本是最低的,与露天开采的平均运营成本最为接近。目前采用的回采法则是运营成本第二高的采矿方法。在其他条件相同的情况下,深孔采矿法(Long-hole Stoping)的运营成本是最低的。然而,各个矿区和区域之间矿床的几何形态特征和生产率的差异是很大的,这导致深孔采矿法的平均运营成本要高于房柱采矿法。

在地下矿山的开采过程中,各个地区之间的成本和生产率也存在着显著的差异。在全球众多类型的矿产品和矿床类型的采矿作业中,地下矿山占40%,不同的地下矿山采用的开采方法不同,其劳动生产率和成本也是各不相同的。这种本质上的差异性意味着每个矿山要面对一系列特定的生产运营条件和一系列独特的挑战,只有克服这些挑战才能真正实现矿山生产率的提升。对于大多数地下矿山来说,矿床的内在特征决定了这些矿床永远不可能采用露天矿山的方法,尽管露天矿山采矿方法的成本要低得多。然而,根据以往的经验,总会找到一些提高矿山生产率和降低成本的途径。

http://www.goldsci.ac.cn/article/2021/1005-2518/1005-2518-2021-29-5-658.shtml

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