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Gold Science and Technology ›› 2017, Vol. 25 ›› Issue (2): 38-44.doi: 10.11872/j.issn.1005-2518.2017.02.038

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Study on the Backfilling Material Properties Based on Fractal Theory and BP Neural Network

LIU Zhixiang,GONG Yongchao,LI Xibing   

  1. School of Resources and Safety Engineering,Central South University,Changsha    410083,Hunan,China
  • Received:2016-03-02 Revised:2016-05-19 Online:2017-04-28 Published:2017-05-12

Abstract:

In order to investigate the impact of whole tailing characteristics of size grading on the performance of backfilling material,this study selected fractional dimension number and correlation coefficient of fractional dimension number to characterize the geometric features of whole tailing.By using cement-sand ratio,slurry concentration,fractional dimension number and correlation coefficient of fractional dimension number as the input factors,compressive strength,slump and bleeding rate as the output factors,a fractal-BP neural network model was constructed to predict the properties of backfilling material.Then data of 7 mines were calculated by the fractal dimension and correlation coefficient of fractal dimension,and the BP neural network was used for the training and prediction.The results showed that the finer the tailing,the bigger the size grading fractional dimension,but contrary to the pore fractal dimension.Furthermore,the fractional dimension of whole tailing is a little higher than grading tailing.The correlation coefficient of the grading tailing is between 0.71 to 0.97,which is more dispersed than that of whole tailing.The relative error is under 8% using fractal-BP neural network model to predict the properties of backfilling material.In a conclusion,the fractal-BP neural network model had a fine precision,which provides a new approach to predict the properties of filling material.

Key words: size grading, fractional dimension number, neural network, properties of backfilling material

CLC Number: 

  • TD853.34 

[1] Gu Desheng,Li  Xibing.Modern Mining Science and Techno- logy for Metal Mineral Resources[M].Beijing:China Metallur- gical Industry Press,2006.[古德生,李夕兵.现代金属矿床开采科学技术[M].北京:冶金工业出版社,2006.]
[2] Zhou Aimin,Gu Desheng.Ming-filling model based on indus- trial ecology[J].Journal of Central South University (Science and Technology),2004,35(3):468-472.[周爱民,古德生.基于工业生态学的矿山充填模式[J].中南大学学报(自然科学版),2004,35(3):468-472.]
[3]  Liu Tongyou.Technology of Backfill Mining and It’s Application[M].Beijing:Metallurgical Industry Press,2001.[刘同有.充填采矿技术与应用[M].北京:冶金工业出版社,2001.]
[4] Liu Tongyou.Present status and development future of backfill technology in Chinese non-ferrous metal mineral mines[J].China Mining Magazine,2002,11(1):28-34.[刘同有.中国有色矿山充填技术的现状及发展[J].中国矿业,2002,11(1):28-34.]
[5] Cai Sijing,Huang Gang,Wu Di,et al.Experimental and mo- deling study on the rheological properties of tailings backfill [J].Journal of Northeastern University(Natural Science),2015,36(6):882-886.[蔡嗣经,黄刚,吴迪,等.尾砂充填料浆流变性能模型与试验研究[J].东北大学学报(自然科学版),2015,36(6):882-886.]
[6] Hou Guoquan,Guo Lijie,Yang Chao,et al.Testing and study on the rheological properties the high-density filling slurry of the full tailings[J].China Mining Magazine,2014,23(2):238-241.[侯国权,郭利杰,杨超,等.高浓度全尾砂充填料浆流变特性试验研究[J].中国矿业,2014,23(2):238-241.]
[7] Ren Haifeng,Bai Xianwu,Cheng Guanghua,et al.Test on transporation performance and rheological property of classified tailings slurry[J].Nonferrous Metals(Mining Section),2015,67(4):49-53.[任海锋,白贤武,程光华,等.分级尾砂料浆输送性能及流变特性试验研究[J].有色金属(矿山部分),2015,67(4):49-53.]
[8] Zhou Keping.A gray correlativity analysis of influence of particle size distribution of filling body strength[J].Mining Research and Development,1995,15(4):32-35.[周科平.充填体粒径分布对其强度影响的灰色关联分析[J].矿业研究与开发,1995,15(4):32-35.]
[9] Liu Zhixiang,Li Xibing.Chaotic optimization of tailings grad- ation[J].Journal of Central South University (Science and Te- chnology),2005,36(4):683-688.[刘志祥,李夕兵.尾砂级配的混沌优化[J].中南大学学报(自然科学版),2005,36(4):683-688.]
[10] Zhang Qinli,Li Xieping,Yang Wei.Optimization of filling slurry ratio in a mine based on back-propagation neural network [J].Journal of Central South University(Science and Technology),2013,44(7):2867-2874.[张钦礼,李谢平,杨伟.基于 BP 网络的某矿山充填料浆配比优化[J].中南大学学报(自然科学版),2013,44(7):2867-2874.]
[11] Han Bin,Wu Aixiang,Wang Yiming,et al.Optimization and application of cemented hydraulic fill (CHF)with low strength aggregate and extra fine grain full tailings[J].Journal of Central South University(Science and Technology),2012,43(6):2357-2362.[韩斌,吴爱祥,王贻明,等.低强度粗骨料超细全尾砂自流胶结充填配合比优化及应用[J].中南大学学报(自然科学版),2012,43(6):2357-2362.]
[12] Liu Zhixiang,Zhou Shilin.Knowledge bank model of design of backfill strength[J].Journal of Hunan University of Science & Technology(Natural Science Edition),2012,27(2):7-11.[刘志祥,周士霖.充填体强度设计知识库模型[J].湖南科技大学学报(自然科学版),2012,27(2):7-11.]
[13] Zhu Hua,Ji Cuicui.Fractal Theory and Its Application[M].Beijing:Science Press,2011.[朱华,姬翠翠.分形理论及其应用[M].北京:科学出版社,2011.]
[14] Tang Shaohui,Sang Yufa.The fractal characteristic of phy- sical and mechanical properties of cemented filling body[J].Nonferrous Metals(Mining Section),1996(5):14-17.[唐绍辉,桑玉发.胶结充填体物理力学性质的分形特性[J].有色金属(矿山部分),1996 (5):14-17.]
[15] Xu Yongfu,Sun Wanying.On fractal structure of expansive soils in China[J].Journal of Hohai University (Natural Sci-ences),1997,25(1):18-23. [徐永福,孙婉莹.我国膨胀土的分形结构的研究[J].河海大学学报(自然科学版),1997,25(1):18-23.]
[16] Gui Weihua,Hu Zhikun,Peng Xiaoqi.A coupled exercise algorithm of forward  neural  network  combined with gradient search and chaotic optimization search[J].Journal of Central South University of Technology(Natural Sci-ence),2002,33(6):629-631.[桂卫华,胡志坤,彭小奇.前馈网络的混沌梯度搜索耦合学习算法及应用[J].中南工业大学学报(自然科学版),2002,33(6):629-631.]
[17] Wu Jiang,Huang Furong,Huang Caihuan,et al.Study on near infrared spectroscopy of transgenic soybean iden-tification based on principal component analysis and neural network[J].Spectroscopy and Spectral Analysis,2013,33(6):1537-1541. [吴江,黄富荣,黄才欢,等.近红外光谱结合主成分分析和BP神经网络的转基因大豆无损鉴别研究[J].光谱学与光谱分析,2013,33(6):1537-1541.]
[18] Kobayashi M,Hattori M,Yamazaki H.Multidirectional as- sociative memory with a hidden layer[J].Systems and Com- puters in Japan,2002,33(6):1-9.
[19] Zhang L P,Yu H J,Hus X.Optimal choice of parameters for particle swarm optimization[J].Journal of Zhejiang Univer- sity:Science A,2005,6(6):528-534.
[20] Liu Zhixiang,Li Xibing.Study on fractal gradation of tailings and knowledge bank of its cementing strength[J].Chinese Journal of Rock Mechanics and Engineering,2005,24(10):1789-1793.[刘志祥,李夕兵.尾砂分形级配与胶结强度的知识库研究[J].岩石力学与工程学报,2005,24(10):1789-1793.]

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