img

Wechat

  • CN 62-1112/TF 
  • ISSN 1005-2518 
  • Founded in 1988
Adv. Search
Mining Technology and Mine Management

Prediction of the Recovery Rate of a Gold Mine Based on Double Hidden Layer BP Neural Network

  • Shuai ZHANG ,
  • Xin ZHAO ,
  • Xiangyu PENG ,
  • Yubin WANG ,
  • Wanting GUI ,
  • Jiayi TIAN
Expand
  • School of Resources Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,Shaanxi,China

Received date: 2023-05-05

  Revised date: 2023-09-04

  Online published: 2024-03-22

Abstract

In order to grasp the action law of process factors affecting the actual recovery rate of a gold ore and predict the gold recovery rate,the flotation test was carried out by the method of orthogonal experiment.The sensitivity of process factors was analyzed by Pearson coefficient,and the gold recovery rate was predicted by using double hidden layer BP neural network.The results show that the sensitivity of the gold recovery rate to different factors in the production process is in descending order:2# oil dosage,sodium sulfide dosage,butyl xanthate dosage,copper sulfate dosage and grinding fineness.The reasons for the significant differences in the effects of 2# oil dosage,sodium sulfide dosage and butyl xanthate dosage on gold recovery rate were also elucidated.On this basis,used three main influencing factors such as 2# oil dosage,the study selected different input layer to the first implicit layer functions,such as tansig,purelin and logsig,and the rest of the activation functions remained unchanged.The first hidden layer to the second hidden layer function was logsig,and the second hidden layer to the output layer function was purelin.When research used logsig as the activation function,the fitted degree and accuracy are high,the goodness of fit R2 is 0.9792,and the relative average error is only 0.666%.The model can be used to predict the recovery rate of gold.The research has certain reference significance for the prediction of metal recovery rate in the production of precious metal mines.

Cite this article

Shuai ZHANG , Xin ZHAO , Xiangyu PENG , Yubin WANG , Wanting GUI , Jiayi TIAN . Prediction of the Recovery Rate of a Gold Mine Based on Double Hidden Layer BP Neural Network[J]. Gold Science and Technology, 2024 , 32(1) : 170 -178 . DOI: 10.11872/j.issn.1005-2518.2024.01.069

References

null Afzali E, Muthukumarana S,2023.Gradient-free kernel conditional stein discrepancy goodness of fit testing[J].Machine Learning with Applications,12(2):100463.
null Cheng Juanjuan,2022.An empirical study on the relationship between research and teaching in universities:An analysis based on Pearson’s correlation coefficient[J].China University Science and Technology,(10):46-52.
null Comley B A, Harris P J, Bradshaw D J,et al,2002.Frother characterization using dynamic surface tension measurements[J].International Journal of Mineral Processing,64:81-100.
null Fan Songhao, Su Panyun, Hou Xiuhong,2022.Characteristics of gold resources and metallogenic regularity in China[J].China Metal Bulletin,(9):47-49.
null Feng Yan, Liu Jian,2023.Prediction of mine friction resistance during tramcar running based on BP neural network[J].Journal of Safety Science and Technology,19(1):54-59.
null Hamid K, Sam A,2011.Flotation frothers:Review of their classifications,properties and preparation[J].The Open Mineral Processing Journal,4(1):25-44.
null Guo Rui,2020.Research on the Prediction of Reagent Addition in Concentrator Based on RSM and BP neural network[D].Kunming:Kunming University of Science and Technology.
null Li Bin, Zhang Yifan, Yan Shiye,et al,2022.Research on photovoltaic power generation prediction method based on improved extreme learning machine(ELM)[J].Journal of Engineering for Thermal Energy and Power,37(10):207-214.
null Li Liang, Wang Yubin, Lin Xingtong,et al,2022.Optimization of influence conditions on strength of gypsum-based composite cementitious materials using BP neural network[J].Nonferrous Metals(Mineral Processing Section),74(4):19-25.
null Li Shuqin, Wang Yubin, Ma Xiaoxiao,et al,2022.Effect of magnetization parameters on removal efficiency of zinc in fly ash by flotation and its model[J].Nonferrous Metals(Mineral Processing Section),(4):27-32.
null Li Z X, Yang Y, Li L W,et al,2023.A weighted Pearson correlation coefficient based multi-fault comprehensive diagnosis for battery circuits[J].Journal of Energy Storage,60:106584.
null Mondal B, Meetei M S, Das J,et al,2015.Quantitative recognition of flammable and toxic gases with artificial neural network using metal oxide gas sensors in embedded platform[J].Engineering Science and Technology,an International Journal,18(2):229-234.
null Nie Shanyu, He Guichun, Shi Yan,et al,2023.Research progress of flotation index prediction modeling based on data driven[J].The Chinses Journal of Nonferrous Metals,33(7):2330-2338.
null Ren Chuancheng, Xia Wencheng, Wang Wenbo,et al,2023.Prediction model for iron concentrate grade based on unbiased grey GM(1,1)[J].Nonferrous Metals(Mineral Processing Section),(1):41-45,56.
null Wang Bin, Li Jingchao, Wang Chengxi,et al,2020.An overview of characteristics and prospecting of gold ore deposits in China[J].Geological Journal of China Universities,26(2):121-131.
null Wang Kai, Chen Yun, Tang Jianlin,2023.Research on the application of BP neural network in performance detection of long-span cable-stayed bridges[J].Journal of Zhejiang University of Technology,51(2):171-179.
null Wang Mingli, Xu Baojin, Zhu Jiaqian,et al,2020.Flotation experiment of tailings from a gold mine in Jiangxi Province[J].Metal Mine,49(4):212-216.
null Wang Xiaochuan, Shi Feng, Yu Lei,et al,2013.43 Case Studies of MATLAB Neural Network[M].Beijing:Beihang University Press.
null Wang Xiu, Wang Jianping, Chen Hong,et al,2015.Situation analysis and sustainable development strategy of gold resources in China[J].Mining Research and Development,35(10):99-103.
null Wang Yandong,2020.Analysis and suggestions of gold resources prospecting situation in China from 2009 to 2019[J].China Mining Magazine,29(11):7-13.
null Xiang Qun,2019.Preliminary study on gold resources and geological exploration situation in China[J].Journal of the Science and Technology,(3):105.
null Xie Fengyun, Dong Jiankun, Wang Erhua,et al,2021.Research on gearbox fault diagnosis based on double hidden layer RWPSO-BP neural network[J].Modern Manufacturing Engineering,(6):155-160.
null Xu Xiaoyang,2013.Review of research on leaching process of carbonaceous refractory gold ore[J].Gold Science and Te-chnology,21(1):82-88.
null Xun Jingwen, Wang Yubin, Lei Dashi,et al,2020.Research on orthogonal test of flotation of a gold ore in Gansu[J].Precious Metals,41(4):56-60.
null Yan Zan, Wang Wendan, Wang Lu,et al,2018.Experimental study on beneficiation of one gold mine in Gansu Province[J].Gold Science and Technology,26(1):74-80.
null Yu Shengli, Wang Yuhua, Zhang Ying,et al,2013.Beneficiation experimental study on a low-grade refractory gold ore[J].Nonferrous Metals(Mineral Processing Section),(2):17-21,25.
null Zhao Xianghong, Bao Jingyang, Ouyang Yongzhong,et al,2019.Detecting outlier of multibeam sounding with BP neural network[J].Geomatics and Information Science of Wuhan University,44(4):518-524.
null Zhou Guanglang, Zhou Dongyun,2023.Experimental study on gold recovery from polysulfide fine-grained disseminated gold mines[J].Precious Metals,44(1):47-53.
null 程娟娟,2022.高校科研与教学关系实证研究——基于皮尔逊相关系数的分析[J].中国高校科技,(10):46-52.
null 樊松浩,苏攀云,侯秀宏,2022.中国金矿资源特征及成矿规律概要[J].中国金属通报,(9):47-49.
null 冯燕,刘剑,2023.基于BP神经网络的矿车运行时矿井摩擦阻力的预测[J].中国安全生产科学技术,19(1):54-59.
null 郭锐,2020.基于RSM和BP神经网络预测选矿厂药剂添加量研究[D].昆明:昆明理工大学.
null 李斌,张一凡,颜世烨,等,2022.基于改进极限学习机ELM的光伏发电预测方法研究[J].热能动力工程,37(10):207-214.
null 李亮,王宇斌,林星彤,等,2022.利用BP神经网络优化石膏基复合胶凝材料强度的影响条件[J].有色金属(矿山部分),74(4):19-25.
null 李淑芹,王宇斌,马晓晓,等,2022.水体磁化参数对飞灰中锌的浮选去除效果的影响规律及其模型[J].有色金属(选矿部分),(4):27-32.
null 聂善煜,何桂春,石岩,等,2023.基于数据驱动的浮选指标预测建模研究进展[J].中国有色金属学报,33(7):2330-2338.
null 任传成,夏文成,王文博,等,2023.基于无偏灰色GM(1,1)的铁精矿品位预测模型[J].有色金属(选矿部分),(1):41-45,56.
null 王斌,李景朝,王成锡,等,2020.中国金矿资源特征及勘查方向概述[J].高校地质学报,26(2):121-131.
null 王凯,陈韵,汤建林,2023.BP神经网络在大跨斜拉桥性能检测中的应用研究[J].浙江工业大学学报,51(2):171-179.
null 王明莉,徐宝金,朱加乾,等,2020.江西某金矿尾矿再选试验研究[J].金属矿山,49(4):212-216.
null 王小川,史峰,郁磊,等,2013.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社.
null 王修,王建平,陈洪,等,2015.我国金矿资源形势分析及可持续发展对策[J].矿业研究与开发,35(10):99-103.
null 王燕东,2020.2009—2019年我国金矿资源勘查形势分析与对策[J].中国矿业,29(11):7-13.
null 相群,2019.我国金矿资源与地质勘查形势的初步研究[J].科技风,(3):105.
null 谢锋云,董建坤,王二化,等,2021.基于双隐含层RWPSO-BP神经网络的齿轮箱故障诊断研究[J].现代制造工程,(6):155-160.
null 许晓阳,2013.碳质难处理金矿浸出工艺研究进展[J].黄金科学技术,21(1):82-88.
null 荀婧雯,王宇斌,雷大士,等,2020.甘肃某金矿浮选正交试验研究[J].贵金属,41(4):56-60.
null 阎赞,王闻单,王露,等,2018.甘肃某金矿选别试验研究[J].黄金科学技术,26(1):74-80.
null 余胜利,王毓华,张英,等,2013.某难选低品位金矿的选矿试验研究[J].有色金属(选矿部分),(2):17-21,25.
null 赵祥鸿,暴景阳,欧阳永忠,等,2019.利用BP神经网络剔除多波束测深数据粗差[J].武汉大学学报(信息科学版),44(4):518-524.
null 周光浪,周东云,2023.多硫化物微细粒浸染型金矿回收金试验研究[J].贵金属,44(1):47-53.
Outlines

/