基于双隐含层BP神经网络的某金矿回收率预测研究

doi:10.11872/j.issn.1005-2518.2024.01.069

Abstract

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.

Key words: BP neural network, Pearson coefficient, activation function, influencing factors, gold mine, recovery rate

CLC Number:

TD953

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.

Figures/Tables 14

Table 1

Table 2

Fig.1

Table 3

Table 4

Relationship between correlation coefficient and correlation"

相关系数\| $ρ$ \|	相关程度
0.8~1.0	极强相关
0.6~0.8	强相关
0.4~0.6	中等程度相关
0.2~0.4	弱相关
0.0~0.2	极弱相关或无相关

Table 4

Table 5

Table 6

Fig.2

Fig.3

Fig.4

Table 7

Fig.5

Fig.6

Table 8

References 0

	Afzali E， Muthukumarana S，2023.Gradient-free kernel conditional stein discrepancy goodness of fit testing［J］.Machine Learning with Applications，12（2）：100463.
	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.
	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.
	Fan Songhao， Su Panyun， Hou Xiuhong，2022.Characteristics of gold resources and metallogenic regularity in China［J］.China Metal Bulletin，（9）：47-49.
	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.
	Hamid K， Sam A，2011.Flotation frothers：Review of their classifications，properties and preparation［J］.The Open Mineral Processing Journal，4（1）：25-44.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	Wang Xiaochuan， Shi Feng， Yu Lei，et al，2013.43 Case Studies of MATLAB Neural Network［M］.Beijing：Beihang University Press.
	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.
	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.
	Xiang Qun，2019.Preliminary study on gold resources and geological exploration situation in China［J］.Journal of the Science and Technology，（3）：105.
	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.
	Xu Xiaoyang，2013.Review of research on leaching process of carbonaceous refractory gold ore［J］.Gold Science and Te-chnology，21（1）：82-88.
	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.
	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.
	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.
	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.
	Zhou Guanglang， Zhou Dongyun，2023.Experimental study on gold recovery from polysulfide fine-grained disseminated gold mines［J］.Precious Metals，44（1）：47-53.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.

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成分	含量/%	成分	含量/%
Au^*	2.13	Al₂O₃	12.66
Ag^*	1.31	TFe	6.40
S	2.28	K₂O	2.78
MgO	3.34	Pb	0.06
SiO₂	55.78	Zn	0.11
CaO	2.83

物相类别	含量/（g·t^-1）	占比/%
合计	0.94	100.00
裸露金及半裸露金	1.33	64.42
硫化物包裹金	0.35	16.07
赤褐铁矿包裹金	0.15	6.79
碳酸盐包裹金	0.13	5.95
硅酸盐包裹金	0.15	6.76

试验编号	水平					回收率/%
试验编号	A	B	C	D	E	回收率/%
1	1	1	1	1	1	65.46
2	1	2	2	2	2	75.92
3	1	3	3	3	3	84.15
4	1	4	4	4	4	83.52
5	2	1	2	3	4	70.42
6	2	2	1	4	3	77.98
7	2	3	4	1	2	82.71
8	2	4	3	2	1	80.13
9	3	1	3	4	2	75.69
10	3	2	4	3	1	82.54
11	3	3	1	2	4	80.02
12	3	4	2	1	3	76.69
13	4	1	4	2	3	64.32
14	4	2	3	1	4	77.45
15	4	3	2	4	1	82.12
16	4	4	1	3	2	84.30

因素	相关系数
磨矿细度（-74 μm含量）/%	0.0052
2^#油用量/（g·t^-1）	0.7436
丁基黄药用量/（g·t^-1）	0.1302
Na₂S用量/（g·t^-1）	0.3319
CuSO₄用量/（g·t^-1）	-0.0553

激活函数	训练集R²	测试集R²
tansig	0.9734	0.9842
purelin	0.9651	0.9401
logsig	0.9793	0.9862

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

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