基于粒子群算法优化BP神经网络的溶浸开采浸出率预测

doi:10.11872/j.issn.1005-2518.2020.01.076

Abstract

Abstract:

With the development of mining technology，the development of mineral resources in China is progressing steadily.Nowadays，the mining trend is green mining mode with environmental protection and high safety.However，many mining methods are faced with serious pollution and low recovery rate.Leaching mining is a kind of mining method which combines mining，sorting and hydrometallurgy.In order to explore how to improve the leaching rate during the leaching process,in this paper,the leaching process of antimonial sulfide ore was taken as an example to analysis the main factors that influence the leaching rate.The BP neural network prediction model was established and optimized by the particle swarm algorithm,so it can conduct the variable training with existing sample data.Finally, the BP neural network model, the PSO - BP neural network model were used to predict leaching rate, respectively，and compared to two kinds of model error precision of the predicted values and actual values.The research results show that the impact containing antimony sulfide ore leaching rate of interaction between these factors and nonlinear relationship and leaching rate is more by 40 groups will affect the relationship between parameters and the leaching rate of leaching rate of data through the neural network training model to predict the 8 groups of leaching rate data，compare the leaching rate of output value and the actual values can be found using the particle swarm algorithm to optimize BP neural network model training accuracy is higher，the more accurate predictions for leaching rate，the prediction data set of normalized linear curve slope is more close to 1.Through further error analysis，it can be seen that compared with BP neural network，the relative errors and variances of the predicted results of the model optimized by particle swarm optimization algorithm and the actual values are significantly reduced.Therefore，this prediction model has certain reference value for the optimization of leaching rate in the current leaching mining area.

Key words: leaching mining, leaching rate, variable training, BP neural network, particle swarm optimization, error analysis

CLC Number:

TD87

Jinge BU,Jianhong CHEN. Based on Particle Swarm Algorithm to Optimize the BP Neural Network of Leaching Rate Prediction in Leaching Mining[J].Gold Science and Technology, 2020, 28(1): 82-89.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Fig.4

Fig.5

Table 3

Table 4

References 21

1	田庆华，洪建邦，辛云涛，等.基于人工神经网络模型的含锑硫化矿氧化浸出行为预测[J].中国有色金属学，2018，28（10）：2103-2111.
	Tian Qinghua，Hong Jianbang，Xin Yuntao，et al.Prediction of oxidation leaching behavior of antimony-containing sulfide ore based on artificial neural network model[J].The Chinese Journal of Nonferrous metals，2018，28（10）：2103-2111.
2	马胜利.溶浸采矿最优化问题分析与探讨[J].矿业研究与开发，1998，18（3）：12-14.
	Ma Shengli.Analysis and discussion on optimization of leaching mining [J].Mining Research and Development，1998，18（3）：12-14.
3	马春华，黄治能.溶浸采矿技术研究应用现状综述[J].采矿技术，2013，13（3）：42-45.
	Ma Chunhua，Huang Zhineng.Review of research and application of leaching mining technology [J].Mining Technology，2013，13（3）：42-45.
4	秦毅男，廖晓辉，赵庆治.一种基于粒子群优化算法的神经网络训练方法[J].河南师范大学学报（自然科学版），2007，35（3）：167-171.
	Qin Yinan，Liao Xiaohui，Zhao Qingzhi.A neural network training method based on particle swarm optimization [J].Journal of Henan Normal University（Nature Science Edition），2007，35（3）：167-171.
5	王昌汉.溶浸采铀（矿）[M].北京：原子能出版社，1998：15-28．
	Wang Changhan.Leaching Uranium（Ore）[M].Beijing：Atomic Energy Press，1998：15-28.
6	吴爱祥，王洪江，杨保华，等.溶浸采矿技术的进展与展望[J].采矿技术，2006，6（3）：39-48.
	Wu Aixiang，Wang Hongjiang，Yang Baohua，et al.Development and prospect of leaching mining technology[J]. Mining Technology，2006，6（3）：39-48.
7	毛健，赵红东，姚婧婧.人工神经网络的发展及应用[J].电子设计工程，2011，19（24）：62-65.
	Mao Jian，Zhao Hongdong，Yao Jingjing.Application and prospect of artificial neural network[J].Electronic Design Engineering，2011，19（24）：62-65.
8	韩立群.人工神经网络[M].北京：北京邮电大学出版社，2006.
	Han Liqun.Artificial Neural Network[M].Beijing：Beijing University of Posts and Telecommunications Press，2006.
9	Ren C，An N，Wang J Z，et al.Optimal parameters selection for BP neural network based on particle swarm optimization：A case study of wind speed forecasting[J].Knowledge-Based Systems，2014，56（3）：226-239.
10	彭时霖.基于人工神经网络的空调房间热环境参数的优化组合[D].长沙：湖南大学，2006.
	Peng Shilin.Combination of Thermal Environment Parameters of Air-Conditioned Rooms Based on Artificial Neural Network [D].Changsha：Hunan University，2006.
11	焦李成.神经网络系统理论[M].西安：西安电子科技大学出版社，1990.
	Jiao Licheng.Neural Network System Theory[M].Xi’an：Xi’an University of Electronic Science and Technology Press，1990.
12	刘浪，陈建宏，杨珊，等.基于灰色关联分析的PSO-BP算法预测矿震危险性[J].中南大学学报（自然科学版），2011，42（8）：2400-2405.
	Liu Lang，Chen Jianhong，Yang Shan，et al.Prediction of PSO-BP algorithm in risk prediction of mine earthquake based on grey correlation analysis[J].Journal of Central South University（Science and Technology），2011，42 （8）：2400-2405.
13	陈建宏，周汉凌，于凤玲.基于改进的QPSO-BP算法的铀价格预测模型及应用[J].计算机工程与应用，2013，49（21）：235-239.
	Chen Jianhong，Zhou Hanling，Yu Fengling.Uranium price forecasting model based on BP improved by QPSO and its application[J].Computer Engineering and Application，2013，49（21）：235-239.
14	朱晴，王晶晶.基于粒子群优化BP神经网络的高校科研管理评估研究[J].现代电子技术，2019，42（7）：87-94.
	Zhu Qing，Wang Jingjing.Research on university scientific research management evaluation based on particle swarm optimization algorithm and BP neural network[J].Modern Electronics Technique，2019，42（7）：87-94.
15	张磊.基于粒子群神经网络的软岩巷道变形预测[J].内蒙古煤炭经济，2014（8）：182-184.
	Zhang Lei.Prediction of soft rock roadway deformation based on particle swarm optimization neural network [J].Inner Mongolia Coal Economy，2014（8）：182-184.
16	许敏.基于PSO算法的BP神经网络在边坡稳定性评价中的应用[J].温州职业技术学院学报，2009，9（2）：45-51.
	Xu Min.Application of BP neural network in slope stability evaluation on the basis of PSO algorithm[J].Journal of Wenzhou Vocational and Technical College，2009，9（2）：45-51.
17	赵振江.基于PSO-BP神经网络的网络流量预测与研究[J].计算机应用与软件，2009，26（1）：218-221.
	Zhao Zhenjiang.Prediction and research on network traffic based on PSO-BP neural network[J].Computer Applications and Software，2009，26（1）：218-221.
18	袁子清.矿震地震活动响应规律及其危险性预测研究[D].长沙：中南大学，2007.
	Yuan Ziqing.Research on Seismic Aactivity Response Law and Hazard Prediction of Mine Earthquakes[D].Changsha：Central South University，2007.
19	Kim H J，Jang B S，Park C K，et al.Fatigue analysis of floating wind turbine support structure applying modified stress transfer function by artificial neural network[J].Ocean Engineering，2018，149：113-126.
20	王维，李洪儒.BP神经网络在状态监测数据趋势预测中的应用[J].微计算机信息，2005（21）：141-143.
	Wang Wei，Li Hongru.Application of BP ANN in predicting condition monitoring data[J].Microcomputer Information，2005（21）：141-143.
21	闻新.MATLAB 神经网络应用设计[M].北京：科学出版社，2001：278-283.
	Wen Xin.Neural Network Simulation and Application Based on MATLAB[M].Beijing：Science Press，2003：278-283.

Related Articles 15

[1]	Renhao LI,Helong GU,Xibing LI,Kuikui HOU,Deming ZHU,Xi WANG. A PSO-RBF Neural Network Model for Rockburst Tendency Prediction [J]. Gold Science and Technology, 2020, 28(1): 134-141.
[2]	Changjin LIANG, Chuanjing MA. Iodine-ammonia Leaching System for Leaching Gold from Waste Printed Circuit Boards [J]. Gold Science and Technology, 2019, 27(5): 784-790.
[3]	Wenfeng XIAO,Jianhong CHEN,Yi CHEN,Ximei WANG. Optimization of Multi-objective Filling Slurry Ratio Based on Neural Network and Genetic Algorithm [J]. Gold Science and Technology, 2019, 27(4): 581-588.
[4]	Xiaodan SUI,Zhouquan LUO,Yaguang QIN,Yule WANG,Dong PENG. Study on Prediction Method of Seepage Line of Tailings Dam Based on Wavelet Decomposition [J]. Gold Science and Technology, 2019, 27(1): 137-143.
[5]	YANG Wei，WANG Gang，CAO Huan，ZHANG Kai. Effect of Roasting-Acid Leaching on Gold and Silver Leaching Rate of Gold Concentrate Containing Copper [J]. Gold Science and Technology, 2017, 25(5): 122-126.
[6]	WANG Shuo. Experimental Study on Gold Leaching Process of a Gold Mine in Gansu Province [J]. Gold Science and Technology, 2017, 25(4): 122-127.
[7]	BI Lin，ZHAO Hui，YANG Xinfeng. Study on Principle and Method for Calculation of Earthwork Volume in the DIMINE Software [J]. Gold Science and Technology, 2017, 25(3): 108-115.
[8]	DENG Gao，YANG Shan. Stability Evaluation of Goafs Based on Combined Forecasting and Variable Precision Rough Fuzzy Set [J]. Gold Science and Technology, 2017, 25(3): 98-107.
[9]	YANG Junyan，GUO Hongzhu，CHEN Ping，XU Zhongmin. Application of Atmospheric Oxygen Enriched Leaching in Gold Cyanide Production [J]. Gold Science and Technology, 2016, 24(4): 160-163.
[10]	SHI Xiuzhi，FAN Yuqian，SHANG Xueyi. Strength Prediction of Filling Body Based on PCA and BP Neural Networks [J]. Gold Science and Technology, 2016, 24(3): 64-69.
[11]	ZHANG Lingling，LI Guoqing，KANG Kuangsong，LI Wei，HU Nailian. A BP Neural Network Based Method for Geological Missing Data Processing [J]. Gold Science and Technology, 2015, 23(5): 53-59.
[12]	GUO Jiang，ZHANG Bixiao. Invalidation Risk Evaluation of Backfill Pipe Based on PCA and BP Neural Network [J]. Gold Science and Technology, 2015, 23(5): 66-71.
[13]	CHEN Jianhong，ZHU Dingyao，CHEN Yijun，YE Aming，QIU Wen. Analysis of the Stability for Mine Tailings Dam Based on PCA-BP Neural Network [J]. Gold Science and Technology, 2015, 23(5): 47-52.
[14]	GAO Weiwei，LIU Jinqiang，LI Ruirui，ZHANG Rengao，DENG Hongrui，WANG Shanjian，LIN Xin. Application of Air-strength Agitation Leaching Trough in Gold Concentration Plant [J]. Gold Science and Technology, 2014, 22(3): 86-89.
[15]	LAI Weiqiang，LIN Honghan，ZHANG Bo，YOU Manli. Experimental Research on Oxidized Refractory Gold Ore from Lintaolegai in Sumuna，Mongolia [J]. Gold Science and Technology, 2013, 21(5): 154-156.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	LI Hong-Jie, CU Jing-Ji, MA Shu-Jiang. [J]. J4, 2010, 18(4): 41 -46 .
[2]	YANG Xiao-Hong. null[J]. J4, 2010, 18(6): 38 -41 .
[3]	XU Qing-Jun, XU Qing-Yuan, LU Ren-Jiang, SUN Jian, HUANG Shan, LIU Ji-Xin. Research on the Technology of Mining Complex Orebody Combined Shallow Hole Shrinkage and Deep Hole Blasting Method[J]. J4, 2011, 19(1): 49 -50 .
[4]	ZHANG Yuan, ZHANG Hong-Xi. [J]. J4, 2010, 18(4): 12 -16 .
[5]	LIANG Chao, CHU Yan-Xiu. Metallogenetic Characteristics of Gold and Polymetallic in the South of Anhui Province[J]. J4, 2011, 19(1): 21 -27 .
[6]	ZHANG Zhiguo, HU J iuhai, L IU Tao. Geolog ical Features and Prospecting Direction of Pangka imen Gold Deposit, Middle of Daxing′anling[J]. J4, 2008, 16(1): 43 -47 .
[7]	HU Qin-Xia, LI Jian-Zhong, YU Guang-Meng, XIE Yan-Fang, ZHANG Ku-Xiao. Discussion on Gold Ore Point of Bailongjiang Metallogenic Belt[J]. J4, 2010, 18(3): 51 -53 .
[8]	LIU Hongjun, LIU Xiyou. The Fashion and Expand of Erguna—HunLunbeier Deep Faultage and The Controlling To Deposite[J]. J4, 2007, 15(1): 10 -13 .
[9]	ZHANG YinFei. Discussion on M ineralization Regularity of the Eighth Ore Body of Shuangwang Gold Deposit, Shaanx i Prov ince[J]. J4, 2008, 16(1): 35 -38 .
[10]	LIU Hong-Jun, LIU Xi-You. The Age of Magma In Li Home Valley Diggings and Its Relation With Deposite[J]. J4, 2007, 15(1): 31 -34 .

编号	温度/℃	时间/h	固液比/（mL·g^-1）	搅拌速度/（r·min^-1）	HCl浓度/（mol·L^-1）	浸出率/%
1	85	1	10	300	4	48.17
2	85	5	8	300	4	84.73
3	85	3	10	500	3	85.06
4	85	4	10	300	3	83.75
5	85	3	10	300	4	76.75
6	85	1	10	300	3	46.25
7	85	5	10	300	1	12.03
8	85	1	10	500	3	58.32
9	85	4	10	900	3	90.93
10	85	5	10	300	3	90.50
11	85	3	10	300	2	51.19
12	85	5	6	300	4	76.00
13	85	3	10	300	1	10.75
14	85	2	10	300	4	64.25
15	85	4	12	300	4	91.08
16	85	1	10	300	2	36.75
17	85	2	10	300	1	10.24
18	85	1	12	300	4	59.07
19	85	1	6	300	4	44.14
20	85	5	10	900	3	93.75
21	85	1	10	900	3	74.21
22	85	1	10	300	4	47.25
23	85	2	10	900	3	88.00
24	85	3	12	300	4	87.78
25	85	2	8	300	4	63.65
26	85	5	10	300	4	93.06
27	85	5	12	300	4	93.58
28	85	4	10	300	4	86.25
29	85	1	10	700	3	63.53
30	85	3	6	300	4	65.57
31	85	5	6	300	4	76.00
32	85	4	10	300	1	11.01
33	85	4	10	500	3	89.96
34	85	5	10	300	4	93.06
35	85	2	10	500	3	72.67
36	85	2	10	300	4	66.20
37	85	5	10	500	3	93.24
38	85	2	10	700	3	74.61
39	85	4	10	700	3	91.48
40	85	5	10	300	2	66.25

编号	温度/℃	时间/h	固液比/（mL·g^-1）	搅拌速度/（r·min^-1）	HCl浓度/（mol·L^-1）	浸出率/%
1	85	4	8	300	4	80.83
2	85	5	10	700	3	93.55
3	85	3	10	300	4	78.06
4	85	2	10	300	4	66.02
5	85	4	10	300	3	83.75
6	85	3	10	300	3	76.75
7	85	3	10	300	4	78.06
8	85	3	10	700	3	85.26

序号	实际值	BP神经网络预测		PSO-BP神经网络预测
序号	实际值	预测值	相对误差	预测值	相对误差
1	80.83	79.45	-0.0171	80.45	-0.0047
2	93.55	92.70	-0.0093	93.21	-0.0036
3	78.06	78.21	0.0019	78.09	0.0038
4	66.02	66.92	0.0136	66.13	0.0017
5	83.75	83.05	-0.0083	83.40	-0.0042
6	76.75	74.98	-0.0231	75.91	-0.0109
7	78.06	78.87	0.0104	78.76	0.0089
8	85.26	85.30	0.0004	85.29	0.0004

指标	BP神经网络模型	PSO-BP神经网络模型
最大相对误差	0.0231	0.0109
最小相对误差	0.0004	0.0004
平均相对误差	0.0118	0.0057
样本方差	0.9675	0.1985

Based on Particle Swarm Algorithm to Optimize the BP Neural Network of Leaching Rate Prediction in Leaching Mining

RichHTML

PDF (PC)