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黄金科学技术 ›› 2019, Vol. 27 ›› Issue (4): 581-588.doi: 10.11872/j.issn.1005-2518.2019.04.581

• 采选技术与矿山管理 • 上一篇    下一篇

基于神经网络与遗传算法的多目标充填料浆配比优化

肖文丰(),陈建宏(),陈毅,王喜梅   

  1. 中南大学资源与安全工程学院,湖南 长沙 410083
  • 收稿日期:2019-03-22 修回日期:2019-05-23 出版日期:2019-08-31 发布日期:2019-08-19
  • 通讯作者: 陈建宏 E-mail:951630775@qq.com;cjh@263.net
  • 作者简介:肖文丰(1997-),男,湖北孝感人,硕士研究生,从事矿业经济和采矿系统工程研究工作。951630775@qq.com
  • 基金资助:
    国家自然科学基金青年基金项目“基于人工智能的矿山技术经济指标动态优化”(51404305);国家自然科学基金项目“基于属性驱动的矿体动态建模及更新方法研究”(51504286);中国博士后科学基金面上项目“辰州矿业采掘计划可视化编制与优化研究”(2015M 572269);湖南省科技计划项目“辰州矿业采掘计划可视化编制与优化研究”(2015RS4060)

Optimization of Multi-objective Filling Slurry Ratio Based on Neural Network and Genetic Algorithm

Wenfeng XIAO(),Jianhong CHEN(),Yi CHEN,Ximei WANG   

  1. School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
  • Received:2019-03-22 Revised:2019-05-23 Online:2019-08-31 Published:2019-08-19
  • Contact: Jianhong CHEN E-mail:951630775@qq.com;cjh@263.net

摘要:

随着充填法在地下矿山开采中的应用越来越广,在满足充填体强度要求的情况下,寻找生产成本最低的充填料浆配比对于矿山生产经营十分重要。基于人工神经网络和遗传算法提出了一种新的充填料浆配比优化方法。首先,以水泥质量分数、粉煤灰质量分数和尾砂质量分数3个充填料浆配比参数为优化参数,以充填体强度为优化目标,建立了3-9-1的BP神经网络,并基于遗传算法对BP神经网络进行优化,建立起预测精度更高的GA_BP神经网络。然后,将预测精度更高的GA_BP神经网络作为适应度函数,结合成本计算函数,通过遗传算法进行多目标优化以获取最优的充填料浆配比参数。结果表明:当充填体抗压强度为1.5 MPa时的成本最低,充填料浆配比组合为水泥质量分数为8%,粉煤灰质量分数为2.3%,尾砂质量分数为66.3%,最低成本为29.3元/t,优化结果与实际情况一致。

关键词: 充填体抗压强度, 充填法, 充填料浆配比优化, GA_BP神经网络, 遗传算法, 多目标优化

Abstract:

As the mining depth continues to increase, the pressure management of the stope and goaf is becoming more and more difficult.In order to maintain the stability of the stope,ensure the safety of the operation and prevent the collapse of the goaf,the filling method has become the preferred method for underground mining and has been widely applied.The filling method is to fill the filling body in the goaf to form a filling body with a certain compressive strength, and then carry out underground management by the supporting action of the filling body.Therefore, in the process of mining using the filling method, it is the key to efficient and safe mining of the mine to prepare the filling slurry with reasonable ratio and considerable economy to ensure that the compressive strength of the filling body can meet the needs of ground pressure management.However, there is no simple linear mapping between the filling ratio and the compressive strength of the filling body, and it is usually difficult to calculate by general mathematical methods.To this end, many researchers have conducted a lot of research on the ratio of filling slurry and the optimization of compressive strength of the filling body.Most of these studies only consider optimization under single-objective conditions, and there are few studies on multi-objective conditions.Therefore, other methods should be found to carry out multi-objective optimization research. In this paper, based on artificial neural network and genetic algorithm, a new optimization method of filling ratio is proposed.Firstly, the parameters of cement filling ratio, fly ash mass fraction and tailings mass fraction were optimized parameters, and the backfill strength was used as the optimization target to establish a BP neural network of 3-9-1.The BP neural network is optimized based on genetic algorithm, and the GA_BP neural network with higher prediction accuracy was established.Then,the GA_BP neural network with higher prediction accuracy is used as the fitness function, combined with the cost calculation function, and multi-objective optimization is performed by genetic algorithm to obtain the optimal filling slurry ratio parameter.The results show that when the compressive strength of the filling body is 1.5 MPa, the cement mass fraction is 8%, the fly ash mass fraction is 2.3%, the tailings mass fraction is 66.3%, the cost is the lowest, and the lowest cost is 29.3 yuan/t.The optimization results are consistent with the actual situation.

Key words: filling body compressive strength, filling method, filling slurry ratio optimization, GA_BP neural network, genetic algorithm, multi-objective optimization

中图分类号: 

  • TD35

表1

充填料浆配比学习样本数据"

参数序号
123456789101112
ω115.2014.8014.0010.8610.008.448.227.786.916.734.694.38
ω2000000000000
ω360.8059.2056.0065.1460.0067.5665.7862.2269.0967.2770.3167.50
抗压强度/MPa3.673.642.892.431.641.591.541.481.140.840.540.43
参数序号
131415161718192021222324
ω14.387.788.448.676.366.913.503.8010.576.367.093.90
ω2015.5616.8917.7312.7313.8214.0015.200014.1815.60
ω365.6346.6750.6752.0050.9155.2752.5057.0063.4363.6456.7358.50
抗压强度/MPa0.430.432.312.211.491.670.530.672.280.901.850.74

表2

充填料浆配比归一化后的样本数据"

参数序号
123456789101112
ω110.9650.8970.6290.5550.4220.4030.3650.2910.2760.1010.085
ω2000000000000
ω30.5970.5300.3940.7810.5630.8830.8080.6570.9480.87110.881
抗压强度/MPa3.673.642.892.431.641.591.541.481.140.840.540.43
参数序号
131415161718192021222324
ω10.0750.3650.4220.4410.2440.29100.0250.6040.2440.3060.034
ω200.8970.97410.7340.7970.8070.877000.8180.900
ω30.80200.1690.2250.1790.3630.2460.4370.7090.7170.4250.500
抗压强度/MPa0.431.852.132.211.491.670.530.672.280.91.850.74

表3

不同隐含层神经元数的网络误差对比"

网络结构误差迭代次数网络结构误差迭代次数
3-4-10.28381593-9-10.176891
3-5-10.2298873-10-10.1791120
3-6-10.2238793-11-10.195994
3-8-10.19611233-12-10.202248

图1

GA_BP神经网络构建流程图"

表4

待优化参数及数量"

参数数量/个
输入层与隐含层连接权值数量27
隐含层阈值数量9
隐含层与输出层连接权值数量9
输出层阈值数量1

图2

GA_BP神经网络训练过程"

图3

GA_BP神经网络预测抗压强度效果"

表5

GA_BP 与 BP神经网络的比较"

试验序号期望输出GA_BP模型BP模型
实际输出相对误差/%实际输出相对误差/%
220.900.90050.050.96116.79
231.851.84920.081.74925.45
240.740.73970.040.78245.73

表6

待优化参数及其取值范围"

配比参数范围配比参数范围
ω12%~16%ω350%~75%
ω20%~20%

表7

全局遗传算子参数"

遗传算子取值遗传算子取值
最优前端个体系数0.3停止代数200
种群大小1 000适应度函数值偏差1×10-3
最大进化代数200

图4

最优配比组合"

表8

不同抗压强度下最低成本及对应的配比参数取值"

参数不同抗压强度下最低成本时的参数取值
1.2 MPa1.3 MPa1.4 MPa1.5 MPa1.6 MPa1.7 MPa1.8 MPa1.9 MPa2.0 MPa
ω17.07.47.68.06.86.58.17.27.1
ω22.52.22.92.39.212.05.710.912.5
ω367.267.367.566.866.366.467.867.569.0
最低成本/(元·t-126.227.428.529.330.431.132.232.733.5
1 于世波, 杨小聪, 董凯程, 等. 空场嗣后充填法充填体对围岩移动控制作用时空规律研究[J]. 采矿与安全工程学报,2014,31(3): 430-434.
YuShibo, YangXiaocong, DongKaicheng,et al. Space-time rule of the control action of filling body for the movement of surrounding rock in method of the delayed filling open stoping[J]. Journal of Mining and Safety Engineering,2014,31(3): 430-434.
2 由希, 任凤玉, 何荣兴, 等. 阶段空场嗣后充填胶结充填体抗压强度研究[J]. 采矿与安全工程学报,2017, 34(1): 163-169.
YouXi, RenFengyu, HeRongxing,et al. Research on compressive strength of cemented filling body in subsequent filling at the stage of open stope [J]. Journal of Mining and Safety Engineering,2017, 34(1): 163-169.
3 JiangH Q, FallM, CuiL. Freezing behaviour of cemented paste backfill material in column experiments[J]. Construction and Building Materials, 2017, 147:837-846.
4 KoohestaniB, KoubaaA, BelemT,et al.Experimental investigation of mechanical and microstructural properties of cemented paste backfill containing maple-wood filler[J].Construction and Building Materials, 2016, 121:222-228.
5 QiC C, FourieA, ChenQ S.Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill[J]. Construction and Building Materials, 2018, 159:473-478.
6 TekinYılmaz, BayramErcikdi. Predicting the uniaxial compressive strength of cemented paste backfill from ultrasonic pulse velocity test[J].Nondestructive Testing and Evaluation, 2015, 31(3):247-266.
7 吴浩, 赵国彦, 陈英. 多目标条件下矿山充填材料配比优化实验[J]. 哈尔滨工业大学学报,2017, 49(11): 101-108.
WuHao, ZhaoGuoyan, ChenYing. Multi-objective optimization for mix proportioning of mine filling materials[J]. Journal of Harbin Institute of Technology,2017, 49(11): 101-108.
8 赵国彦, 马举, 彭康, 等. 基于响应面法的高寒矿山充填配比优化[J]. 北京科技大学学报,2013, 35(5): 559-565.
ZhaoGuoyan, MaJu, PengKang,et al. Mix ratio optimization of alpine mine backfill based on the response surface method[J].Journal of University of Science and Technology Beijing,2013, 35(5): 559-565.
9 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.
ZhangQinli, LiXieping, YangWei.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.
10 ArmaghaniD, MohamadE, NarayanasamyM,et al. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition[J]. Tunnelling and Underground Space Technology, 2017, 63:29-43.
11 HassanW. Application of a genetic algorithm for the optimization of a location and inclination angle of a cut-off wall for anisotropic foundations under hydraulic structures[J].Geotechnical and Geological Engineering, 2019, 37(2):883-895.
12 SantosJ, FerreiraA, FlintschG. An adaptive hybrid genetic algorithm for pavement management[J].International Journal of Pavement Engineering,2019, 20(3):266-286.
13 DingS F, LiH, SuC Y,et al. Evolutionary artificial neural networks: A review[J].Artificial Intelligence Review, 2013, 39(3):251-260.
14 WangJ, FangJ D, ZhaoY D. Visual prediction of gas diffusion concentration based on regression analysis and BP neural network[J]. Journal of Engineering-Joe, 2019,(13):19-23.
15 王振华, 龚殿尧, 李广焘, 等. 遗传算法优化神经网络的热轧带钢弯辊力预报模型[J]. 东北大学学报(自然科学版),2018, 39(12): 1717-1722.
WangZhenhua, GongDianyao, LiGuangtao,et al. Bending force prediction model in hot strip rolling based on artificial neural network optimize by genetic algorithm[J].Journal of Northeastern University(Natural Science),2018, 39(12): 1717-1722.
16 范伟, 林瑜阳, 李钟慎. 遗传算法优化的BP神经网络压电陶瓷蠕变预测[J]. 电机与控制学报,2018, 22(7): 91-96.
FanWei, LinYuyang, LiZhongshen. Prediction model of the creep of piezoceramic based on BP neural network optimized by genetic algorithm[J]. Electric Machines and Control,2018, 22(7): 91-96.
17 邬书良, 陈建宏, 杨珊. 基于主成分分析与BP网络的锚杆支护方案优选[J]. 工程设计学报,2012, 19(2): 150-155.
WuShuliang, ChenJianhong, YangShan.Optimization of bolting scheme based on combination of principal component analysis and BP neural network[J]. Chinese Journal of Engineering Design,2012, 19(2): 150-155.
18 DengJ, GuD, LiX,et al. Structural reliability analysis for implicit performance functions using artificial neural network[J]. Structural Safety, 2005, 27(1):25-48.
19 KonakA, CoitD, SmithA. Multi-objective optimization using genetic algorithms: A tutorial[J]. Reliability Engineering and System Safety, 2006, 91(9):992-1007.
20 SchmittL.Theory of genetic algorithms Ⅱ: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling[J]. Theoretical Computer Science, 2004, 310(1/2/3):181-231.
21 DingS F, ZhangY A, ChenJ R,et al. Research on using genetic algorithms to optimize Elman neural networks[J]. Neural Computing and Applications, 2013, 23(2):293-297.
22 LimaD C L, LimaoD O R, RoisenbergM. Optimization of neural networks through grammatical evolution and a genetic algorithm[J]. Expert Systems with Applications, 2016, 56:368-384.
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