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Gold Science and Technology ›› 2019, Vol. 27 ›› Issue (4): 581-588.doi: 10.11872/j.issn.1005-2518.2019.04.581

• Mining Technology and Mine Management • Previous Articles     Next Articles

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

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

CLC Number: 

  • TD35

Table 1

Filling slurry ratio data of learning samples"

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

Table 2

Normalized samples data of filling slurry ratio"

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

Table 3

Comparison of network errors for number of neurons in different hidden layers"

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

Fig.1

Flow chart of GA_BP neural network construction"

Table 4

Parameters to be optimized and their quantity"

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

Fig.2

Training process of GA_BP neural network"

Fig.3

Prediction of compressive strength effect by GA_BP neural network"

Table 5

Comparison between GA_BP and BP neural network"

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

Table 6

Parameters to be optimized and their range of values"

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

Table 7

Globally optimized genetic operator parameters"

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

Fig.4

Optimum proportion combination"

Table 8

Minimum cost and corresponding ratio parameter value under different compressive strength"

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
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