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Gold Science and Technology ›› 2022, Vol. 30 ›› Issue (1): 46-53.doi: 10.11872/j.issn.1005-2518.2022.01.102

• Mining Technology and Mine Management • Previous Articles     Next Articles

Optimization of Proportioning of Waste Rock and Tailings Mixed Filling Materials Based on NSGA-II Algorithm

Feng GAO1(),Haoquan AI1(),Yaodong LIANG2,Zengwu LUO2,Xin XIONG1,Keping ZHOU1,Gen YANG1   

  1. 1.School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
    2.Guangxi Gaofeng Mining Co. , Ltd. , Nandan 547205, Guangxi, China
  • Received:2021-07-29 Revised:2021-09-27 Online:2022-02-28 Published:2022-04-25
  • Contact: Haoquan AI E-mail:csugaofeng@csu.edu.cn;2269840005@qq.com

Abstract:

With the increasing attention on the environmental protection of resource development and the strict requirements for the discharge of waste rocks,tailings,waste residues and other wastes generated in resource development,it is particularly important to dispose these wastes.The mixed filling of waste rocks and tailings is the most effective way to solve the discharge of mine waste.Taking the underground filling of Gaofeng mine as an example,It is necessary to determine the optimal ratio of waste rock and tailings mixed filling materials.The particle size of tailings and waste rock were analyzed by laser method and sieve method.The chemical composition of waste rock and tailings was obtained by X-ray spectrometry.The orthogonal experiment with four factors and four levels was designed,and the range analysis of the experimental data was carried out.The primary and secondary factors affecting the strength of filling body,slurry bleeding rate and slurry slump were explored,and the filling material ratio that preliminarily met the requirements of the mine was obtained.The slurry concentration is 83%,the ash sand ratio is 0.25,the waste tail ratio is 1.5 and the amount of pumping agent is 0.5%.According to the experimental data,the quadratic polynomial regression model of 28 d filling body strength,slurry bleeding rate and slump was established.The theoretical value and experimental value of the regression model were compared and analyzed.It is found that the relative error is within a reasonable range,indicating that the model has certain reliability for the prediction of filling body performance.Multi-objective optimization Pareto solution set obtained based on NSGA-II algorithm.The mixture ratio of waste rock and tailings filling slurry with good performance and lowest cost was determined.The results are as follows:According to range analysis,The ratio of ash to sand has the greatest influence on the strength of filling body,and the influence of slurry concentration,waste tail ratio and pumping agent decreases in turn.The ash sand ratio has obvious control effect on the slurry bleeding rate,and the waste tail ratio,slurry concentration and pumping agent effect decrease in turn.The slurry concentration has the greatest influence on slump,and the influence of pumping agent,ash sand ratio and waste tail ratio decreases in turn.Without increasing the cost of additional materials,the proportion of waste rock can be appropriately increased to improve the strength of the filling body.The cost of filling material for the optimized scheme is reduced by 2.9% compared with the preliminary scheme determined by orthogonal test,the optimized filling ratio is slurry concentration 82.989%,ash sand ratio 0.240,waste tail ratio 1.419 and pumping agent 0.537%.

Key words: orthogonal test, mixed filling materials, regression model, multi-objective optimization, NGSA-Ⅱ algorithm, gamultiobj function

CLC Number: 

  • TD853

Table 1

Physical properties of waste rock and tailings"

材料种类真密度/(g·cm-3松散密度/(g·cm-3紧密密度/(g·cm-3孔隙率/%堆积密实度
废石2.7221.5971.93429.470.709
尾砂3.0911.3131.76043.060.569

Fig.1

Particle size distribution diagram of tailings"

Fig.2

Particle size distribution diagram of waste rock"

Table 2

Level of factors for orthogonal test"

水平因素
A(料浆浓度)B(灰砂比)C(废尾比)D(泵送剂)
177%1∶43∶70%
279%1∶54∶60.5%
381%1∶65∶51.0%
483%1∶76∶42.0%

Table 3

Results of orthogonal test"

试验编号因素指标
料浆 浓度/%灰砂比废尾比

泵送剂

/%

28 d抗压 强度/MPa泌水率/%坍落度/cm
1771∶43∶703.17612.8727.8
2771∶54∶60.52.57012.8428.6
3771∶65∶51.02.49811.5927.2
4771∶76∶42.01.69817.6828.7
5791∶44∶61.05.40810.4528.8
6791∶53∶72.03.0199.8629.8
7791∶66∶402.91217.2928.4
8791∶75∶50.52.23312.4329.3
9811∶45∶52.05.33710.3428.8
10811∶56∶41.03.76112.9627.6
11811∶63∶70.53.02213.5328.0
12811∶74∶601.94313.0226.8
13831∶46∶40.57.6227.07026.0
14831∶55∶504.9909.7225.5
15831∶64∶62.03.07314.5526.6
16831∶73∶71.02.74714.2922.3

Table 4

Results of extreme analysis"

指标因素ABCD因素主次
28 d抗压 强度/MPak12.4865.3862.9913.255B>A>C>D
k23.3933.5853.2493.862
k33.5162.8763.7653.604
k44.6082.1553.9983.282
R2.1223.2311.0070.607
泌水率/%k113.7510.1812.6413.23B>C>A>D
k212.5111.3512.7211.47
k312.4614.2411.0212.32
k411.4114.3613.7513.11
R2.344.182.731.76
坍落度/cmk128.128.727.027.1A>D>B>C
k229.127.927.728.9
k327.827.627.726.5
k425.126.827.628.5
R4.11.80.702.20

Fig.3

Analysis diagram of sensitivity factors of different indexes"

Fig.4

SEM diagran of filling body with different cement-sand ratio"

Fig.5

Absolute value of relative error between model predictive value and test value"

Fig.6

Algorithm step diagram"

Fig.7

Pareto frontier map"

Table 5

Pareto partial optimal solution set"

组号料浆浓度/%灰砂比废尾比泵送剂/%28 d抗压强度/MPa泌水率/%坍落度/cm
182.9880.2481.3711.0877.1856.51726.72
282.9880.2501.1300.8077.8886.51227.35
382.9910.2461.3070.6457.5086.65326.66
482.9900.2501.0721.0447.8246.46827.50
582.9930.2481.2980.7377.5756.52326.75
682.9890.2401.4190.5377.0666.89726.16
782.9900.2421.4120.4997.2136.89126.19
882.9990.2451.4120.4587.3656.89626.21
982.9950.2471.2210.5247.6516.84526.89
1082.9900.2501.1870.9947.6946.36727.26

Table 6

Cost budget of different group numbers"

组号成本/(元/吨)组号成本/(元/吨)
1129.376120.38
2127.667120.80
3124.068121.58
4130.299123.34
5125.8410128.31
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