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  • CN 62-1112/TF 
  • ISSN 1005-2518 
  • 创刊于1988年
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采选技术与矿山管理

基于NSGA-Ⅱ算法的废石及尾砂混合充填料配比优化

  • 高峰 ,
  • 艾浩泉 ,
  • 梁耀东 ,
  • 罗增武 ,
  • 熊信 ,
  • 周科平 ,
  • 杨根
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  • 1.中南大学资源与安全工程学院,湖南 长沙 410083
    2.广西高峰矿业有限责任公司,广西 南丹 547205
高峰(1981-),男,湖南怀化人,博士,副教授,从事矿山开采、灾害机理与防治方面的研究工作。csugaofeng@csu.edu.cn

收稿日期: 2021-07-29

  修回日期: 2021-09-27

  网络出版日期: 2022-04-25

基金资助

国家重点研发计划项目“面向固废源头减量的硼镁铁矿精准连续化开采技术与示范”(2020YFC1909801);湖南省自然科学基金项目“寒区冻结裂隙岩体爆破损伤断裂特征与机理研究”(2020JJ4704)

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

  • Feng GAO ,
  • Haoquan AI ,
  • Yaodong LIANG ,
  • Zengwu LUO ,
  • Xin XIONG ,
  • Keping ZHOU ,
  • Gen YANG
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  • 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 date: 2021-07-29

  Revised date: 2021-09-27

  Online published: 2022-04-25

摘要

为了提高高峰矿尾砂充填体强度和解决井下废石利用问题,开展高峰矿废石及尾砂混合充填材料的最优配比研究,设计了四因素四水平的正交试验,采用极差分析获得了影响充填体强度、料浆泌水率和料浆坍落度的主次因素,得到初步满足矿山要求的配比:料浆浓度为83%,灰砂比为0.25,废尾比为1.5,泵送剂添加量为0.5%。根据正交试验数据建立了28 d充填体强度、料浆泌水率和坍落度的二次多项式回归模型,基于NSGA-Ⅱ算法进行多目标优化,获得最优充填料浆配比。研究结果表明:灰砂比对充填体强度影响最大,料浆浓度和废尾比次之,泵送剂影响最小;灰砂比对料浆泌水率有明显的控制作用,废尾比和料浆浓度次之,泵送剂作用最小;料浆浓度对坍落度影响最大,泵送剂和灰砂比影响次之,废尾比影响最小;多目标优化后的配比:料浆浓度为82.989%,灰砂比为0.240,废尾比为1.419,泵送剂添加量为0.537%,优化后的配比方案所需充填材料成本相比正交试验确定的方案成本下降了2.9%。

本文引用格式

高峰 , 艾浩泉 , 梁耀东 , 罗增武 , 熊信 , 周科平 , 杨根 . 基于NSGA-Ⅱ算法的废石及尾砂混合充填料配比优化[J]. 黄金科学技术, 2022 , 30(1) : 46 -53 . DOI: 10.11872/j.issn.1005-2518.2022.01.102

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%.

参考文献

null Chen Yincong,2017.Optimization Design Method and Application of Filling Material Proportion Based on Neural Network and Genetic Algorithm[D].Kunming:Kunming University of Science and Technology.
null Deb K, Pratap A, Agarwal S, al et,2002.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,6(2):182-197.
null Fu H C, Li P,2019.A multi-objective optimization model based on non-dominated sorting genetic algorithm[J].Internatio-nal Journal of Simulation Modeling,18(3):510-520.
null Hu N, Li C H, Liu Y, al et,2021.Fuzzy mathematics comprehensive forecasting analysis of metal mine rockburst based on multiple criteriahere[J].IOP Conference Series:Earth and Environmental Science,632(2):022080.
null Ju Jianhua, Qiang Haiyang,2017.The trend and direction of green development of the mining industry in China[J].China Mining Magazine,26(2):7-12.
null Li Xibing, Fan Yun, Lan Ming, al et,2015.Evaluation of phosphogypsum filling water quality using game theory and matter-element analysis theory[J].Science & Technology Review,33(15):22-26.
null Li Yifan, Zhang Jianming, Deng Fei, al et,2005.Experimental study on strength characteristics of tailings cement backfilling at deep-seated mined-out area[J].Rock and Soil Mechanics,26(6):865-868.
null Liu Li, Xia Yufeng, Ni Wenbin,2020.Local energy supply ratio optimization method based on particle swarm optimization[J].Electronic Design Engineering,28(9):179-183.
null Liu W, Luo F M, Liu Y H, al et,2019.Optimal siting and sizing of distributed generation based on improved nondominated sorting genetic algorithm II[J].Processes,7(12):955.
null Qi C C, Fourie A, Chen Q S, al et,2018.A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill[J].Journal of Cleaner Production,183:566-578.
null Ren Sida,2019.Research on Green Development of China’s Mining Economy[D].Wuhan:China University of Geosciences.
null Shi Xiaomeng, Liu Baoguo, Xiao Jie,2015.A method for determining the ratio of similar materials with cement and plaster as bonding agents[J].Rock and Soil Mechanics,36(5):1357-1362.
null Wang Xiaotian, Wang Hongjiang,2020.Research on dual-index quality evaluation method of filling proportion based on fuzzy mathematics[J].Mining Research and Development,40(9):60-65.
null Wu Hao, Zhao Guoyan, Chen Ying, al et,2019.Optimization of mix proportioning of mine filling materials using RSM-DF experiments design method[J].Journal of Basic Science and Engineering,27(2):453-461.
null Xiao Wenfeng, Chen Jianhong, Chen Yi, al et,2019.Optimization of multi-objective filling slurry ratio based on neural network and genetic algorithm[J].Gold Science and Technology,27(4):581-588.
null Yin Shenghua, Hao Shuo, Zou Long, al et,2020.Research on strength regression and slurry optimization of cemented backfill based on response surface method[J].Journal of Central South University(Science and Technology),51(6):1595-1605.
null Zhai Yuyao, Shi Xianjun, Yang Shuai, al et,2021.Multi-objective test optimization selection based on NSGA-Ⅱ under unreliable test conditions[J].Journal of Beijing University of Aeronautics and Astronautics,47(4):792-801.
null Zhang Guosheng, Chen Yanting, Hu Yajun, al et,2020.Resear-ch on mixing proportions of a new backfilling cementitious material based on artificial intelligence neural network[J].Mining Research and Development,40(9):143-148.
null Zhang Qinli, Li Xieping, Yang Wei,2013.Optimization of filling slurry ratio in a mine based on back-propagation neural network[J].Journal of Central South University(Science and Technology),44(7):2867-2874.
null Zhang Shengyou, Zhang Wei, Li Jinxin,2020.Strength influ-ence analysis of slag-cement-unclassified tailings cemented filling based on stepwise regression analysis[J].Bulletin of the Chinese Ceramic Society,39(12):3866-3873,3880.
null Zhang Xiuxiang, Qiao Dengpan,2015.Simulation and experiment of pipeline transportation of high density filling slurry with coarse aggregates[J].The Chinese Journal of Nonferrous Metals,25(1):258-266.
null Zhang Z W, Tian R L, Zhang X L, al et,2021.A novel butterfly-shaped auxetic structure with negative Poisson’s ratio and enhanced stiffness[J].Journal of Materials Science,56(25):14139-14156.
null 陈寅聪,2017.基于神经网络和遗传算法的充填料配比优化设计方法与应用[D].昆明:昆明理工大学.
null 鞠建华,强海洋,2017.中国矿业绿色发展的趋势和方向[J].中国矿业,26(2):7-12.
null 李夕兵,范昀,兰明,等,2015.基于博弈论的磷石膏充填水质物元评价[J].科技导报,33(15):22-26.
null 李一帆,张建明,邓飞,等,2005.深部采空区尾砂胶结充填体强度特性试验研究[J].岩土力学,26(6):865-868.
null 刘莉,夏宇峰,倪文斌,2020.基于粒子群算法的局域供能配比优化方法[J].电子设计工程,28(9):179-183.
null 任思达,2019.中国矿业经济绿色发展研究[D].武汉:中国地质大学.
null 史小萌,刘保国,肖杰,2015.水泥和石膏胶结相似材料配比的确定方法[J].岩土力学,36(5):1357-1362.
null 王筱添,王洪江,2020.基于模糊数学的双指标充填配比质量评价方法研究[J].矿业研究与开发,40(9):60-65.
null 吴浩,赵国彦,陈英,等,2019.基于RSM-DF的矿山充填材料配比优化[J].应用基础与工程科学学报,27(2):453-461.
null 肖文丰,陈建宏,陈毅,等,2019.基于神经网络与遗传算法的多目标充填料浆配比优化[J].黄金科学技术,27(4):581-588.
null 尹升华,郝硕,邹龙,等,2020.基于RSM的胶结充填体强度回归及料浆寻优研究[J].中南大学学报(自然科学版),51(6):1595-1605.
null 翟禹尧,史贤俊,杨帅,等,2021.不可靠测试条件下基于NSGA-Ⅱ的多目标测试优化选择[J].北京航空航天大学学报,47(4):792-801.
null 张国胜,陈彦亭,胡亚军,等,2020.基于人工智能神经网络新型充填胶凝材料配比研究[J].矿业研究与开发,40(9):143-148.
null 张钦礼,李谢平,杨伟,2013.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),44(7):2867-2874.
null 张盛友,孙伟,李金鑫,2020.基于逐步回归分析法的炉渣—水泥—全尾砂胶结充填体强度影响分析[J].硅酸盐通报,39(12):3866-3873,3880.
null 张修香,乔登攀,2015.粗骨料高浓度充填料浆的管道输送模拟及试验[J].中国有色金属学报,25(1):258-266.
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