黄金科学技术 ›› 2022, Vol. 30 ›› Issue (1): 46-53.doi: 10.11872/j.issn.1005-2518.2022.01.102
高峰1(),艾浩泉1(
),梁耀东2,罗增武2,熊信1,周科平1,杨根1
Feng GAO1(),Haoquan AI1(
),Yaodong LIANG2,Zengwu LUO2,Xin XIONG1,Keping ZHOU1,Gen YANG1
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%。
中图分类号:
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
Ren Sida,2019.Research on Green Development of China’s Mining Economy[D].Wuhan:China University of Geosciences. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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. | |
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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[11] | 谢敏雄,李政要,孙文波. 氰化贫液除杂的必要性及其综合评估[J]. J4, 2010, 18(6): 73-77. |
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