多因素耦合下尾砂胶结充填体强度优化与预测

doi:10.1007/s12204-022-2409-6

摘要/Abstract

摘要： 为解决低温环境下（≤20℃）尾砂胶结充填体强度不稳定问题，开展了多因素影响下尾砂胶结充填体强度优化与预测。采用响应面法进行试验设计，分析了固化温度、砂灰比和料浆质量分数耦合作用下充填体强度的发展规律，并对配合比进行了优化；采用人工神经网络算法（ANN）和粒子群优化算法（PSO），建立了充填体强度预测模型。根据响应面法试验结果，得到了不同固化温度下的最佳配比。固化温度为10 ~ 15℃时，砂灰比最佳配合比为9，料浆质量分数为71%；固化温度为15 ~ 20℃时，砂灰比最佳配合比为8，料浆质量分数为69%。ANN-PSO智能模型能够准确地预测尾砂胶结充填体强度，其平均相对估计误差值和相关系数值仅为1.95%和0.992，可以快速准确地预测不同混合比例下的尾砂胶结充填体强度。

关键词: 尾砂胶结充填体, 响应面法, 多因素耦合, 强度优化, 智能预测模型

Abstract: In order to solve the problem of strength instability of cemented tailings backfill (CTB) under low temperature environment ( 20 ◦C), the strength optimization and prediction of CTB under the influence of multiple factors were carried out. The response surface method (RSM) was used to design the experiment to analyze the development law of backfill strength under the coupling effect of curing temperature, sand-cement ratio and slurry mass fraction, and to optimize the mix proportion; the artificial neural network algorithm (ANN) and particle swarm optimization algorithm (PSO) were used to build the prediction model of backfill strength. According to the experimental results of RSM, the optimal mix proportion under different curing temperatures was obtained. When the curing temperature is 10—15 ◦C, the best mix proportion of sand-cement ratio is 9, and the slurry mass fraction is 71%; when the curing temperature is 15—20 ◦C, the best mix proportion of sandcement ratio is 8, and the slurry mass fraction is 69%. The ANN-PSO intelligent model can accurately predict the strength of CTB, its mean relative estimation error value and correlation coefficient value are only 1.95% and 0.992, and the strength of CTB under different mix proportion can be predicted quickly and accurately by using this model.

中图分类号:

TD 853

胡亚飞, 李克庆, 韩斌, 吉坤. 多因素耦合下尾砂胶结充填体强度优化与预测[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(5): 845-856.

HU Yafei(胡亚飞), LI Keqing(李克庆), HAN Bin (韩斌), JI Kun(吉坤). Strength Optimization and Prediction of Cemented Tailings Backfill Under Multi-Factor Coupling[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(5): 845-856.

参考文献

[1] GAO S, CUI X W, KANG S B, et al. Sustainable applications for utilizing molybdenum tailings in concrete[J]. Journal of Cleaner Production, 2020, 266: 122020.
[2] LI L, JIANG T, CHEN B J, et al. Overall utilization of vanadium-titanium magnetite tailings to prepare lightweight foam ceramics [J]. Process Safety andEnvironmental Protection, 2020, 139: 305-314.
[3] DENG H W, HE W, ZHOU K P. Heavy metals distribution in reclamation tailings and assessment of ecological risk [J]. The Chinese Journal of Nonferrous Metals, 2015, 25(10): 2929-2935 (in Chinese).
[4] ZHANG Q L, WANG S, WANG X M. Influence rules of unit consumptions of flocculants on interface sedimentation velocity of unclassified tailings slurry [J]. The Chinese Journal of Nonferrous Metals, 2017, 27(2):318-324 (in Chinese).
[5] LIU B, GAO Y T, JIN A B, et al. Dynamic characteristics of superfine tailings-blast furnace slag backfill featuring filling surface [J]. Construction and Building Materials, 2020, 242: 118173.
[6] ZHAO K, YU X, ZHU S T, et al. Acoustic emission investigation of cemented paste backfill prepared with tantalum-niobium tailings [J]. Construction and Building Materials, 2020, 237: 117523.
[7] XU W B, CAO Y, LIU B H. Strength efficiency evaluation of cemented tailings backfill with different stratified structures [J]. Engineering Structures, 2019, 180: 18-28.
[8] ZHANG S Y, REN F Y, GUO Z B, et al. Strength and deformation behavior of cemented foam backfill in subzero environment [J]. Journal of Materials Research and Technology, 2020, 9(4): 9219-9231.
[9] ROSHANI A, FALL M. Rheological properties of cemented paste backfill with nano-silica: Link to curing temperature [J]. Cement and Concrete Composites, 2020: 114: 103785.
[10] FANG K, FALL M. Effects of curing temperature on shear behaviour of cemented paste backfill-rock interface [J]. International Journal of Rock Mechanics and Mining Sciences, 2018, 112: 184-192.
[11] LI J J, YILMAZ E, CAO S. Influence of solid content, cement/tailings ratio, and curing time on rheology and strength of cemented tailings backfill [J]. Minerals, 2020, 10(10): 922.
[12] YIN S H, LIU J M, SHAO Y J, et al. Influence rule of early compressive strength and solidification mechanism of full tailings paste with coarse aggregate [J]. Journal of Central South University(Science and Technology), 2020, 51(2): 478-488 (in Chinese).
[13] QI C C, TANG X L, DONG X J, et al. Towards intelligent mining for backfill: A genetic programmingbased method for strength forecasting of cemented paste backfill [J]. Minerals Engineering, 2019, 133: 69-79.
[14] ZHANG F X, KANG Z Q, XIN D F. Characteristic test and proportion study of cemented backfill in an iron mine [J]. Mining Research and Development, 2020,40(2): 38-41(in Chinese).
[15] QI C C, YANG X Y, LI G C, et al. Research status and perspectives of the application of artificial intelligence in mine backfilling [J]. Journal of China Coal Society, 2021, 46(2): 688-700 (in Chinese).
[16] XU W B, LI Q L, LIU B. Coupled effect of curing temperature and age on compressive behavior, microstructure and ultrasonic properties of cemented tailings backfill [J]. Construction and Building Materials, 2020, 237: 117738.
[17] WANG Y, WU A X, WANG H J, et al. Effect of low temperature on early strength of cemented paste back-fill from a copper mine and engineering recommendations [J]. Chinese Journal of Engineering, 2018, 40(8):925-930 (in Chinese).
[18] HOU C, ZHU W C, YAN B X, et al. The effects of temperature and binder content on the behavior of frozen cemented tailings backfill at early ages [J]. Construction and Building Materials, 2020, 239: 117752.
[19] CHEN S M, WU A X, WANG Y M, et al. Coupled effects of curing stress and curing temperature on mechanical and physical properties of cemented paste backfill [J]. Construction and Building Materials, 2021,273: 121746.
[20] BULL A J, FALL M. Curing temperature dependency of the release of arsenic from cemented paste backfill made with Portland cement [J]. Journal of Environmental Management, 2020, 269: 110772.
[21] FU Z G, QIAO D P, GUO Z L, et al. Experimental research on mix proportioning and strength of cemented hydraulic fill with waste rock and eolian sand based on RSM-BBD [J]. Journal of China Coal Society, 2018, 43(3): 694-703 (in Chinese).
[22] TAO Y J, ZHU X N, TAO D P, et al. Optimization of triboelectrostatic decarbonization experiment of fly ash by Design-Expert [J]. Journal of China Coal Society, 2016, 41(2): 475-482 (in Chinese).
[23] GAO Q, YANG X B, WEN Z J, et al. Optimization of proportioning of mixed aggregate filling slurry based on BBD response surface method [J]. Journal of Hunan University (Natural Sciences), 2019, 46(6): 47-55 (in Chinese).
[24] ZHU L Y, LU W S, YANG P, et al. Thickening sedimentation of unclassified tailings under influence of external field based on response surface method [J]. The Chinese Journal of Nonferrous Metals, 2018, 28(9):1908-1917 (in Chinese).
[25] WU H, ZHAO G Y, CHEN Y. Multi-objective optimization for mix proportioning of mine filling materials [J]. Journal of Harbin Institute of Technology, 2017,49(11): 101-108 (in Chinese).
[26] XU M F, GAO Y T, JIN A B, et al. Prediction of cemented backfill strength by ultrasonic pulse velocity and BP neural network [J]. Chinese Journal of Engineering, 2016, 38(8): 1059-1068 (in Chinese).
[27] JAHANGIR H, EIDGAHEE D R. A new and robust hybrid artificial bee colony algorithm-ANN model for FRP-concrete bond strength evaluation [J]. Composite Structures, 2021, 257: 113160.
[28] RAO P S, KUMAR S, KHAN M Y. Comparison of prediction capabilities of MRR parameter using RSM and ANN for dry turning of Inconel 825 alloy using cryogenically treated tungsten carbide tool [J]. Materials Today: Proceedings, 2020. https://doi.org/10.1016/j.matpr.2020.10.163.
[29] ALONSO-MONTESINOS J, BALLESTR′IN J, LOPEZ G, et al. The use of ANN and conventional solar-plant meteorological variables to estimate atmospheric horizontal extinction [J]. Journal of Cleaner Production, 2021, 285: 125395.
[30] Chinese Forum of MATLAB. MATLAB neural network analysis of 30 cases [M]. Beijing: Beijing University of Aeronautics and Astronautics Press, 2010(in Chinese).
[31] QI C C, CHEN Q S, FOURIE A, et al. An intelligent modelling framework for mechanical properties of cemented paste backfill [J]. Minerals Engineering, 2018, 123: 16-27.
[32] RAMACHANDRAN S, JAYALAL M L, RIYAS A, et al. Application of genetic algorithm for optimization of control rods positioning in a fast breeder reactor core [J]. Nuclear Engineering and Design, 2020, 361:110541.
[33] ZHOU K P, WANG X X, GAO F. Stope structural parameters optimization based on strength reduction and ANN-GA model [J]. Journal of Central South University (Science and Technology), 2013, 44(7): 2848-2854(in Chinese).
[34] WU W, JI K, ZHANG P. Strength prediction of filling body based on ANN-PSO model and its engineering application [J]. Mining Research and Development,2020, 40(2): 53-57 (in Chinese).
[35] SHAO H D, DING Z Y, CHENG J S, et al. Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO [J]. ISA Transactions, 2020, 105: 308-319.
[36] MA C, ZHAO L, MEI X S, et al. Thermal error modeling of machine tool spindle based on particle swarm optimization and neural network [J]. Journal of Shanghai Jiao Tong University, 2016, 50(5): 686-695 (in Chinese).