Nonlinear chaotic characteristic in leaching process and prediction of leaching cycle period

Abstract

A laboratory leaching experiment with samples of different grades was carried out, and an analytical method of concentration of leaching solution was put forward. For each sample, respectively, by applying phase space reconstruction for time series of monitoring data, the saturated embedding dimension and the correlation dimension were obtained, and the evolution laws between neighboring points in the reconstructed phase space were revealed. With BP neural network, a prediction model of concentration of leaching solution was set up and the maximum error of which was less than 2%. The results show that there exist chaotic characteristics in leaching system, and samples of different grades have different nonlinear dynamic features; the higher the grade of sample, the smaller the correlation dimension; furthermore, the maximum Lyapunov index, energy dissipation and chaotic extent of the leaching system increase with grade of the sample; by phase space reconstruction, the subtle change features of concentration of leaching solution can be magnified and the inherent laws can be fully demonstrated. According to the laws, a prediction model of leaching cycle period has been established to provide a theoretical foundation for solution mining.

Keywords

leaching system / phase space reconstruction / chaotic characteristic / leaching cycle period / neural network prediction

References

[1] LiY-h, LiuZ-h, ZhaoZ-w, LiQ-h, LiuZ-y, ZengLi. Determination of arsenic speciation in secondary zinc oxide and arsenic leachability [J]. Transactions of Nonferrous Metals Society of China, 2012, 22(5): 1209-1216

[2] WatlingH R. The bioleaching of sulphide minerals with emphasis on copper sulphides: A review [J]. Hydrometallurgy, 2006, 84(1/2): 81-108

[3] WangY-m, WuA-x, ZuoH, YangB-hua. Effect of particles sedimentation during leaching on seepage characteristic of copper dumps [J]. Transactions of Nonferrous Metals Society of China, 2007, 17(12): 2074-2078

[4] LiH-x, CangD-q, QiuG-z, WuA-xiang. Kinetics of secondary copper sulfide heap bioleaching concerning potential and heap constitution [J]. Journal of Central South University: Science and Technology, 2006, 37(6): 1087-1093

[5] SivakumarB. Chaos theory in geophysics: Past, present and future [J]. Chaos, Solitons and Fractals, 2004, 19(2): 441-462

[6] LiuX-y, LiuA-h, LiX-bing. Experimental study of permeability of rock-like material with filling fractures under high confining pressure [J]. Chinese Journal of Rock Mechanics and Engineering, 2012, 31(7): 1391-1398

[7] WuA-x, LiuC, YinS-h, KeJ-f, XueZ-lin. Leaching properties of quasi-sandstone type ore [J]. The Chinese Journal of Nonferrous Metals, 2014, 24(7): 1856-1863

[8] PanagouliaD, VlahogianniE I. Non-linear dynamics and recurrence analysis of extreme precipitation for observed and general circulation model generated climates [J]. Hydrological Processes, 2014, 28(4): 2281-2292

[9] LiuZ-x, GuoY-l, LiuC, LuJ-hua. Laws of strata energy release and corresponding safety waming system in metal mine [J]. Transactions of Nonferrous Metals Society of China, 2011, 21(11): 2508-2512

[10] AlbanoA M, MuenchJ, SchwartzC, MeesA I, RappP E. Singular-value decomposition and the Grassberger-Procaccia algorithm [J]. Physical Review A, 1988, 38(6): 3017-3026

[11] SherifJ, SinghaniaR. Extracting order from chaos [J]. Kybernetes, 1972, 38(6): 1014-1020

[12] BrestenC L, JaeH J. A study on the numerical convergence of the discrete logistic map [J]. Communications in Nonlinear Science and Numerical Simulation, 2009, 14(7): 3076-3088

[13] WolfA, SwiftJ B, SwinneyH L, VastanoJ A. Determining Lyapunov exponents from a time series [J]. Physica, 1985, 16(3): 285-317

[14] ShahverdievE M, HashimovaL H, HashimovaN T. Chaos synchronization in some power systems [J]. Chaos, Solitons and Fractals, 2008, 37(3): 827-834

[15] LiuZ-x, LiuC, LiuQ, YueY-liang. Chaotic time series reconstruction and security alarm system of rock mass deformation in undersea mining [J]. Chinese Journal of Geotechnical Engineering, 2010, 32(10): 1530-1534

[16] AndersVFourier analysis and its applications [M], 2003New YorkSpringer-Verlag122-145

[17] KayacanE, UlutasB, KaynakO. Grey system theory-based models in time series prediction [J]. Expert Systems with Applications, 2010, 37(2): 1784-1789

[18] SivakumarB, JayawardenaA W, FernandoT M. River flow forecasting: Use of phase-space reconstruction and artificial neural networks approaches [J]. Journal of Hydrology, 2002, 265(1): 225-245

[19] LiuZ-x, TangZ-x, WangW-h, SunJ-j, PengKang. New algorithm of mine slope reliability based on limiting state hyper-plane and its engineering application [J]. Journal of Central South University, 2015, 22(1): 317-322