中南大学学报(英文版)

J. Cent. South Univ. (2017) 24: 2431-2437

DOI: https://doi.org/10.1007/s11771-017-3654-y

Evaluation of spontaneous combustion tendency of sulfide ore heap based on nonlinear parameters

PAN Wei(潘伟)1, WU Chao(吴超)1, LI Zi-jun(李孜军)1, WU Zhi-wei(伍智伟)1, YANG Yue-ping(杨月平)2

1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China;

2. School of Nuclear Resources Engineering, University of South China, Hengyang 421001, China

Central South University Press and Springer-Verlag GmbH Germany 2017

Abstract:

To explore a new evaluation method for spontaneous combustion tendency of different areas in sulfide ore heap, ore samples from a pyrite mine in China were taken as experimental materials, and the temperature variations of the measuring points of simulated ore heap were measured. Combined with wavelet transform and nonlinear parameters extraction, a new method for spontaneous combustion tendency of different areas in sulfide ore heap based on nonlinear parameters was proposed and its reliability was verified by field test. The results indicate that temperature field evolution of the simulated ore heap presents significant spatial difference during self-heating process. Area with the maximum increasing extent of temperature in sulfide ore heap changes notably with the proceeding of self-heating reaction. Self-heating of sulfide ore heap is a chaotic evolution process, which means that it is feasible to evaluate spontaneous combustion tendency of different areas by nonlinear analysis method. There is a relatively strong correlation between the maximum Lyapunov exponent and spontaneous combustion tendency with the correlation coefficient of 0.9792. Furthermore, the sort of the maximum Lyapunov exponent is consistent with that of spontaneous combustion tendency. Therefore, spontaneous combustion tendency of different areas in sulfide ore heap can be evaluated by means of the maximum Lyapunov exponent method.

Key words:

sulfide ore heap; spontaneous combustion tendency; self-heating process; nonlinear parameters; maximum Lyapunov exponent

1 Introduction

Spontaneous combustion of sulfide ores is one of the most serious disasters in high-sulfur mines during mining process [1]. If the broken sulfide ores contact with air, oxidation reaction will occur slowly and release a little heat. If the reaction heat is not dissipate entirely, sulfide ores will be heated and more and more heat will be released. When the temperature of sulfide ores reaches the ignition temperature, spontaneous combustion will take place. It is well known that spontaneous fire of sulfide ores not only leads to serious economic losses, but also brings about a number of safety and environmental problems for further production in high-sulfur mines [2–4]. Therefore, research on spontaneous combustion tendency of sulfide ores is an important premise to ensure safe and efficient mining for high-sulfur mines.

According to the results of literature retrieval, there are a number of reports on spontaneous combustion tendency of sulfide ores. Determination indicators and determination methods are the two research hotspots. Determination indicators include self-heating initiative temperature [5], ignition point [6], apparent activation energy [7] and so on. Some typical determination methods contain nonlinear multi-parameters fusion method [8], evaluation method based on entropy and set pair analysis theory [9], and Fisher discriminant analysis method [10].

For sulfide ore heap, research on spontaneous combustion tendency of different areas is beneficial to determine combustible region and take pertinence measure for fire prevention. Currently, there are very scarce researches on it. As the self-heating of sulfide ore heap is a nonlinear multi-factor coupling evolution process, evaluation of spontaneous combustion tendency based on nonlinear theory is worthy of further study.

In this work, sulfide ore samples from a pyrite mine in China were taken as experimental materials, and temperature variations of the measuring points of simulated ore heap were measured with an experimental apparatus designed by the authors. Combined with wavelet transform and nonlinear parameters extraction, a new method for spontaneous combustion tendency of different areas in sulfide ore heap based on nonlinear parameters was proposed. Finally, the reliability of this evaluation method was verified by field test.

2 Experimental

2.1 Ore samples analysis

Sulfide ore samples were collected from a pyrite mine in China by the sampling method of multi-point sampling. The metallic mineral is pyrite with two-stage mineralization. The main chemical composition is listed in Table 1.

Table 1 Chemical composition of ore samples (mass fraction, %)

2.2 Experimental method

A cuboid model was designed to simulate the three-dimensional confined space of underground stope or ore bin. The length, width and height of the model were 200, 160 and 300 mm, respectively. Its top is open to be convenient for air exchange between the environment and the experiment system. In order to arrange the measuring points conveniently, the moisture ore samples were accumulated as a cuboid model with the geometric size of 200 mm×160 mm×250 mm. Owing to its small scale, eight measuring points (A, B, C, D, E, F, G, H) were arranged in the simulated ore heap. Their coordinates were (50, 50, 50), (50, 100, 100), (50, 150, 100), (100, 100, 150), (150, 50, 50), (150, 100, 100), (150, 150, 100) and (100, 150, 150), respectively.

In the experiment, the particle diameter of simulated ore heap was ground to less than 8 mm. The average porosity of the ore heap was measured with the value of 21.37%. The particle size distribution of the ore heap is listed in Table 2.

Owing to the weak oxidation of sulfide ores at normal temperature, a heat source was put in the ore heap to accelerate the oxidation process. The coordinate of heat source was (90, 90, 120). The initial temperature of experiment was set at about 20 °C, and the induced self-heating process was simulated at the heating rate of about 0.8 °C /min. According to the measured results of self-heating initiative temperature of some typical sulfide ore samples, the maximum temperature of the heat source was set at about 150 °C. Because self-heating always occurs in a certain depth of sulfide ore heap, it is more practical to adopt internal heating method. The experimental device is shown in Fig. 1.

Table 2 Particle size distribution of simulated ore heap

Fig. 1 Experimental device:

3 Experimental results

Figure 2 depicts the typical variation of temperature field of simulated ore heap at some periods using the three-dimensional reconstruction method. It can be found that area with the maximum increasing extent of temperature locates in the center position of the ore heap before 80 min. Due to the poor thermal conductivity of the ores, it is very difficult for heat to transfer to the external region. Thus, with the increase of distance to heat source, temperature increasing extent decreases during this period. After 80 min, as the water evaporates in the ore heap, the porosity increases to facilitate heat transfer. Temperature of some areas with sufficient oxygen reaches the critical point and self-heating takes place. Correspondingly, the central area of the ore heap is very hard to produce self-heating owing to the dense condition with lower oxygen content. Therefore, area with the maximum increasing extent of temperature makes a move from center position to external region during this period.

At present, the common evaluation index for spontaneous combustion tendency of different areas in sulfide ore heap is average temperature rising rate or increasing extent of temperature during a specific period. The evaluation results of this traditional method are subjective because the evaluation period is selected by the evaluators. Moreover, area with the maximum increasing extent of temperature in sulfide ore heap changes notably with the proceeding of self-heating reaction, as shown in Fig. 2. Therefore, in order to obtain objective and accurate results, it is necessary to propose a new evaluation method.

Fig. 2 Three-dimensional reconstruction of temperature field variation of simulated ore heap at different periods:

4 Research methods and flow

In this experiment, temperature variations of simulated ore heap were caused by the comprehensive effect of heat source and exothermic oxidation of sulfide ores, and the former played as a dominant role. Hence, it is necessary to separate self-heating information from the measured temperature series. Then, the temperature series were processed by wavelet decomposition and reconstruction [11]. The low frequency components of the series (large scale approximation parts) reflect the heating effect of heat source, and high frequency components (details) contain the complicated self- heating information of ores.

Due to the limited measured data, the temperature series of the eight measuring points were extended by the cubic spline interpolation method. For temperature increments could show the pre- and post-temperature variations more intuitively, they were selected as the research objects.

Six kinds of common nonlinear parameters, including correlation dimension (D2) [12], maximum Lyapunov exponent (λmax) [13], approximate entropy (EA) [14], fuzzy entropy (EF) [15], Hurst exponent (H) [16], Kolmogorov entropy (KTL) [17], were extracted to explore the relationship between nonlinear parameters and spontaneous combustion tendency by means of some related algorithms. Concrete research flow is illustrated in Fig. 3.

Fig. 3 Concrete research flow

5 Evaluation of spontaneous combustion tendency with nonlinear parameters

5.1 Extraction of nonlinear parameters

According to the optimizing criteria of wavelet function in Refs. [18], temperature increment series of measuring points were decomposed with the optimal wavelet function, and then the first layer of high frequency coefficients was reconstructed. The results of wavelet function optimization indicate that there is no common wavelet function suitable for all the measuring points. For the measuring point E, the optimal wavelet function is bior3.1, and the wavelet function db5 is suitable for the other points. Finally, the reconstructed high frequency series were normalized as research series and six kinds of nonlinear parameters were extracted. As an example, nonlinear parameters calculation results of the measuring point A are shown in Fig. 4, where C(m, r), m, r, y(i), i, R/S, n, RMSE, SSE, R, R2 are separately correlation integral, embedding dimension, threshold value, average distance of phase points after i discrete time step, discrete time step, average rescaled range, subinterval length, mean square error, residual sum of squares, correlation coefficient and coefficient of determination. Correlation dimension, maximum Lyapunov exponent, approximate entropy, fuzzy entropy, urst exponent, Kolmogorov entropy of this point are 1.4385, 0.0019, 0.5101, 1.0179, 0.5118 and 0.2379, respectively.

Table 3 lists the sort of nonlinear parameters of measuring points. It is found for each measuring point,that the correlation dimension of self-heating process is fraction and the maximum Lyapunov exponent is positive, which means that self-heating of sulfide ore heap is a chaotic evolution process. As a result, it is feasible to evaluate spontaneous combustion tendency of different areas with nonlinear analysis method.

Fig. 4 Nonlinear parameters calculation results of measuring point A:

Table 3 Sort of nonlinear parameters of measuring points

5.2 Relationship between spontaneous combustion tendency and nonlinear parameters

Spontaneous combustion tendency of different areas in simulated ore heap was characterized with average temperature rising rate (τav) during the whole period of experiment. The sort of spontaneous combustion tendency of measuring points is listed in Table 4.

Table 4 Sort of spontaneous combustion tendency of measuring points

In conjunction with Table 3, correlation coefficients between spontaneous combustion tendency and the six kinds of nonlinear parameters were calculated with the value of 0.6245, 0.9792, 0.0912, 0.0901, 0.2603 and –0.8256, respectively. It is known that maximum Lyapunov exponent has a relatively strong correlation with spontaneous combustion tendency. Moreover, the sort of maximum Lyapunov exponent is consistent with that of spontaneous combustion tendency. Thus, spontaneous combustion tendency of different areas in sulfide ore heap can be evaluated by means of maximum Lyapunov exponent method.

6 Field demonstration

Field test was carried out to verify the reliability of the nonlinear evaluation method of spontaneous combustion tendency proposed in this work (Fig. 5). The volume of the test ore heap was about 45 m3. The mass fractions of colloidal pyrite and fine particles of pyrite were 60% and 40%, respectively. The average density of the mixed ores was about 4.2 t/m3. Air temperature in the stope lay between 22 °C and 23 °C. Air velocity was less than 0.1 m/s and humidity was greater than 90%. There was no water but more humid on the floor. Three steel monitoring pipes with many pores were placed in the ore heap to measure the spontaneous combustion tendency of different areas.

Fig. 5 Schematic diagram of field test:

During the field test, the monitoring pipe 2 was blocked by the collapsed ores. As a result, temperature of this pipe was not measured completely. The measured temperature data of the other monitoring pipes (pipe 1 and 3) were selected as research objects. Temperature variation curves of field test are shown in Fig. 6. It is found that temperature variations of the monitoring pipe 1 and 3 are not significant before 28 d. Their increasing extents of temperature are 7.2 °C and 6.5 °C, respectively. After 28 d, self-heating reaction of the ore heap accelerates significantly due to the pre-heat and pre-oxidation effect of sulfide ores during the previous period. Correspondingly, temperature rises of the monitoring pipes 1 and 3 increase obviously and their temperature variation curves show large deviation, which means that spontaneous combustion tendency of different areas in the ore heap has great difference. Temperature rise of the monitoring pipe 1 is greater than that of the monitoring pipe 3, which indicates that spontaneous combustion tendency of the former is stronger. Figure 6 also displays that temperature of the monitoring pipe 3 is higher than that of the monitoring pipe 1 at a certain stage (before 16 d). It illustrates that distribution of high temperature area of sulfide ore heap is not constant during self-heating process, which validates the accuracy of laboratory experiments.

Fig. 6 Temperature variation curves of field test

The measured temperatures of the two monitoring pipes were extended by the cubic spline interpolation method, then their temperature increment series were constructed and normalized. On this basis, maximum Lyapunov exponent of normalization series was calculated. Figure 7 shows the relationship between maximum Lyapunov exponent and average temperature rising rate of the monitoring pipes 1 and 3. It is known that there is a positive correlation between maximum Lyapunov exponent and spontaneous combustion tendency. Therefore, maximum Lyapunov exponent can be chosen as a reference index to evaluate spontaneous combustion risk of different areas in sulfide ore heap.

At present, to prevent spontaneous fires of sulfide ore heap, temperature of the ore heap is measured through the monitoring pipes. If the temperature of some certain areas rises abnormally, some prevention methods (such as enhancing ventilation, spraying inhibitor and digging out ignition ores) will be applied. But according to mining practice, if temperature of the ore heap rises abruptly, spontaneous combustion will soon occur. Thus, incubation period for spontaneous combustion of sulfide ores should be adopted as research emphasis. Research on the measured temperature of sulfide ore heap during this period to extract useful information and determine danger areas of spontaneous combustion, is worthy of further study.

Fig. 7 Relationship between maximum Lyapunov exponent and average temperature rising rate of monitoring pipes 1 and 3

From the view of nonlinear characteristics of spontaneous combustion process, maximum Lyapunov exponent method to evaluate spontaneous combustion tendency of different areas in sulfide ore heap was proposed in this work through laboratory experiment and field tests. Compared with the traditional evaluation method based on average temperature rising rate or increasing extent of temperature, the new method is more objective. Furthermore, this method does not need long- term observation. The reason is that for a determined nonlinear evolution process, its nonlinear parameters are unique. If the time series that characterize the evolution process are adequate, nonlinear parameters can be approximately extracted with some related algorithms. So, it is possible for high-sulfur mines to take some specific measures to prevent spontaneous fires of sulfide ores at early stage.

7 Conclusions

1) During self-heating process, temperature field evolution of the simulated ore heap presents significant spatial difference. Area with the maximum increasing extent of temperature in sulfide ore heap changes notably with the proceeding of self-heating reaction. It makes a move from center position to external region.

2) For each measuring point, the correlation dimension of self-heating process is fraction and the maximum Lyapunov exponent is positive, which means that self-heating of sulfide ore heap is a chaotic evolution process. Thus, it is feasible to evaluate spontaneous combustion tendency of different areas with nonlinear analysis method.

3) Maximum Lyapunov exponent has a relatively strong correlation with spontaneous combustion tendency. Furthermore, the sort of maximum Lyapunov exponent is consistent with that of spontaneous combustion tendency, which is validated by field test results. Therefore, spontaneous combustion tendency of different areas in sulfide ore heap can be evaluated by means of maximum Lyapunov exponent method.

References

[1] PAYANT R, ROSENBLUM F, NESSET J E, FINCH J A. The self-heating of sulfides: Galvanic effects [J]. Minerals Engineering, 2012, 26: 57-63.

[2] MURPHY R, STRONGIN D R. Surface reactivity of pyrite and related sulfides [J]. Surface Science Reports, 2009, 64: 1-45.

[3] WANG Hong-jiang, XU Chao-shui, WU Ai-xiang, AI Chui-ming, LI Xi-wen, MIAO Xiu-xiu. Inhibition of spontaneous combustion of sulfide ores by thermopile sulfide oxidation [J]. Minerals Engineering, 2013, 49: 61-67.

[4] LIU Hui, WU Chao, SHI Ying. Locating method of fire source for spontaneous combustion of sulfide ores [J]. Journal of Central South University of Technology, 2011, 18(4): 1034-1040.

[5] LI Zi-jun, GU De-sheng, WU Chao. Dangerousness assessment of ore spontaneous combustion in high temperature high sulfide deposits [J]. Metal Mine, 2004(5): 57-59. (in Chinese)

[6] WANG Hong-jiang, WU Ai-xiang, LUO Fei-xia, ZHANG Xu, CHEN Jia-sheng, SHI Liang-gui. Experimental study on inhibiting the spontaneous combustion of sulfide ores by bacteria desulfurization [J]. Journal of University of Science and Technology Beijing, 2011, 33(4): 395-399. (in Chinese)

[7] YANG Fu-qiang, WU Chao, CUI Yan, LU Guang. Apparent activation energy for spontaneous combustion of sulfide concentrates in storage yard [J]. Transactions of Nonferrous Metals Society of China, 2011, 21(2): 395-401.

[8] PAN Wei, WU Chao, LI Zi-jun, YANG Yue-ping. Self-heating tendency evaluation of sulfide ores based on nonlinear multi-parameters fusion [J]. Transactions of Nonferrous Metals Society of China, 2015, 25(2): 582-589.

[9] XIE Zheng-wen, WU Chao, LI Zi-jun, YANG Fu-qiang. Evaluation on spontaneous combustion tendency of sulfide ores based on entropy and set pair analysis theory [J]. Journal of Central South University: Science and Technology, 2012, 43(5): 1858-1863. (in Chinese)

[10] HU Han-hua, LIU Zheng, LI Zi-jun, CUI Tian-tian. Fisher discriminant analysis to the classification of spontaneous combustion tendency grade of sulphide ores [J]. Journal of China Coal Society, 2010, 35(10): 1674-1679. (in Chinese)

[11] BELAYNEH A, ADAMOWSKI J, KHALIL B, QUILTY J. Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction [J]. Atmospheric Research, 2015, 172: 37-47.

[12] GRASSBERGER P, PROCACCIA I. Dimension and entropy of strange attractors from a fluctuating dynamic approach [J]. Physica D: Nonlinear Phenomena, 1984, 13: 34-54.

[13] MICHAEL T R, JAMES J C, CARLO J D L. A practical method for calculating largest lyapunov exponents from small data sets [J]. Physica D: Nonlinear Phenomena, 1993, 65(1, 2): 117-134.

[14] DIEGO L S, RODRIGO N. Detection of cracks in shafts with the Approximated Entropy algorithm [J]. Mechanical Systems and Signal Processing, 2016, 72-73: 286-302.

[15] LI Yong-bo, XU Min-qiang, ZHAO Hai-yang, HUANG Wen-hu. Hierarchical fuzzy entropy and improved support vector machine based binary tree approach for rolling bearing fault diagnosis [J]. Mechanism and Machine Theory, 2016, 98: 114-132.

[16] MANDELBROT B. Statistical methodology for non-periodic cycles: From the covariance to R/S analysis [J]. Annals of Economic and Social Measurement, 1972, 1: 257-290.

[17] SCHOUTEN J C, TAKENS F, VAN DEN BLEEK C M. Maximum-likelihood estimation of the entropy of an attractor [J]. Physical Review E, 1994, 49(1): 126-129.

[18] PAN Wei, WU Chao, LI Zi-jun, YANG Yue-ping. Fractal dimension characteristics of self-heating process of sulfide ores [J]. The Chinese Journal of Nonferrous Metals, 2015, 25(2): 492-498. (in Chinese)

(Edited by YANG Hua)

Cite this article as:

PAN Wei, WU Chao, LI Zi-jun, WU Zhi-wei, YANG Yue-ping. Evaluation of spontaneous combustion tendency of sulfide ore heap based on nonlinear parameters [J]. Journal of Central South University, 2017, 24(10): 2431–2437.

DOI:https://dx.doi.org/https://doi.org/10.1007/s11771-017-3654-y

Foundation item: Projects(51304238, 51534008) supported by the National Natural Science Foundation of China; Project(2015CX005) supported by Innovation Driven Plan of Central South University, China

Received date: 2016-05-10; Accepted date: 2016-09-05

Corresponding author: WU Chao, Professor; Tel: +86–13974870456; E-mail: wuchao@csu.edu.cn

Abstract: To explore a new evaluation method for spontaneous combustion tendency of different areas in sulfide ore heap, ore samples from a pyrite mine in China were taken as experimental materials, and the temperature variations of the measuring points of simulated ore heap were measured. Combined with wavelet transform and nonlinear parameters extraction, a new method for spontaneous combustion tendency of different areas in sulfide ore heap based on nonlinear parameters was proposed and its reliability was verified by field test. The results indicate that temperature field evolution of the simulated ore heap presents significant spatial difference during self-heating process. Area with the maximum increasing extent of temperature in sulfide ore heap changes notably with the proceeding of self-heating reaction. Self-heating of sulfide ore heap is a chaotic evolution process, which means that it is feasible to evaluate spontaneous combustion tendency of different areas by nonlinear analysis method. There is a relatively strong correlation between the maximum Lyapunov exponent and spontaneous combustion tendency with the correlation coefficient of 0.9792. Furthermore, the sort of the maximum Lyapunov exponent is consistent with that of spontaneous combustion tendency. Therefore, spontaneous combustion tendency of different areas in sulfide ore heap can be evaluated by means of the maximum Lyapunov exponent method.

[1] PAYANT R, ROSENBLUM F, NESSET J E, FINCH J A. The self-heating of sulfides: Galvanic effects [J]. Minerals Engineering, 2012, 26: 57-63.

[2] MURPHY R, STRONGIN D R. Surface reactivity of pyrite and related sulfides [J]. Surface Science Reports, 2009, 64: 1-45.

[3] WANG Hong-jiang, XU Chao-shui, WU Ai-xiang, AI Chui-ming, LI Xi-wen, MIAO Xiu-xiu. Inhibition of spontaneous combustion of sulfide ores by thermopile sulfide oxidation [J]. Minerals Engineering, 2013, 49: 61-67.

[4] LIU Hui, WU Chao, SHI Ying. Locating method of fire source for spontaneous combustion of sulfide ores [J]. Journal of Central South University of Technology, 2011, 18(4): 1034-1040.

[5] LI Zi-jun, GU De-sheng, WU Chao. Dangerousness assessment of ore spontaneous combustion in high temperature high sulfide deposits [J]. Metal Mine, 2004(5): 57-59. (in Chinese)

[6] WANG Hong-jiang, WU Ai-xiang, LUO Fei-xia, ZHANG Xu, CHEN Jia-sheng, SHI Liang-gui. Experimental study on inhibiting the spontaneous combustion of sulfide ores by bacteria desulfurization [J]. Journal of University of Science and Technology Beijing, 2011, 33(4): 395-399. (in Chinese)

[7] YANG Fu-qiang, WU Chao, CUI Yan, LU Guang. Apparent activation energy for spontaneous combustion of sulfide concentrates in storage yard [J]. Transactions of Nonferrous Metals Society of China, 2011, 21(2): 395-401.

[8] PAN Wei, WU Chao, LI Zi-jun, YANG Yue-ping. Self-heating tendency evaluation of sulfide ores based on nonlinear multi-parameters fusion [J]. Transactions of Nonferrous Metals Society of China, 2015, 25(2): 582-589.

[9] XIE Zheng-wen, WU Chao, LI Zi-jun, YANG Fu-qiang. Evaluation on spontaneous combustion tendency of sulfide ores based on entropy and set pair analysis theory [J]. Journal of Central South University: Science and Technology, 2012, 43(5): 1858-1863. (in Chinese)

[10] HU Han-hua, LIU Zheng, LI Zi-jun, CUI Tian-tian. Fisher discriminant analysis to the classification of spontaneous combustion tendency grade of sulphide ores [J]. Journal of China Coal Society, 2010, 35(10): 1674-1679. (in Chinese)

[11] BELAYNEH A, ADAMOWSKI J, KHALIL B, QUILTY J. Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction [J]. Atmospheric Research, 2015, 172: 37-47.

[12] GRASSBERGER P, PROCACCIA I. Dimension and entropy of strange attractors from a fluctuating dynamic approach [J]. Physica D: Nonlinear Phenomena, 1984, 13: 34-54.

[13] MICHAEL T R, JAMES J C, CARLO J D L. A practical method for calculating largest lyapunov exponents from small data sets [J]. Physica D: Nonlinear Phenomena, 1993, 65(1, 2): 117-134.

[14] DIEGO L S, RODRIGO N. Detection of cracks in shafts with the Approximated Entropy algorithm [J]. Mechanical Systems and Signal Processing, 2016, 72-73: 286-302.

[15] LI Yong-bo, XU Min-qiang, ZHAO Hai-yang, HUANG Wen-hu. Hierarchical fuzzy entropy and improved support vector machine based binary tree approach for rolling bearing fault diagnosis [J]. Mechanism and Machine Theory, 2016, 98: 114-132.

[16] MANDELBROT B. Statistical methodology for non-periodic cycles: From the covariance to R/S analysis [J]. Annals of Economic and Social Measurement, 1972, 1: 257-290.

[17] SCHOUTEN J C, TAKENS F, VAN DEN BLEEK C M. Maximum-likelihood estimation of the entropy of an attractor [J]. Physical Review E, 1994, 49(1): 126-129.

[18] PAN Wei, WU Chao, LI Zi-jun, YANG Yue-ping. Fractal dimension characteristics of self-heating process of sulfide ores [J]. The Chinese Journal of Nonferrous Metals, 2015, 25(2): 492-498. (in Chinese)