ORIGINAL PAPER
Fractal characteristics analysis of ore-particle clusters under quasi-static loading
1
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology
2
Jiangxi Province Engineering Research Center for Mechanical and Electrical of Mining and Metallurgy
Submission date: 2024-01-08
Final revision date: 2024-04-10
Acceptance date: 2024-07-13
Publication date: 2024-09-11
Corresponding author
Shuhao Hao
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2024;40(3):69-86
KEYWORDS
TOPICS
ABSTRACT
To investigate the quasi-static loading fracture characteristics of a certain wolframite ore, a crushing model of ore particle clusters was established using the Tavares model. The fracture characteristics of ore particle clusters under quasi-static loading were studied through simulation, and the results were compared with experimental data from crushing tests. The findings revealed that under different quasi-static loads, the post-crushing particle size distribution varied significantly. With increasing quasi-static loads, the proportion of smaller particles after crushing increased, indicating a higher degree of fragmentation in the ore particle clusters. Additionally, as the quasi-static load continued to increase, the average particle size of the ore particle clusters decreased. The average particle size provided a direct and intuitive measure of the fragmentation status of the ore particle clusters. Furthermore, the ore particle clusters exhibited fractal patterns during quasi-static loading, with the fractal dimension of particle size distribution ranging from 0.9205 to 1.3603 under different quasi-static loads. The fractal dimension increased with the increment of quasi-static load, indicating a higher level of fragmentation. Moreover, the fractal dimension of ore particle clusters during quasi-static loading exhibited a decreasing trend with the average particle size of fragmentation. This study contributes to a comprehensive understanding of the fractal characteristics associated with the quasi-static loading fracture of ore particle clusters.
ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China grant 52364025.
METADATA IN OTHER LANGUAGES:
Polish
Analiza charakterystyk fraktalnych skupisk cząstek rudy pod obciążeniem quasi-statycznym
wymiar fraktalny, skupiska cząstek rudy, charakterystyka pęknięć, model Tavaresa, wolframit
Aby zbadać charakterystykę pękania pod obciążeniem quasi-statycznym określonej rudy wolframitu, opracowano model kruszenia skupisk cząstek rudy przy użyciu modelu Tavaresa. Charakterystykę pękania skupisk cząstek rudy pod obciążeniem quasi-statycznym zbadano poprzez symulację, a wyniki porównano z danymi eksperymentalnymi z testów kruszenia. Wyniki badań wykazały, że przy różnych obciążeniach quasi-statycznych rozkład wielkości cząstek po kruszeniu znacznie się różnił. Wraz ze wzrostem obciążeń quasi-statycznych zwiększał się udział mniejszych cząstek po kruszeniu, co wskazuje na wyższy stopień rozdrobnienia skupisk cząstek rudy. Dodatkowo, w miarę dalszego wzrostu obciążenia quasi-statycznego, średni rozmiar cząstek w skupiskach cząstek rudy zmniejszał się. Średni rozmiar cząstek stanowił bezpośrednią i intuicyjną miarę stanu rozdrobnienia skupisk cząstek rudy. Co więcej, skupiska cząstek rudy wykazywały fraktalne wzory przy quasi-statycznym obciążeniu, z fraktalnym wymiarem rozkładu wielkości cząstek w zakresie od 0,9205 do 1,3603 przy różnych obciążeniach quasi-statycznych. Wymiar fraktalny wzrastał wraz ze wzrostem obciążenia quasi-statycznego, wskazując na wyższy stopień kruszenia. Co więcej, wymiar fraktalny skupisk cząstek rudy podczas quasi-statycznego obciążenia wykazywał tendencję malejącą wraz ze średnim rozmiarem cząstek kruszenia. Badanie to przyczynia się do wszechstronnego zrozumienia charakterystyki fraktalnej związanej z quasi-statycznym pękaniem obciążeniowym skupisk cząstek rudy.
REFERENCES (18)
1.
Barrios et al. 2020 – Barrios, G., Jiménez-Herrera, N. and Tavares, L.M. 2020. Simulation of particle bed breakage by slow compression and impact using a DEM particle replacement model. Advanced Powder Technology 31(7), pp. 2749–2758, DOI: 10.1016/j.apt.2020.05.011.
2.
Cleary, P.W. and Sinnott, M.D. 2015. Simulation of particle flows and breakage in crushers using DEM: Part 1 – Compression crushers. Minerals Engineering 74, pp. 178–197, DOI: 10.1016/j.mineng.2014.10.021.
3.
Cai et al. 2016 – Cai, G.P., Xiao, X.H., Xu, Q. and Shen, J. 2016. Establishing Low-frequency Vibration Extrusion Crushing Energy Consumption Prediction Expression Based on Fractal Theory. Nonferrous Metals (Mineral Processing Section), pp. 58–62 (in Chinese).
4.
Evertsson, C.M. and Bearman, R.A. 1997. Investigation of interparticle breakage as applied to cone crushing. Minerals Engineering 10(2), pp. 199–214, DOI: 10.1016/S0892-6875(96)00146-X.
5.
Huang, D.M. 2007. Research on Working Mechanism and Working Performance Optimization of Compressive Crusher. Shanghai Jiao Tong University (in Chinese).
6.
Jiménez-Herrera et al. 2017 – Jiménez-Herrera, N., Barrios, G. and Tavares, L.M. 2017. Comparison of breakage models in DEM in simulating impact on particle beds. Advanced Powder Technology 29(3), pp. 692–706, DOI: 10.1016/j.apt.2017.12.006.
7.
Liu, J. and Schönert, K. 1996. Modelling of interparticle breakage. International Journal of Mineral Processing 44–45, pp. 101–115, DOI: 10.1016/0301-7516(95)00022-4.
8.
Liu et al. 2005 – Liu, H.Y., Kou, S.Q. and Lindqvist, P.A. 2005. Numerical studies on the inter-particle breakage of a confined particle assembly in rock crushing. Mechanics of Materials 37(9), pp. 935–954, DOI: 10.1016/j.mechmat.2004.10.002.
9.
Liu et al. 2013 – Liu, S., Xu, J.Y., Bai, E.L. and Gao, Z.G. 2013. Research on impact fracture of rock based on fractal theory. Journal of Vibration and Shock 32(05), pp. 163–166 (in Chinese).
10.
Li et al. 2019 – Li, C.J., Xu, Y., Zhang, Y.T. and Li, H.L. 2019. Study on energy evolution and fractal characteristics of cracked coal-rock-like combined body under impact loading. Chinese Journal of Rock Mechanics and Engineering 38(11), pp. 2231–2241 (in Chinese).
11.
Liu, R.Y. 2020. Study on mechanism and performance evaluation of cone crusher. University of Science and Technology Beijing (in Chinese).
12.
Nguyen et al. 2002 – Nguyen, A.Q., Husemann, K. and Oettel, W. 2002. Comminution behaviour of an unconfined particle bed. Minerals Engineering 15(1–2), pp. 65–74, DOI: 10.1016/S0892-6875(01)00201-1.
13.
Ning et al. 2018 – Ning, S., Yang, Y.L., Lv, J.K. and Duan, H.Q. 2018. The Fractal Characteristics of Coal Sample’s Fragments Subjected to Cyclic Loading. Geotechnical and Geological Engineering 37(15), pp. 2267–2281, DOI: 10.1007/s10706-018-0735-0.
14.
Tavares et al. 2021 – Tavares, L.M., Rodriguez, V.A., Sousani, M., Padros, C.B. and Ooi, J.Y. 2021 An effective sphere-based model for breakage simulation in DEM. Powder Technology 392, pp. 473–488, DOI: 10.1016/j.powtec.2021.07.031.
15.
Wu, R.J. and Li, H.B. 2019. Multi-scale failure mechanism analysis of layered phyllite subject to impact loading. Explosion and Shock Waves 39(190), pp. 108–117, DOI: 10.11883/bzycj-2019-0187 (in Chinese).
16.
Wang, Y.X. and Liang, W.M. 2020. Fractal Study on Influence of Impact Load on Microscopic Pore of Anthracite. Chinese Journal of High-Pressure Physics 34(157), pp. 134–144 (in Chinese).
17.
Zhang et al. 2017 – Zhang, Z.L., Ren, T.Z., Cheng, J.Y. and Jin, X. 2017. Research on the Inter-particle Breakage of Cone Crusher Considering the Characteristics of Particle Shape Transformation. Journal of Mechanical Engineering 53(16), pp. 173–180 (in Chinese).
18.
Zhang et al. 2017 – Zhang, Z.L., Ren, T.Z., Cheng, J.Y. and Jin, X. 2017. The improved model of inter-particle breakage considering the transformation of particle shape for cone crusher. Minerals Engineering 112, pp. 11–18.
| eISSN: | 2299-2324 |
| ISSN: | 0860-0953 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
