Adaptive Multi-Size Block Modeling for Mineral Resources and Ore Reserves Evaluation

Khorram, F.; Asghari, O.; Memarian, H.; Hoseein Morshedy, A.; Emery, X. M.

doi:10.22044/jme.2021.10596.2010

Document Type : Original Research Paper

Authors

¹ School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

² Faculty of Mining and Metallurgical Engineering, University of Yazd, Yazd, Iran

³ Department of Mining Engineering, University of Chile, Advanced Mining Technology Center, Chile

https://doi.org/10.22044/jme.2021.10596.2010

Abstract

The key input parameters for mine planning and all subsequent mining activities is based on the block models. The block size should take into account for the geological heterogeneity and the grade variability across the deposit. Providing grade models of smaller blocks is more complex and costly than larger blocks, but larger sizes cannot represent areas with high spatial variability accurately. Hence, a unique block size is not an optimal solution for modeling a mine site. This paper presented a novel algorithm to create an adaptive block model with locally varying block sizes aiming to control dilution and ore loss in Sungun porphyry copper deposit of Iran with a complex geometry characterized by multiple dikes. Three grade block models with different block sizes and simulated by direct block simulation are the inputs of algorithm. The output is a merged block model, assigning the smaller blocks to the complex zones, such as ore-waste boundaries, and larger blocks to the continuous and homogeneous zones of the ore body. The presented algorithm is capable to provide an accurate spatial distribution model with a fewer number of blocks in comparison to the traditional block modeling concepts.

Keywords

20.1001.1.22518592.2021.12.2.4.1

References

[1]. Yasrebi, A.B., Wetherelt, A., Foster, P., Afzal, P. and Ahangaran, K. (2015). Determination of an optimum voxel size based on statistical methods in the Kahang Cu porphyry deposit, central Iran. Journal of Mining and Metallurgy, 51A(1): 21–27.

[2]. Abdollahi Sharif, J., Jafarpour, A. and Yousefi, S. 2020. A Hybrid Fuzzy MCDM Approach to Determine an Optimal Block Size in Open-Pit Mine Modeling; Journal of Mining and Environment. 11 (2): 611-627.

[3]. Heidari, S.M. (2015). Quantification of Geological Uncertainty and Mine Planning Risk using Metric Spaces (Ph.D Thesis) The University of New South Wales, Sydney, Australia.

[4]. Dominy, S.C., Platten, I.M., Xie, Y. and Minnitt, R.C.A. (2010). Underground grade control protocol design: case study from the Liphichi gold project, Larecaja, Bolivia. Applied Earth Science. 119 (4): 205–219.

[5]. Iliyas, N. and Madani, N. (2019). Block Modelling based on Grade Domaining: Is It Reliable? In: Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection MPES 2019; Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG), pp. 183–190.

[6]. Câmara, T.R., Leal, R.S., Peroni, R.D.L. and Capponi, L.N. (2019). Controlling operational dilution in open-pit mining, Mining Technology. 128 (1): 1–8.

[7]. Ramirez-Ruiseco, J. and Kumral, M. (2016). A practical approach to mine equipment sizing in relation to dig-limit optimization in complex ore bodies: multi-rock type, multi-process, and multi-metal case. Natural Resources Research. 26 (1): 23–35.

[8]. Richmond, A.J. and Beasley, J.E. (2010). Financially efficient dig-line delineation incorporating equipment constraints and grade uncertainty. International Journal of Surface Mining, Reclamation and Environment. 18 (2): 99–121.

[9]. Hekmat, A., Osanloo, M. and Moarefvand, P. (2013). Block size selection with the objective of minimizing the discrepancy in real and estimated block grade. Arabian Journal of Geosciences. 6 (1): 141–155.

[10]. Saleki, M., Kakaie, R. and Ataei, M. (2019).Mathematical relationship between ultimate pit limits generated by discounted and undiscounted block value maximization in open pit mining. Journal of Sustainable Mining. 18 (2):94-99.

[11]. Smith, C.B., Nehring, M. and Bosompem, M.K. (2014). A critique of selective mining unit sizing at Century Mine to optimise productivity with dilution. Journal of Research Projects Review. 3 (1): 63-68.

[12]. Flores, G. and Catalan, A. (2019). A transition from a large open pit into a novel “macroblock variant” block caving geometry at Chuquicamata mine, Codelco Chile, Journal of Rock Mechanics and Geotechnical Engineering, 11(3): 549-561.

[13]. Pars Olang Engineering Consultant Company. (2006). Modeling and Reserve Estimation Report of Sungun Copper Mine, Tehran.

[14]. Peattie, R. and Dimitrakopoulos, R. (2013). Forecasting recoverable ore reserves and their uncertainty at Morila Gold deposit, Mali: An efficient simulation approach and future grade control drilling. Mathematical Geosciences. 45: 1005–1020.

[15]. Boucher, A. and Dimitrakopoulos, R. (2009). Block simulation of multiple correlated variables. Mathematical Geosciences. 41 (2): 215–237.

[16]. Birch, C. (2017). Optimization of cut-off grades considering grade uncertainty in narrow, tabular gold deposits, Journal of the Southern African Institute of Mining and Metallurgy. 117 (2):149-156.

[17]. Rondon, O. (2016). Resource model dilution and ore loss: a change of support approach. In Geostatistics Valencia. Springer, Quantitative Geology and Geostatistics book series (QGAG, volume 19), pp. 345–355.

[18]. Chiquini, A. and Deutsch, C. (2020). Mineral Resources Evaluation with Mining Selectivity and Information Effect, Master of Science Thesis, University of Alberta, 98 pp.

[19]. Martinelli, R., Collard, J. and Gamache, M. (2020). Strategic planning of an underground mine with variable cut-off grades, Optimization and Engineering 21: 803–849.

[20]. Amirá, R., Morales N. and Cácere, A. (2019). Analysis of the Impact of the Dilution on the Planning of Open-Pit Mines for Highly Structural Veined-Shaped Bodies, Journal of Sustainable Mining 18(2):94-99.

[21]. Thakur, M., Samanta, B. and Chakravarty, D. (2014). Support and information effect modeling for recoverable reserve estimation of a beach sand deposit in India. Natural Resources Research. 23 (2): 231–245.

[22]. Coombes, J., Tran, T. and Earl, A. (2019). Going local innovative resource estimates to improve investment decisions, Mineral processing and extractive metallurgy. 129 (1): 1–11.

[23]. Hosseini, S.A., Asghari, O. and Emery, X. (2017). Multivariate simulation of block-support grades at Mehdiabad deposit, Iran, Applied Earth Science. 126 (3): 146–157.

[24]. Hezarkhani, A. (2006). Petrology of the intrusive rocks within the Sungun porphyry copper deposit, Azerbaijan, Iran. Asian Journal of Earth Sciences. 27 (3): 326–340.

Adaptive Multi-Size Block Modeling for Mineral Resources and Ore Reserves Evaluation

References

References

Volume 12, Issue 2April 2021Pages 339-350

Volume 12, Issue 2
April 2021
Pages 339-350