Khodayari, F & Diering, T 2022, 'Material flow simulation using marker mixing', in Y Potvin (ed.), Caving 2022: Fifth International Conference on Block and Sublevel Caving
, Australian Centre for Geomechanics, Perth, pp. 1387-1396, https://doi.org/10.36487/ACG_repo/2205_96
Material mixing and its impact on the block cave operations is one of the main aspects for evaluating and operating any caving project. Initial modelling with limited geotechnical data and calibration using the actual data are the two main phases to build the flow model. Challenges on the way are inevitable; how to predict the flow in the first place, and how to calibrate even when such data is available. In order to achieve realistic results, the right tools are a necessity to be able to mimic the actual cave. Vertical Mixing (VM), Template Mixing (TM), and CA3D tools in GEOVIA PCBC have been extensively used for more than two decades by many users across the globe; very useful tools but slow and limited on graphics. To overcome these challenges, Marker Mixing (MM) was introduced in 2021; a powerful material flow simulator with unique graphical capabilities and much higher speed compared to other existing tools. The stand-alone feature in MMIX provides the option to run a mixing model without even running a productions schedule. Implementing cave back and other geotechnical and operational constraints are easier and visually assessable, increasing the transparency while making it possible to improve the caving model in a more efficient approach. Markers simulate the material movements from their original location to extraction (from drawpoints) within the cave. Simulation is done step by step with vertical and horizontal movements, toppling, riling, erosion and frozen concepts all captured and visualised in the new MMIX tool. The theory and experience behind MMIX can help define the initial flow model, then in the next step, actual production data can be used to calibrate the parameters. In addition, information gathered from Beacons (smart markers) installed in the cave zone can be visualised, interpreted, and implemented using the MMIX play back tool. This feature can significantly improve the calibration process for achieving more precise models by mimicking the actual flow in the cave. The residual block model can also be generated using MMIX much faster and with better visual checks compared to CA3D. This paper introduces Marker Mixing tool, its features, and capabilities, with comparisons to existing Vertical Mixing, Template Mixing, and CA3D tools in PCBC.
Keywords: block caving, material flow, marker mixing, production schedule, PCBC
Diering, T 2022, GEOVIA PCBC (6.8.7) Caving Notes, GEOVIA, Dassault Systèmes.
Diering, T 2007, ‘Template Mixing: A Depletion Engine for Block Cave Scheduling’, APCOM 2007 – 33rd International Symposium on Application of Computers and Operations Research in the Mineral Industry, 2007, Santiago, Chile, Gecamin Ltd., Chile, pp. 313–320.
Khodayari, F & Pourrahimian, Y 2019, ‘Long-term production scheduling optimization and 3D material mixing analysis for block caving mines’, Mining Technology, vol. 128, no. 2, pp. 65– 76.
Diering, T, Ngidi, SN, Bezuidenhout, JJ & Paetzold, HD 2018, ‘Palabora Lift 1 block cave: understanding the grade behaviour’,
in Y Potvin & J Jakubec (eds), Caving 2018: Proceedings of the Fourth International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 91–106,
Khodayari, F, et al. 2019, “Production scheduling with horizontal mixing simulation in block cave mining”, Journal of Mining Science, Journal of Mining Science 55 (5), 789-80.
Khodayari, F, and Pourrahimian, Y 2015, ‘Mathematical programming applications in block-caving scheduling: a review of models and algorithms’, International Journal of Mining and Mineral Engineering, vol. 6, no. 3, pp. 234–257.
Pierce, M 2010, A Model for Gravity Flow of Fragmented Rock in Block Caving Mines, PhD thesis, Sustainable Minerals Institute, The University of Queensland, Brisbane.
Power, GR, & Campbell, AD 2016, ‘Modelling of real-time marker data to improve operational recovery in sublevel caving mines’, in C Carr & G Chitombo (eds), Proceedings of MassMin 2016, The Australian Institute of Mining and Metallurgy, Melbourne, pp. 105–109.
Sharrock, GB, Beck, D, Booth, G & Sandy, M 2004, ‘Simulating gravity flow in sub-level caving with cellular automata’, in A Karzulovic & A Alfaro (eds), Proceedings of MassMin 2004, Chilean Engineers Institute, Santiago, pp. 189–194.