A Mine-to-Mill opportunity for copper block cave mines, referred to as Cave-to-Mill, integrates underground production scheduling with fragment size and geo-metallurgical ore characteristics. Cave-to-Mill is envisioned to be an important design and operational strategy for block cave mines. Linkages between key cave and mill parameters have been established so that coordinated efforts towards maximizing Net Present Value (NPV) can be made.
The figure below shows how key aspects of a caving operation are linked so that a coordinated effort can be made to maximize Net Present Value (NPV). The presented Cave-to-Mill strategy focuses on its application to copper porphyry deposits, as the majority of present and future caving operations are based on excavating copper ores. Focus areas and enabling technologies, highlighted in blue and green respectively, represent significant opportunities to improve the productivity of block caving operations by implementing them in Cave-to-Mill.
The major objective of adjusting design and operational parameters within Cave-to-Mill is to maximize project value. A central component is the ore block model, which is continuously refined during project development and operation through input of geotechnical, geological and metallurgical information. At the exploration stage, initial access to an orebody is generally provided through exploratory drilling, allowing block models and design parameters to be established through analysis of core samples. As projects progress towards development, excavations and boreholes provide access to additional sample and data, which can be used to repopulate block models. Following plant commissioning, logs of mine and mill performance, such as fragment size measurements and specific energy consumption of mill processes, can serve to calibrate predictive models and to refine block models.
Key areas of research focus are:
Discrete Fracture Network (DFN)
DFN based methods were found to hold considerable value within the Cave-to-Mill approach. The variable and relatively uncontrollable nature of cave fragmentation is considered to be a key distinguishing feature of Cave-to-Mill when compared with typical Mine-to-Mill strategies for open-pit mines. Further development of Cave-to-Mill is aimed at relating simulated fracture intensity to variations in lithology and extending the use of Synthetic Rock Mass (SRM) methods.
Rock Mass Characterization using Discrete Fracture Networks
Rock mass characterization methods are essential to block cave design. In addition to empirical approaches, the use of Discrete Fracture Network (DFN) based methods to characterize the fracture system of rock masses is an integral part of Cave-to-Mill.
Fragmentation Modeling In the context of Cave-to-Mill
DFN models also provide considerable value as a means to predict cave fragmentation; a critical input parameter for predicting flow geometry, drawpoint availability and crushing/grinding requirements.
Fragmentation Measurement
There have been considerable efforts directed towards modeling cave fragmentation, whilst comparatively little work has been carried out towards improving the accuracy of measuring fragmented material located at muck piles. For this reason, there is a clear lack of comparisons of fragmentation model predictions and actual size measurements from cave operations.
Production Schedules
The production schedule, which refers to the schedule of tonnage taken from specific draw-points, is an aspect of block caving which serves as an important input to a Cave-to-Mill strategy. Although schedules are constrained by aforementioned cave stresses, flow geometry and cave geometry, they do provide the ability to control properties of the plant feed that affect mill performance when considered as a means to blend ore types in a fashion that increases mill productivity.
Metallurgical Characterization
A major objective of Cave-to-Mill is to populate ore block models with key mill performance parameters. Performance parameters include those that impact sensor based sorting as well as downstream comminution and mineral separation. Characterizing in-situ heterogeneity is a first step towards assessing sortability. Footprint specification and production scheduling can then be carried out with a greater confidence in assigned Net Smelter Return (NSR) block values. The following figures shows a high degree of heterogeneity from draw-points at the 400 ton scale that has potential to allow sorting and grade control from the cave.
Lithology and Grade Classification using Sensors
Due to the nature and design of cave mines, they lend themselves to automation such as via the incorporation of sensors systems to support planning and operational decisions. These systems can be used to develop operational strategies that focus on Cave-to-Mill objectives such as maximizing the NPV. There is a degree of heterogeneity in all ore deposits related to variations in fragmentation as described above as well as variations in grades. The amount of dilution will vary depending on a number of geological factors such as lithological rock types of a deposit as well we design criteria such as cut-off grade. Where heterogeneity exists, there is an opportunity to use sensors to classify and sort ore and waste.
References
- Nadolski, S., M. Munkhchuluun, B. Klein, D. Elmo, and Craig Hart. “Cave fragmentation in a cave-to-mill context at the New Afton Mine part I: fragmentation and hang-up frequency prediction.” Mining Technology 127, no. 2 (2018): 75-83.
- Nadolski, Stefan, Christopher O ‘Hara, Bern Klein, Davide Elmo, and Craig JR Hart. “Cave fragmentation in a cave-to-mill context at the New Afton mine Part II: implications to mill performance.” Mining Technology (2018): 1-12.
- Klein, B., Erdenebat, E., Nadolski, S., Samuels, M. (2018). Assessing Ore Heterogeneity for Bulk Sorting at the New Afton Copper Mine. 8th International Conference on Sensor-Based Sorting & Control (SBSC 2018), Aachen, Germany.
- Munkhchuluun, M., Elmo, D., Nadolski, S., Moss, A. & Klein, B. (2018). Presence of natural fracture as an indicator of operational difficulty for cave operations, in Y Potvin & J Jakubec (eds), Proceedings of the Fourth International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, Australia, pp. 261-270.
- Nadolski, S., Klein, B., Hart, C.J.R., Moss, A. and Elmo, D. (2018). An approach to evaluating block and panel cave projects for sensor-based sorting applications, in Y Potvin & J Jakubec (eds), Proceedings of the Fourth International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, Australia, pp. 133-140.
- Nadolski, S., Klein, B., Samuels, M., Hart, C. and Elmo, D. (2018). Evaluation of Cave-to-Mill opportunities at the New Afton Mine, in Proceedings of the 50th Annual Meeting of the Canadian Mineral Processors (2018), Ottawa, Canada.
- Nadolski, S., Munkhchuluun, M., Elmo, D. and Klein, B. (2017). A Piecewise Linear Interpolation Algorithm to reduce the uncertainty of cumulative fracture intensity plots for Discrete Fracture Network modelling, in 15th International Conference of the International Association for Computer Methods and Advances in Geomechanics (15th IACMAG), Wuhan, China.
- Nadolski, S., Munkhchuluun, M., Elmo, D. and Klein, B. (2017). Characterization of Rock Mass Fragmentation for Cave Mining. In 51st US Rock Mechanics/Geomechanics Symposium (ARMA 2017), San Francisco, California, USA.
- Nadolski, S., Liu, Y., Klein, B., Elmo, D., Scholar, J. & Scoble, M. (2016). Investigation into the Implementation of Sensor-based Ore Sorting Systems at a Block Caving Operation. In 7th International Conference & Exhibition on Mass Mining, Sydney, Australia.
- Nadolski, S., B. Klein, D. Elmo, and M. Scoble. “Cave-to-Mill: a Mine-to-Mill approach for block cave mines.” Mining Technology 124, no. 1 (2015): 47-55.
- Liu, Y., Nadolski, S., Elmo, D., Klein, B., & Scoble, M. (2015). Use of Digital Imaging Processing Techniques to Characterise Block Caving Secondary Fragmentation and Implications for a Proposed Cave-to-Mill Approach. American Rock Mechanics Association.