Determination of the transition point from open pit to underground mining Strategic Mine planning and Optimisation for Combination Mining Method J Chung, M Asad, E Topal, AK Ghosh Department of Mining Engineering and Metallurgical Engineering Western Australian School of Mines, Curtin University Western Australia The Ninth AusIMM Open Pit Operators Conference 2016 2016 Kalgoorlie, Western Australia
Presentation Outline 1. Introduction 2. Problem Definition & Objectives 3. Underground Mining System 4. Modelling the Transition Problem 5. Case Study 6. Conclusions & Recommendations 7. References Ninth Open Pit Operators Conference 2016 Page 2
Introduction Haulage cost and stripping ratio in OP mining will increase when the pit goes deeper. As the stripping cost goes over UG mining cost and OP mining becomes uneconomical, UG mining emerges as a viable option. Transition from OP to UG required. Ninth Open Pit Operators Conference 2016 Page 3
Introduction: Combination Mining In combination mining, all involved mining strategies need to be considered simultaneously in the mine planning and optimisation process to ensure global optimality achieved. 4
Introduction Transition Problem is the determination of the optimal transition point with the aim of maximisation of project s value and resource utilisation. Transition Point is where the decision has to be made whether expand the pit or make the transition from OP to UG. Page 5
Problem Definition & Objectives Conservative/Simplest approach: The transition is considered near or after the exhaustion (secondary) of the available reserves inside the ultimate pit (Finch 2012). Disadvantages: 1. UG mining could have been the optimal strategy for some of the OP reserves, but were planned for OP mining 2. Evaluates OP and UG mining options separately 3. Ignores the variation in mining layout from one UG mining method to the other. 4. Defines the crown pillar (CP) is an arbitrary location. Ninth Open Pit Operators Conference 2016 Page 6
Problem Definition & Objectives Objectives of this study are: Present an implementation of an integer programming (IP) based mathematical model Evaluates possible variations in transition point from OP to UG mining for sublevel stoping and block caving methods. Demonstrates the impact of OP to UG mining strategies and different UG mining methods on the overall value of the project. Ninth Open Pit Operators Conference 2016 Page 7
UG Mining System Block Caving Mining Method High production rate and low mining cost Highly depend on cave-ability of the ore and host rock Dilution Costly if caving cannot be maintained Stoping Mining Method Minimum dilution if hanging wall is strong Stopes can be filled with waste rock, paste fill to recover pillar Early production is possible Safe working environment Ninth Open Pit Operators Conference 2016 Page 8
Mathematical Modelling for Transition Problem IP model for OP & UG stoping combined method: Objective function: Maximises the undiscounted profit from both OP mining and UG mining. Constraints: (i) OP slope or block precedence constraint (ii) UG mine design constraints (iii) Reserve restriction constraints (iv) CP design constraint that ensures the placement of CP is underneath the pit (v) The provision of required number of level needed for CP is in accordance to the geotechnical requirement. Ninth Open Pit Operators Conference 2016 Page 9
Mathematical Modelling for Transition Problem IP model for OP & UG block caving combined method: Objective function: Maximises the undiscounted profit from both OP mining and UG mining. Constraints: (i) OP slope or block precedence constraint (ii) UG mine design constraints (iii) Reserve restriction constraints (iv) CP design constraint that ensures the placement of CP is underneath the pit (v) The provision of required number of level needed for CP is in accordance to the geotechnical requirement. Ninth Open Pit Operators Conference 2016 Page 10
Mathematical Modelling for Transition Problem Issues: Size Big data handling Computer/Hardware capability What if they are solved: Accuracy Precision Effectiveness and efficiency Adapting to Change Ninth Open Pit Operators Conference 2016 Page 11
Case Study Case study profile and parameters Three-dimension (3-D) hypothetical gold deposit. 41,472 blocks with block size of 25m 25m 25m. Design stope size 2 2 2 blocks. Two levels need to be retained as crown pillar. IP problem written by Microsoft Visual Basic (VB.net) and solved by using CPLEX solver. Ninth Open Pit Operators Conference 2016 Page 12
Result Discussions Scenarios IP model results ($billion) Scenario 1: OP UG stoping method 21.657 Scenario 2: OP UG block caving method 26.020 Scenario 3: OP mining method only 18.396 Scenario 4: UG stoping method only 12.541 Scenario 5: UG block caving method only 13.123 Ninth Open Pit Operators Conference 2016 Page 13
Case Study OP-UG stoping and OP-UG block caving methods have the highest values. CP for scenario 1 is at Level 17-18 and scenarios 2 is Level 16-17. OP-UG block caving generates a higher value -- low mining cost, high production rate and economy of scale. Proved that if the deposit can be mined through a combination mining method, optimality can be achieved through strategic mine planning. Ninth Open Pit Operators Conference 2016 Page 14
Case Study If OP mining is selected for the shallow deposit without considering the potential transition to UG mining, the ultimate pit will extend deeper than the final pit generated. The IP model includes opportunity cost of all available mining strategies. Avoid the delays in production during the transition plan the development in the early stage. Maximised resource and reserve utilisation. Ninth Open Pit Operators Conference 2016 Page 15
Conclusions & Recommendations OP, UG and CP concurrently during the strategic mine planning strategy is important global optimisation. UG mining method selection plays an important role in combination method as it will affect the mining layout and project s value directly. IP models are presented to optimise the mine planning of combination mining method Ninth Open Pit Operators Conference 2016 Page 16
Conclusions & Recommendations Technical limitations: Production rate, Equipment requirements Variation of labour skills Limitations: Timing of transition: Production scheduling Problem size reduction strategy: Nature of IP model Ninth Open Pit Operators Conference 2016 Page 17
References 1. Alford, C, 1995. Optimization in underground mine design, in Proceedings of 25th International APCOM Symposium 1995, pp 213-218 (Australasian Institute of Mining and Metallurgy,Melbourne: Brisbane, Australia). 2. Asad, M and Topal, E, 2011. Production scheduling of open pit mining operations through cutoff grade optimization. South African Institute of Mining and Metallurgy, 111(11):741-750. 3. Bakhtavar, E and Shahriar, K, 2007. Optimal ultimate pit depth considering an underground alternative, in Proceedings of Fourth AACHEN International Mining Symposium-High Performance Mine Production 2007, pp 213-221 (AIMS: Germany). 4. Bakhtavar, E, Shahriar, K and Mirhassani, A, 2012. Optimization of the transition from open-pit to underground operation in combined mining using (0-1) integer programming. J. South. Afr. Inst. Min. and Metall., 112(12):1059-1064. 5. Brazil, M, Thomas, D A, Weng, J F, Rubinstein, J H and Lee, D H, 2005. Cost optimisation for underground mining networks. Optimization and engineering, 6(2):241-256. 6. Camus, J P, 1992. Open pit optimization considering an underground alternative, in Proceedings of 23th International APCOM Symposium 1992, pp 435-441 (SME: Tucson). 7. Chung, J, Topal, E and Erten, O, 2015. Transition from open-pit to underground - using integer programming considering grade uncertainty, in The 17th annual conference of the International Association for Mathematical Geosciences 5-13 September 2015 2015, (Schaeben, H, Delgado, R T, Boogart, K G and Boogart, R), pp 268-277 (IAMG: Freiberg, Germany). 8. Chung, J, Topal, E and Ghosh, A G, in press. Where to make the transition from open-pit to underground? - using integer programming. South African Institute of Mining and Metallurgy. 9. Dagdelen, K and Traore, I, 2014. Open pit transition depth determination through global analysis of open pit and underground mine scheduling, in Orebody Modelling and Strategic Mine Planning 24-26 November 2014 2014, (Dimitrakopoulus, R), pp 195-200 (The Australasian Institute of Mining and Metallurgy: Perth, Australia). 10. Dimitrakopoulos, R, Martinez, L and Ramazan, S, 2007. A maximum upside/minimum downside approach to the traditional optimization of open pit mine design. Journal of Mining Science, 43(1):73-82. 11. IBM CPLEX Optimization Solver, 2013. Version 12.6. IBM CPLEX ILOG Corp. 12. Johnson, T B, 1968. Optimum open pit mine production scheduling. Berkeley: DTIC Document. 13. Lerchs, H and Grossman, F I, 1964. Optimum design of open-pit mines, in Operations research 1964, pp. 14. Opoku, S and Musingwini, C, 2013. Stochastic modelling of the open pit to underground transition interface for gold mines. Int. J. Min. Reclam. and Environ., 27(6):407-424. 15. Soderberg, A and Rausch, D O, 1968. Pit planning and layout, pp 142-143 (The American Institute of Mining, Metallurgical, and Petroleum Engineers, Inc: New York). 16. Topal, E and Ramazan, S, 2012. Strategic mine planning model using network flow model and real case application. International Journal of Mining, Reclamation and Environment, 26(1):29-37. Ninth Open Pit Operators Conference 2016 Page 18
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Introduction Mine Planning & Optimisation Exploration stage Block model generation Determination appropriate mining method Search mineralization zone Geological block model Economic block model Shallow deposit Deep deposit Near surface orebody extend vertically to a considerable depth Open pit mining method Underground mining method Combination mining method Transition problem Transition point Ninth Open Pit Operators Conference 2016 Page 20