Abstract
Conventional mine planning approaches use an estimated orebody model as input to generate optimal production schedules. The smoothing effect of some geostatistical estimation methods cause most of the mine plans and production forecasts to be unrealistic and incomplete. With the development of simulation methods, the risks from grade uncertainty in ore reserves can be measured and managed through a set of equally probable orebody realizations. In order to incorporate grade uncertainty into the strategic mine plan, a stochastic mixed integer programming (SMIP) formulation is presented to optimize an underground cut-and-fill mining production schedule. The objective function of the SMIP model is to maximize the net present value (NPV) of the mining project and minimize the risk of deviation from the production targets. To demonstrate the applicability of the SMIP model, a case study on a cut-and-fill underground gold mining operation is implemented.
Highlights
Mine production scheduling is the determination of the sequence and processing of mineralized material that maximizes the overall net present value subject to technical and operational constraints, while shielding the mining operation from risk [1], [2]
In the last few years, the average ore grade shows a trend of fluctuating downward below the lower bound because the overall ore quality decreases in the remaining deposit. These results demonstrate both schedules from stochastic mixed integer programming (SMIP) and conventional optimization method (CM) could provide a reasonable, reliable, and implementable solution for the underground mining project
Even though the total metal content extracted by the SMIP schedule is less, the higher net present value (NPV) is as a result of the SMIP model scheduling consistently higher grades earlier in the mine life compared to the CM model
Summary
Mine production scheduling is the determination of the sequence and processing of mineralized material that maximizes the overall net present value subject to technical and operational constraints, while shielding the mining operation from risk [1], [2]. S. Huang et al.: SMIP Framework for Underground Mining Production Scheduling Optimization Considering Grade Uncertainty net present value (NPV) within acceptable technical and operational constraints. As the main input of mine optimization models, the uncertainty of grade estimation can affect the metal content of blocks as well as economic block values This will impact the production plan and NPV of the mining operation. Based on Ramazan and Dimitrakopoulos’s approach [16], Carpentier et al [20] presented a SIP model with recourse optimization formulation to generate the optimal longterm schedule for an underground mining project extension while considering geological uncertainty, economically minable lenses, and cut-off grade. This research seeks to develop a riskbased optimization framework using stochastic mixed integer programming (SMIP) that effectively integrates grade uncertainty into the optimization of long-term production scheduling in underground cut-and-fill mining. G the geological discount rate; used to control the risk distribution over time
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
