Controlling short-term deviations from production targets by blending geological confidence classes of reporting standards

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Insinööritieteiden korkeakoulu | Master's thesis
European Mining Course
Degree programme
Master's Programme in Tranportation and Environmental Engineering
Meeting short-term production targets is desired by many companies, since this would enable them to finetune the processing operation,meet budget plans and obey contract requirements. Recently stochastic optimization solutions have been developed requiring geostatistical simulations as input. The significant value added has been demonstrated, however, an operational implementation of such approaches for day-to-day use is complex and seems currently difficult as it requires expert knowledge and extensive computational capacity. To control the short-term deviations, a new fast metaheuristic scheduler is developed that blends Geological Confidence Classes (GCC’s) from resource reporting standards. For the scheduler, a new penalty function is developed to schedule for a target blend of GCC’s and a new method is developed to enforce smooth mining patterns in three dimensions. The metaheuristic solver uses a Genetic Algorithm and an Ant Colony Optimization algorithm to efficiently converge towards the Pareto optimum. To establish an optimal blend of GCC’s, a methodology is developed which creates a range of equally probable scenarios of deviations from production targets for different blends of GCC’s. A least-squares estimate can be fitted to these scenarios at the required level of confidence to determine the optimal blend for a maximum allowed deviation. An historical world class gold deposit is used to show that the monthly and quarterly deviations can be controlled by blending GCC’s. Furthermore, the case study shows the possibility to establish an optimal blend of GCC’s by using the developed methodology. The scheduler proofs to be able to efficiently create and evaluate schedules to blend the GCC’s for this case study. For a maximum quarterly deviation of 15% at a 90% confidence level, the established optimal blend is 59% ore tonnage classified as measured resources. For the monthly deviations, a maximum of 15% is too low and cannot be met at a 90% confidence level.
Buxton, Mike
Thesis advisor
Benndorf, Joerg
mine planning, resource reporting standards, metaheuristics, deviations production