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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Benndorf, Joerg
dc.contributor.author Bijmolt, Martijn
dc.date.accessioned 2016-12-08T13:27:12Z
dc.date.available 2016-12-08T13:27:12Z
dc.date.issued 2016-11-21
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/23616
dc.description.abstract 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. en
dc.format.extent 71+2
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Controlling short-term deviations from production targets by blending geological confidence classes of reporting standards en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Insinööritieteiden korkeakoulu fi
dc.subject.keyword mine planning en
dc.subject.keyword resource reporting standards en
dc.subject.keyword metaheuristics en
dc.subject.keyword deviations production en
dc.identifier.urn URN:NBN:fi:aalto-201612085807
dc.programme.major European Mining Course fi
dc.programme.mcode R3008 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Buxton, Mike
dc.programme Master's Programme in Tranportation and Environmental Engineering fi


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