Simulation of an underground cut and fill mine. A simulation approach using SimMine to determine the systems bottlenecks and the added value of additional miners in the production shift.

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Kemian tekniikan korkeakoulu | Master's thesis
European Mining Course
Degree programme
European Mining, Minerals and Environmental Programme (EMMEP)
As ore-bodies become more complex and difficult to extract, together with the increase towards clean energy technologies such as wind, solar, and energy storage requires more minerals. Mining operations need to increase their production efficiency and performance. It sounds simple, but finding a sustainable way of achieving continuous improvement is difficult in practice. A methodology that has been proven successful in other industries, is called the ‘theory of constraints’. This theory focusses on the improvement change, that will make the most positive difference, rather than making lots of small changes. Especially in underground operations, there are many factors limiting production. In the mining industry the constraints are considered as capacity bottlenecks, influencing the choice of the operating fleet and the usage of resources. It is essential to identify the real bottlenecks and then develop plans to mitigate the bottlenecks. The case study consist of a small underground mine with a small mining crew. The vehicle park is relatively large, and therefore it is necessary to establish the added value of additional miners or equipment for short-term production planning purposes, assuming that staff size currently limits production capacity to find out if staff size is indeed the bottleneck in the production capacity of the mine operation. When the bottlenecks of the mining system are known, it will be easier to focus on necessary areas and further implementations to improve the system. This research is aimed to fill the gaps in the literature, namely, determining the bottlenecks in an underground cut and fill gold mine, where ore material is only transported by truck. The TOC is a management framework used for improving system performances but doesn’t provide any detailed analytics tools. This study compared to others is unique because it uses a simulation study that considers: the blast cycle process, the in-mine ore and waste transport to the surface, and the operator size. The purpose of this study is to simulate the mining operation and identify the production constraints, and research the influence on the number of people with the assumption that staff sizes limit the production. The mine management is considering to add operators in the mine production. Currently, the mine is operating on a two-shift schedule with 11 or 12 people per shift. The mine wants to know if additional staff would increase the production of the mine with a lower cost per tonne. In order to investigate this problem, a literature study was conducted, and SimMine was selected as simulation software. The first step to this approach was building a simulation model based on the mining situation of 2019, and conducting time studies about relevant ongoing mining activities. The necessary input data was collected by conducting activity studies during the period of March till June of 2020 in the Kankberg-mine. By simulating the mining production cycle, using the size of the current machine park and the size of the production shift, it was determined that the number of trucks is the limiting factor within the production. Based on the discovered bottleneck, scenarios with different truck numbers and operators were simulated. The truck numbers used in the simulation study were ranging from 4 to 7, and the operator pool size was ranging from 10 to 15 people. Significant findings of this study are that with the current mine setup of 4 trucks, there would be no increase in production when adding operators. For the 24 scenarios the production increase was determined, the revenue change and the mining cost. By adding trucks and operators, a production increase of 19.38 % could be reached with 15 operator and 7 trucks. The optimal scenarios are determined by the highest revenue for the scenario and the lowest mining cost. The highest revenue of 124.9% can be found using 14 operators and 7 trucks, however the lowest mining cost can be found at 456 SEK/t using 12 operators and 7 trucks.
Serna, Rodrigo
Thesis advisor
Brussee, Ottomar
simulation, SimMine, theory of constrints, bottlneck
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