Reaaliaikainen riskinhallinta syvissä kaivoksissa

Metals and minerals have been extracted from the ground for thousands of years. As resources are utilized at ever growing rates, the remaining resources become more difficult to find and are situated deeper underground. Eight out of the ten deepest mines in the world are located in South Africa (e.g. -3.9 km Mponeng, -3.7 km Savuka, -3.5 km TauTona). The last two mines (-3.0 km Kidd Creek and -2.5 km Creighton) are located in Canada. The deepest metal mine in Europe is the Pyhäsalmi mine (-1.5 km) in Finland. Using geophysics we know that the orebodies of some Finnish mines, such as Kemi, extend kilometers downwards. Mining activities at great depths are limited by technical, economic, health and safety issues. Deep mining poses new risks: high temperatures (over 50 °C), high stresses (up to 100 MPa), induced seismicity (M4 or more) and mine stability threats (e.g. loss of lives in Tautona Mine in September 2015).
The primary objective of the On-Line Risk Management in Deep Mines (ORMID) project is to understand how the fracture process zone develops ahead and around the mining excavations. The secondary objective is to use this knowledge to mitigate the geotechnical risks in deep mining, prevent loss of life and loss of equipment. The tertiary goal is to promote and advance the development of suitable research instruments capable of on-line monitoring. The work is divided into three work packages:
• Development of a theoretical basis for the formation of the damage zone ahead and around of mining excavations to support geotechnical risk mitigation.
• Development of on-line monitoring equipment to predict the strain and stress state development.
• Develop and install instrumentation for on-line monitoring of deep underground mines.
Aalto rock mechanics team has developed two key components essential for the research: the geotechnical risk assessment framework and a rapid numerical approach capable of on-line back-calculation of stresses. Finland has the deepest metal mine in Europe and Canada has one of the deepest mines in the world. An understanding of stresses and the damage process surrounding the mines and how to convert it into changes in the mechanical parameters is needed. Queen’s mine design team has a deep understanding of destressing methods to overcome stress driven problems. Aalto has been doing research on risk management procedures and has developed a method to measure rock stress changes. Together these technologies enable the advancement of smart mining technologies in deep mines.
As a result of the whole research project, we will have connected the existing geotechnical risk assessment and classification to the on-line sensor-based analysis. We will have connected geophysical input into correlating rock mechanical properties and further developed rock mass failure modes. The existing back-calculation method will be further developed to accept geophysical input and to account for plasticity. A set of equipment able to be connected to the existing mine sensor network and to operate under harsh conditions providing on-line back-calculation of the observed rock mass state will be tested. The results put together allow mines to extend the coverage of their current sensor network and to automate the geotechnical risk management. The produced method can be then adjusted according to the mine’s local needs. This research will combine qualitative hazards identification with quantitative hazard likelihood assessment and will enable quicker risk assessments and better risk representation. The results of this study will help reduce risk of geotechnical accidents in underground mines and will help draft a robust risk management system in the mine. Real-time evaluation of data will reduce decision-making time and will help give feedback on the efficiency of the support systems in place. Pilot trials for alternate mining methodology or sequencing can then be carried out to evaluate its potential. This will also help to integrate mine planning with risk planning from very early stages of mining activity. Improved data will help to understand better mining environment and to reduce mining costs by optimised rock support and by providing safer working environment.