New geometallurgical model results in more efficient mines

A precise model that can be used to plan the optimal way to extract and process an ore is deemed crucial for efficient and sustainable mining operations. Researchers at Luleå University of Technology have now developed a geometallurgical model for the Garpenberg mine that can be used to show the distribution of metals and minerals more clearly in orebodies, and thereby optimise extraction. The model can be applied to other mines, and researchers are now collaborating with Boliden with the aim of implementing and further developing the model.

“We are proud that our research has had such a large impact and that several mining companies around the world are showing interest in our results. The extraction of different valuable metals from the same orebody places high demands on ore characterisation and process development – and we have succeeded in developing a model that can facilitate this”, says Glacialle Tiu, a researcher in ore geology at Luleå University of Technology, who has recently defended her thesis on the subject.

Glacialle Tiu and her research colleagues have focused on the Garpenberg mine close to Hedemora, where Lappberget is one of the most important orebodies for the production of base metals in Europe. The research has been conducted in close collaboration with Boliden who runs the mine. Lappberget is a so-called polymetallic orebody, that is to say an ore containing several different valuable base- and noble metals, and this makes the optimisation of the enrichment process more complicated.

Glacialle Tiu has based her work on the three-dimensional mineral resource models that are currently being developed by the mine’s geologists. These form the most important basis for estimating the value of the ore as well as planning the extraction and enrichment processes. A weakness with these models is that they often only contain information about the concentrations of different metals in the ore, and not information about the ore’s mineralogical or bedrock geological characteristics. These can have significant implications for how the ore will behave during extraction and enrichment, which in turn can lead to unexpected ore losses or that the extraction is more difficult, and therefore more expensive, than expected. There is therefore widespread agreement in the mining sector that traditional ore models need to become more detailed in order to optimise the whole mining value-chain, from exploration and extraction, to mineral processing, smelting, and environmental work.

One challenge directly related to this is the need to create geometallurgical models that can link together the entire value-chain. This challenge was the starting point for Glacialle Tiu’s work with creating a more detailed mineral resource model. She has developed a geometallurgical model that improves the follow-up between the mine and the ore beneficiation process. This means that we can achieve a better characterisation of the ore already before extraction, and this helps optimise the extraction and beneficiation process. She has developed this detailed geometallurgical model by integrating a comprehensive understanding of the mineralogical distribution of the ore’s components at different scales, through the use of mineral processing tests. These tests confirm that the variation in hardness, texture, and mineralogy of the ore control how efficiently the ore minerals can be concentrated into mineral concentrates in the processing plant. Through this work, Glacialle has been able to group the ore into new domains where each domain has a specific relationship between the ore’s characteristics and their technical beneficiation performance.

With the help of this new model, mining companies can in a better way predict how the ore will behave in the plant and therefore be able to make the process more efficient. The complex ore from the Garpenberg mine used in this study contained not only base- and noble metals but also metals present on the EU’s list of critical raw materials. These metals are required in modern technology. Currently, the EU relies heavily on the import of these metals as they are missing in domestic production, something that can result in supply risks.

With this new model, mining companies can also explore the possibility of extracting these critical metals from the ore. This, together with increased efficiency, can lead to a reduction in the amount of metals ending up in mining waste, something that is both environmentally and economically positive.

Glacialle Tiu, along with her research colleagues, will now work with further developing the model together with Boliden to make it even more complete and to automate parts of the process. Glacialle’s research provides the Garpenberg mine with a better understanding of the different types of data that need to be collected in order to make the process more efficient, a sort of blueprint. Future research will focus on identifying ways to build up databases that can facilitate the integration of information from different parts of the mining operations, by utilising smart sensor technology to gather data in real-time. Machine learning and AI are important components of this continued work towards creating a so-called digital twin of the model and the entire mining process.

You can read more about the research behind the model in Glacialle Tiu’s doctoral thesis ”Geometallurgy of a complex ore: Lappberget Zn-Pb-Ag-(Cu-Au) deposit, Garpenberg mine, Sweden”.

Glacialle Tiu’s doctoral thesis:
Geometallurgy of a complex ore: Lappberget Zn-Pb-Ag-(Cu-Au) deposit, Garpenberg mine, Sweden External link.