A fundamental principle of geometallurgy is to test a large number (high volume) of samples and to relate metallurgical response to ore properties. The large number of samples and tests typically results in large datasets that contain valuable data. Related disciplines in the mining value chain, such as geology and mining engineering, also manage large data sets; however, these disciplines have a wide range of commercial software applications tailored for their specific data types. There are no commercial applications specifically tailored to managing geometallurgical and mineralogical data. One reason for this is the lack of standardization in the reporting of geometallurgical and mineralogical test results which can create a barrier to the development and commercialization of data management applications.
The development of common data reporting guidelines for geometallurgical and mineralogical data is the first step to unlock greater value from geometallurgical data through better data management, interoperability, efficient data communication, and more efficient and effective data retention and use.
Once data is organized and stored in a database it removes barriers for a wide range of analysis and visualization tools and methodologies, including collaborative innovation, embedded reconciliation and continuous improvement, and machine learning and AI. Data reporting guidelines can improve the quality of the available data, make the industry more efficient, and is a key enabler for downstream data analysis and decision making.
The objective of this project is to develop data reporting guidelines, including templates and user guides for common geometallurgical and mineralogical tests, as a key enabler for efficient consumption of data. The guidelines are intended to include terminology, representation, suggested format, definition, structuring, and tagging. The expectation is to develop reporting guidelines to cover the most common comminution, flotation, leaching, mineralogy (X-Ray Diffraction, automated mineralogy), and hyperspectral analysis forms of data. Note that there are some standards (i.e., ISO) for iron ore that could be considered as well (e.g., RI, RDI, TI).
To ensure a manageable scope, particularly with the large number of potential tests to consider, it is expected that a set of core attributes be defined along with guiding/design Principles. These would provide a standardised reference that can be applied to any test.
The data value chain consists of five main links: collection, organization, analysis, storage, and use. The focus of this project is on the first step of the chain, improving the collection of data through reporting guidelines as an enabler of better organization, analysis, storage, and use.
The existing methods for reporting data between testing laboratories and customers (mining companies) is largely determined by the metallurgical laboratories that generate and report data and mining companies that receive the data.
The project will only be successful if it is supported and adopted by both metallurgical laboratories, mining companies and in some cases major equipment manufacturers. Stakeholder engagement and industry support will be critical to project success.
Some aspects of the scope outlined below are based on the CoalLog Standard that was developed by the AusIMM.
- Development of Guiding/Design Principles with a standardised format/terminology for the most common terms that can be applied universally.
- Development of Guiding/Design Principles with a standardised format/terminology for the most common terms that can be applied universally.
- Prioritisation list
- Data layout: A great example of a very successful global data layout is the LAS file put together by the Canadian Well Logging Society: https://www.cwls.org/wp-content/uploads/2014/09/LAS_20_Update_Jan2014.pdf (Not recommending this by far btw as it has many failing in its scalability, but a modernisation of this would be ideal)
- Glossary/ Codes (including differing sample origins, sample characteristics, test parameters and results)/Terminology
- Results file formats
- Data transfer format
- File names for transfer files
- Common metallurgical and geometallurgical tests for:
- Comminution
- Flotation
- Leaching
- Heap leaching
- ARD and waste management
- Common Iron Ore ISO tests (RI, RDI, TI etc.)
- Mineralogy including but not limited to:
- Automated mineralogy
- X-ray diffraction
- Hyperspectral
- Association
- Liberation (may need to be collected in 10% buckets only)
- PSD
- GSD
- Shape factor
- Development of suggested templates
- Development of User guide(s)
- API format
- Stakeholder engagement with major metallurgical labs
- Geotechnical and structural data
- The guideline will include data from automated mineralogy but will exclude data formats that cannot easily interact with the manufacturer (i.e., MLA data)
- tQEMSCAN data
- Chemical analysis and reporting