In the first part of this blog (2nd April 2014) a brief introduction was given on having a systematic approach to tailings characterisation. There is no argument that tailings dam reprocessing can be profitable, and this blog is not about the methodology to do the actual reprocessing. The research being done is rather on the best way to characterise a tailings dam before processing starts. From published literature it seems that sampling on tailings dams is limited, and variations in tailings mineralisation (and thus grade and recovery potential) are not always taken into account. Yes, a profit can be made using this approach, but is it the optimum profit? Companies involved in reprocessing all drill and sample their tailings dams – the question lies however in whether there can be improvements in the sampling and testing done to provide a more comprehensive picture of the tailings dam. How many companies develop a block model of the tailings dam that includes metallurgical parameters? This is especially important when dealing with a multi commodity tailings dam which will require different processing options for different metals and minerals.
The aim of geometallurgical analysis is to reduce uncertainty and risk in mining projects. Various authors (Dunham and Vann, 2007; Knight et al., 2011; Williams and Richardson, 2004) state that geometallurgy is targeted at integrating issues like in situ tonnes/grades with factors like deleterious elements, throughput rates, mining/processing costs and metallurgical recovery by identifying either direct measures or proxies for throughput (hardness, grindability), recovery (liberation, mineral shape/texture, etc.) and concentrate quality from easily collected macro-, meso- and microscopic data. These “geometallurgical variables” drive project costs and revenues in a fundamental way. The most critical input into the evaluation process is the spatial estimate as this defines the spatial distribution of tonnes and grade for mineralisation and is the “template” or “blueprint” mining engineers use to create mining value options (Dunham and Vann, 2007). All schedules of tonnes and grades, and thus all cash flows and downstream financial analysis, are derived from this spatial estimate. The main driver for the PhD project is to develop a geometallurgical characterisation approach to base metals flotation tailings dams specifically. The ultimate objective is to produce a 3D spatial model showing not only grade distribution but metallurgical performance differences as well. The objective is to produce a domain map of the tailings dam similar to what is produced for a primary ore deposit.
The challenge in geometallurgical modelling is to provide rigorous geostatistical methodologies for spatially distributing the various attributes into typical block and domain models (Walters and Kojovic, 2006).
According to Keeney and Walters (2011) current primary limitations to obtaining the maximum value from geometallurgy are:
– A lack of appropriate metallurgical orebody domain definition. Using pre-defined boundaries (i.e. lithological/grade domains) to control the distribution of metallurgical performance indices and test work, without proper evaluation of the relationship between domain definition and process characteristics, introduces high uncertainty. Typically there is no guaranteed direct relationship between geological ore definition and metallurgical performance (Walters and Kojovic, 2006).
– A lack of rigorous sampling protocols and inadequate numbers of metallurgical samples. As metallurgical tests tend to require large masses, limited numbers of drill hole samples are frequently composited. Using composite samples without understanding variability is high risk.
– A lack of an accepted geostatistical methodology for spatially distributing potentially non-additive and non-linear geometallurgical attributes. If insufficient data exists to assess variability and variography, a sophisticated geostatistical method will still produce suboptimal results. No defined industry accepted methodology exists for conducting a geometallurgical study (Keeney and Walters, 2011).
Keeney and Walters (2011) proposed a new method for integrating diverse geological and metallurgical data sets to produce predictions of metallurgical performance indices suitable for process domain definition. An overview of the Integrated Geometallurgical Method (IGM) is shown in Figure 1.
The approach suggested by Keeney and Walters (2011), which was developed for primary ore deposits, will be tested and modified as appropriate for base metal tailings dam material in this PhD project.
More information on the proposed modified methodology can be requested from the author.
Dunham, S. and Vann, J. (2007). Geometallurgy, Geostatitsics and Project Value – Does your Block Model Tell You What You Need To Know?. Project Evaluation Conference, Melbourne, Victoria.
Keeney, L. and Walters, S.G. (2011). A Methodology for Geometallurgy Mapping and Orebody Modelling. The First AusIMM International Geometallurgy Conference, Brisbane, Australia
Knight, R., Olson Hoal, K., and Abraham, A.P.G. (2011). Three-Dimensional Geometallurgical Data Integration for Predicting Concentrate Quality and Tailings Composition in a Massive Sulphide Deposit. The First AusIMM International Geometallurgy Conference, Brisbane, Australia
Walters, S. and Kojovic, T (2006). Geometallurgical mapping and mine modelling (GeMIII) – the way of the future. In Proceedings SAG2006 Conference, Vancouver, vol IV, pp411-425
Williams, S.R., and Richardson, J.M., (2004). Geometallurgical Mapping: A New Approach That Reduces Technical Risk. Proceedings – 36th Annual Meeting of the Canadian Mineral Processors, Ontario, Canada.