In the blog about “The Value of mineralogical monitoring” series, we discussed the value of tracking the mineralogical composition for efficient ore beneficiation on the examples of copper and nickel ore. This blog elaborates on copper ore application in more detail.

Complex mineralogy of copper ore

Copper, being a transition metal, is part of many mineralogical phases. Over 150 copper minerals were identified, however, only a few are of economic importance. Copper minerals can be divided into three groups: (i) primary sulphide minerals (e.g. chalcopyrite, bornite, and enargite); (ii) oxides, formed by weathering of primary sulphides (e.g. cuprite, malachite, chrysocolla and covellite); and (iii) secondary sulphides (e.g. chalcocite and covellite) formed from copper leached from near-surface primary sulphides.

The complex mineralogy of copper ore deposits presents a challenge for effective mine planning and further beneficiation steps. Every mineral behaves differently during the flotation or leaching; therefore, mineralogy analysis will help to select correct reagents and efficiently use them. Apart from accurate quantification of economically valuable copper sulphides and oxides, the presence of gangue minerals, like quartz, talc, clays, pyroxenes, or amphiboles has a huge influence on processing and recovery rate.

Failure to adequately monitor copper ore mineralogy can result in reduced recovery rate due to significant variance of the feed and wrong calculation of oxide/sulphide ratio, reduced grinding, and pumping efficiency due to the presence of soft and hard minerals or increased acid consumption due to alteration minerals such as clays.  The combined impact can lead to tens of millions of losses, which can be prevented if accurate and frequent mineralogy checks are in place.  

Earlier we established that X-ray diffraction (XRD) is fast, versatile and accurate mineralogy probe, which can be easily implemented in the process flow at mine operation and processing plant. In the following case study, we evaluate the accuracy of XRD for resolving mineralogical composition of copper ore using 100 samples from a drill core of a Northern American copper ore deposit.

Accurate characterization of copper ore mineralogy by XRD

One hundred samples were prepared as pressed pellets and measured on Aeris Minerals benchtop diffractometer with a scan time of 10 minutes, followed by an automatic quantitative phase analysis. Figure 1 shows the result of XRD analysis using Aeris Minerals tabletop diffractometer of one of those samples.

Figure 1. Quantitative phase analysis of complex copper ore using Aeris Minerals tabletop diffractometer. Measurement time is 10 minutes.

Any XRD pattern is a set of diffraction peaks of different intensities, located at certain diffraction angles (2Theta), specific to a certain mineralogical phase. Peak positions enable identification of present phases. The relative intensities of each mineral contribution to the XRD pattern allows us to quantify the relative amount of each present mineral using full-pattern Rietveld refinement [1].

The analyzed sample set is characterized by a very complex mineral composition, varying from sample to sample (Figure 2, top). In total 23 different minerals were identified and quantified in the analyzed sample set. Let’s first discuss the copper-bearing minerals. Both, copper sulfides and oxides, were identified. The main found copper-bearing minerals are chalcopyrite CuFeS2, cuprite Cu2O, tenorite CuO, brochantite Cu4[(OH)6(SO4)] and serpierite Ca(Cu, Zn). The found complex mineralogy should be considered in the planning of the next processing steps. Copper oxides and sulphides should be separated and concentrated. The type and quantity of active agents for flotation and leaching should be identified based on the found relative phase amounts.

Figure 2. Quantitative phase analysis of complex copper ore (top); comparison of total copper and iron content, calculated from mineral composition (black squares), and bulk chemical analyses using XRF (red cycles).

Let’s now look at the present gangue minerals. The majority of the analyzed samples are very high in quartz (Figure 2, top).  Quartz is a hard mineral, which will increase wear and tear of the crushing and milling equipment. A significant amount of talc and clay minerals also a reason for concern. These soft minerals are known to cause issues during flotation, increase consumption of acid used for leaching, finally, soft minerals lead to tubes clogging and blockage, reducing milling and pumping efficiency. Knowing the concentration of hard and soft minerals helps to plan usage and maintenance of a processing plant infrastructure.

Performed quantitative mineralogy analysis (Figure 2, top) allows us to take several decisions to optimize mine planning, further downstream processing, and allows fast counteractions.  But how accurate are the presented results?

Using the identified mineral quantities, the total oxides can be calculated, which can be compared with the bulk chemical analyses. In the bottom graph in Figure 2 the total iron and copper content, calculated from the mineralogical composition, are compared with that measured by x-ray fluorescence (XRF).

We see a very good agreement between XRD and XRF results. Even very small amounts of copper minerals can be monitored, and the respective Cu-content can be accurately predicted using 10 minutes measurement of a complex copper ore on a tabletop XRD instrument.

Added-value of XRD monitoring throughout the whole process

In the above section we analyzed mineralogy of 100 drill-core samples of copper ore and identified points of attention for the next processing step. The efficiency of the following steps can also be monitored using X-ray diffraction. In addition to the classical quantitative phase analysis, XRD offers several other tools to simplify day-to-day process monitoring. In our blogs on iron ore and heavy mineral sand processing (to be published shortly) we will give an example of cluster analyses [2,3] being used for quick and easy monitoring of ore grade definition and mineral separation efficiency. A similar approach can be used to monitor the separation and concentration efficiency at copper processing plant. Mineralogy of tails and waste products can also be controlled using XRD.

On-line control of clay content in copper ore

In the introduction blog we discussed the advantages of near-infrared spectroscopy (NIR) for online mineralogy monitoring. Not all minerals, commonly present in copper ore, are visible for NIR spectroscopy.  However, talc and clay minerals are, and therefore can be easily identified and quantified using on-line NIR over-the-belt analyzer or laboratory NIR spectrometer. Real-time monitoring of clay and talc content in the run-of-mine will improve the efficiency of flotation and leaching processes, prevent possible equipment blockage and other common issues, associated with the presence of large quantities of soft minerals in the ore.

NIR spectrometers can also be part of completely automated laboratories, operate standalone in a laboratory, or assist mine geologists as handheld devices in the field.

To summarize, copper ore mineralogy is very complex and has a major impact on the beneficiation process. To run it efficiently, not only the copper oxide/sulphide ratio should be considered, the adequate and timely control over mineral impurities should be a part of the process monitoring. A moderate investment in fast, accurate, and reliable mineralogical probs upstream helps to save millions of dollars downstream.

To learn more about the added value of XRD for copper mine and processing plant watch our webinars-on-demand, discussing the Use of XRD for mineralogical analysis of copper ore concentrates and tailings and showcasing Aeris Minerals for this application. For additional information on NIR analysis of clays and talc in copper ore, please review the dedicated application study or visit our website.


  • [1] H.M. Rietveld, A profile refinement method for nuclear and magnetic structures, J. Appl. Cryst. (1969), 2, 65 – 71.
  • [2] H. Lohninger, Teach Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999, ISBN 3-540-14743-8.
  • [3] G.N. Lance, W.T. Williams, A general theory of classification sorting strategies 1., Hierarchical systems, Comp. J. (1966), 9, 373 – 380.