The mining industry leverages several analytical tools to investigate various ore types and grades. This article will briefly explore the role of spectroscopy in modern ore analysis.
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Why Is Ore Analysis Important?
Knowing the concentration of minerals in any given ore sample informs mining companies of the economic feasibility of their operations. In addition to providing vital information on ore composition, proper ore analysis helps determine the processing methods needed to extract those valuable minerals.
Enhanced ore analysis yields higher-quality results. This is important as mining projects often operate with tight margins: numerous tons of ore must be sorted and processed to produce only a minute amount of valuable minerals.
Ore samples are extremely heterogeneous, containing various chemical constituents and naturally occurring elements. They must be regularly sampled on a mass or timed basis using cross-stream methods.
Ensuring proper sampling of one-dimensional streams is also vital for sample integrity in both dry and wet slurry applications. Guaranteeing access to the complete ore stream and taking random samples is essential.
Correct equipment design and sampling techniques can eliminate common sources of bias, such as extraction, preparation, and delineation errors. However, some sampling errors can only be minimized due to the challenges in their complete elimination. Proper ore analysis can potentially save millions in downstream costs.
Why Is Spectroscopy Important for Ore Analysis?
Spectroscopy is a fundamental analytical technique in several industries and scientific research fields. It has been central to the development of several theories, such as quantum mechanics and electrodynamics.
Spectroscopy is the study of color. Spectroscopic techniques analyze a sample’s light and radiation emission, with the recent expansion of its definition to include the study of interactions between particles. These highly sensitive techniques can effectively determine even the smallest amounts of contaminants.
Laboratory spectroscopic techniques are becoming increasingly important for the mining industry; especially in ore analysis, given the extremely heterogeneous nature of ore samples. In addition to economically viable minerals, bulk and processed ore samples contain numerous other minerals, compounds, and potential contaminants.
X-Ray Fluorescence Spectroscopy in Ore Analysis
X-Ray fluorescence spectroscopy is a powerful tool used in mineral deposit surveys, the analysis of trace minerals and rare mineral species, the determination of trace elements in saline samples, and the monitoring of ore flotation processes. This technique is used as a control in certain analyses.
In recent years, X-Ray fluorescence has been combined with other analytical processes. This provides mining scientists and engineers with comprehensive analytical capabilities, significantly improving the investigation of complex mineralogical samples. Coupled with improved equipment, this technique has proven to be extremely powerful.
Through the integration of advanced X-Ray fluorescence spectroscopy techniques with improved instrumentation, X-Ray analysts can determine the chemical composition of any given ore sample at a microgram level analytical range. Sample preparation methods have also significantly advanced in recent years.
Advantages of Spectroscopy for Ore Analysis
Spectroscopy is just one of the various tools employed by analytical scientists to determine the composition and mineralogical concentration of an ore sample. This analytical method has its benefits and drawbacks, which inform its application in the laboratory.
Spectroscopy offers several advantages, including:
- Low-cost
- The ability to perform rapid, high-throughput analysis
- Suitability for routine analysis
- High-precision as an analytical tool
- The mapping of large-area samples with new and improved systems
However, spectroscopy cannot provide textural information on ore samples. This is better addressed by techniques such as optical image analysis.
A Method for Identifying Iron Ore Based on Reflectance Spectrometry and Deep Neural Networks
Recent research has utilized deep neural networks and reflectance spectrometry to improve iron ore identification. Rapid and accurate identification of iron ore is essential for subsequent downstream applications.
Conventional physical, manual, and chemical identification methods have the distinct disadvantage of being costly and time-consuming, which can adversely impact a mine’s financial bottom line. Companies therefore require rapid, accurate ore identification methods.
In a 2021 study published in the journal Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, a powerful spectroscopic tool was combined with advanced machine learning capabilities. The deep neural network utilized in the research is based on the extreme learning machine algorithm and a convolution neural network.
The team produced an iron ore identification framework with an overall accuracy of approximately 98 %. This proposed framework can effectively identify the iron ore types present in a given sample and has the potential to be used in the future by mining companies to enhance the efficiency of ore analysis.
Conclusion
Spectroscopy is rapidly becoming a central analytical tool in the mining industry for the determination of ore sample composition. Whilst still an emerging field in the industry, recent research has combined spectroscopic techniques with other methods, artificial intelligence, and machine learning to produce a powerful suite of tools for mine operators.
Spectroscopy is especially valuable for the rapid, high-throughput, low-cost, and high-precision analysis of ore samples due to their heterogeneous nature, where the mineral of interest may only constitute a minuscule proportion of the sample.
By utilizing these techniques, analytical scientists can improve the efficiency of ore extraction and the economic viability of mines in the 21st century.
See More: Iron Ore Analysis Using XRF Techniques
References and Further Reading
Xiao, D., Le, BT., Ha, TTL. (2021). Iron ore identification method using reflectance spectrometer and a deep neural network framework. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. doi.org/10.1016/j.saa.2020.119168.
Rose, HJ., Cuttitta, F. (2019) X-Ray Fluorescence Spectroscopy in the Analysis of Ores, Minerals, and Waters. [Online] Cambridge University Press. Available at: https://www.cambridge.org/core/journals/advances-in-x-ray-analysis/article/abs/xray-fluorescence-spectroscopy-in-the-analysis-of-ores-minerals-and-waters/0F61B086202C49FEC6F96094DA870D98.
Multotec. (no date). Laboratory and Ore Analysis. [Online] Multotec. Available at: https://www.multotec.com/en/news-articles/laboratory-and-ore-analysis.
Vining, K. (2017). Understanding Iron Ore Analytical Tools. [Online] CSIRO. Available at: https://research.csiro.au/resourcesandsustainability/iron-ore-analytical-tools/.
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