Taillings Characterization: Exploring Laser Granulometry with Machine Learning

Abstract

The conventional particle size test has been a widely used method in the characterization of soils and tailings. Such information is particularly useful in the evaluation of materials deposited in tailing stacks or compacted landfills, which must follow reference particle size ranges. However, the method has limitations, the main one being the execution time, which usually lasts around three days. On the other hand, laser testing appears as a viable alternative. This innovative method obtains the grain size curve of the soil through the light dispersion pattern and lasts a few minutes, a significant improvement over the conventional method. Furthermore, this method can cover particle size ranges of up to 0.1 micrometers, while the conventional method is limited to 1 micrometer. Despite the benefits of using this equipment, the laser grain size test does not yet have specific standardization for use in the field of soil mechanics. In this context, this work proposes the use of machine learning techniques to demonstrate the existence of compatibility between both methods. To this end, tests were carried out using both methodologies on different samples of iron ore tailings and an algorithm was developed to predict the material classification. The evaluation of the results made it possible to verify the consistency and precision of the results between the two methods, reinforcing the reliability and viability of the laser test as an efficient alternative to the traditional method

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