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With the development of mineral resources, minerals are becoming increasingly difficult to process. In order to utilize these resources more effectively, in-depth research into process mineralogy has become increasingly important in the field of mineralogy, and particle size measurement under the microscope is one of the critical aspects of process mineralogy. At present, the use of scanning electron microscopes and other equipment for measurement is very expensive, and manual measurement has problems such as poor accuracy and low efficiency. In addition, there is a lack of reference materials for the segmentation algorithm of mineral light images. This article proposes a Gaussian pyramid based on bilateral filtering combined with directional maximum intercept to measure mineral particle size under the microscope. In the experiments, different segmentation algorithms were studied, including Gaussian pyramid segmentation based on bilateral filtering, segmentation based on Fuzzy C-Means, and the rapidly developing deep learning segmentation algorithms in recent years. By comparing the segmentation effects of these three algorithms on various mineral thin-section images, the Gaussian pyramid segmentation algorithm based on bilateral filtering was selected as the optimal one. This was then combined with the directional maximum intercept method to measure the particle size of ilmenite and pyrite images. The experimental results show that the segmentation method based on the bilateral filtering Gaussian pyramid technique has higher segmentation accuracy than the other two algorithms, and can accurately measure the particle size of minerals under the microscope. Compared with manual measurement, this method can effectively and accurately measure the microscopic particle size of target minerals, greatly reducing the workload of measurement personnel and reducing the time spent on measurement.
With the development of mineral resources, minerals are becoming increasingly difficult to process. In order to utilize these resources more effectively, in-depth research into process mineralogy has become increasingly important in the field of mineralogy, and particle size measurement under the microscope is one of the critical aspects of process mineralogy. At present, the use of scanning electron microscopes and other equipment for measurement is very expensive, and manual measurement has problems such as poor accuracy and low efficiency. In addition, there is a lack of reference materials for the segmentation algorithm of mineral light images. This article proposes a Gaussian pyramid based on bilateral filtering combined with directional maximum intercept to measure mineral particle size under the microscope. In the experiments, different segmentation algorithms were studied, including Gaussian pyramid segmentation based on bilateral filtering, segmentation based on Fuzzy C-Means, and the rapidly developing deep learning segmentation algorithms in recent years. By comparing the segmentation effects of these three algorithms on various mineral thin-section images, the Gaussian pyramid segmentation algorithm based on bilateral filtering was selected as the optimal one. This was then combined with the directional maximum intercept method to measure the particle size of ilmenite and pyrite images. The experimental results show that the segmentation method based on the bilateral filtering Gaussian pyramid technique has higher segmentation accuracy than the other two algorithms, and can accurately measure the particle size of minerals under the microscope. Compared with manual measurement, this method can effectively and accurately measure the microscopic particle size of target minerals, greatly reducing the workload of measurement personnel and reducing the time spent on measurement.
This work aims to evaluate the use of a superabsorbent polymer (SAP) to provide improvements in the handling properties of iron ore tailings (IOT). The material studied came from the magnetic separation reprocessing of the material discarded at the Gelado Dam, located in Serra dos Carajás in the state of Pará, Brazil. While the concentrate presents reasonable handling conditions, the tailings, with 61.5% iron, 15% moisture, and 39% of the mass, have high cohesiveness and adhesiveness due to their fine nature and the climatic conditions of the Amazon rainforest. However, the tailings can still be considered a product as long as the handling and transportation logistics are feasible. Thus, studies with an SAP and IOT were carried out in a bench rotating drum to promote mixing between them, and the main variables studied were the SAP dosage and the required contact time. The improvement in the physical properties of the IOT were evaluated considering the Hausner ratio, Carr index, Jenike’s flow function index, Atterberg limits, and chute angle. The superabsorbent polymer promoted a significant improvement in the state of consistency of the material, and the best performance was obtained with a dosage of 1000 g t−1. As long as a suitable contact condition was promoted, a contact time of 1 min was enough to achieve the expected benefits. After dosing with the superabsorbent polymer, the material’s handling classification changed from ‘cohesive’ to ‘easy flow’, and the chute angle was reduced from 90° to levels below 60°. It was concluded that the application of the superabsorbent polymer has the potential to improve the fluidity of the material discarded in the magnetic concentration operation, allowing it to be handled throughout the production and transportation chain. The SAP appears to be an important additive for the full use of the material present in the dam (100% recovery), with both economic and socio-environmental benefits.
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