Nigeria is rich is solid mineral resources, such as: Kaolin, gypsum, mica, clay, tantalite, iron ore, gemstone, silica sand.
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Recycling concrete &building rubble is not only able to bring profit to investors, but also good for environment protection.
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By absorbing the advanced technology from the world, we researched and designed PF series impact crusher.…
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Base on the plentiful experience of producing and marketing of impact crusher, Sofol R&D institution…
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Adopting technology from the world, PY Series spring cone crusher has an excellent performance in secondary…
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The vibrating screen is a kind of sieving equipment of international advanced level, developed by our…
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Sofol VSI Series vertical shaft impact crusher is designed by reputed German expert of Sofol and every…
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Laboratory-based hyperspectral imaging (HSI) is an optical non-destructive technology used to extract mineralogical information from bedrock drill cores. In the present study, drill core scanning in the long-wave infrared (LWIR; 8000–12,000 nm) wavelength region was used to map the dominant minerals in HSI pixels. Machine learning classification …
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National Occupational Classification (NOC) 2021 Version 1.0 - Machine operators in mineral and metal processing operate machinery to process mineral ore and metal. They are employed in mineral ore and metal processing plants such as copper, lead and zinc refineries, uranium processing plants, steel mills, aluminum plants, precious metal refineries and cement …
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The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification.
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Rock classification. 1. Introduction. Deep learning (DL) has been highly effective in a range of tasks in geosciences, including capturing complex relationships in datasets, …
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Introduction. Modern mining, sorting, and mineral use have been challenged by the scarcity of mineral resources. At this stage, the development and application of vision-based ore sorting equipment have become one of the mainstreams to increase economic benefits, improve mineral grades, and reduce mining costs (Ali and Frimpong, 2020; Barnewold and …
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Mineral Classification Using Seismic Data allows for the differentiation of valuable minerals from surrounding rock formations, enhancing the efficiency of mineral exploration projects. ... with advancements in seismic technology driving improved accuracy and efficiency in mineral exploration. Integration with machine learning is ...
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LIBS serves as a powerful tool in mineral classification, offering numerous advantages such as rapid analysis, non-destructive testing, and high sensitivity to trace elements. Despite its strengths, LIBS poses certain challenges, including limited depth profiling and difficulties in analyzing certain mineral types. ... By leveraging machine ...
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Vision-based mineral image recognition and classification is a proven solution for autonomous unmanned ore sorting. Although accurate identification can be achieved by training models offline using large-scale datasets, the lack of sufficient labeled images still limits the accessibility and exploration of high-performance deep learning models. To address the above …
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Mineral image classification technology based on machine vision is an efficient system for ore sorting. With the development of artificial intelligence and computer technology, …
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The identification of minerals is indispensable in geological analysis. Traditional mineral identification methods are highly dependent on professional knowledge and specialized equipment which often consume a lot …
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A novel froth image analysis based production condition recognition method is presented to identify the froth phases under various production conditions. Gabor wavelet transformation is employed to froth image processing firstly due to the ability of Gabor functions in simulating the response of the simple cells in the visual cortex. Successively, the statistical distribution …
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A Mineral Classifier can be used to identify the minerals just by looking at their photographs without any need of human intervention and can thus help humans in mineral exploration.It can help…
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Machine Learning (ML) has found several applications in spectroscopy, including recognizing minerals and estimating elemental composition. ... We firstly reviewed and tested several ML …
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Download Citation | On Sep 1, 2024, Eloy Peña-Asensio and others published Machine learning applications on lunar meteorite minerals: From classification to mechanical properties prediction ...
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JXSC grinding & classification equipment is the key equipment for crushing materials after they are crushed. The ball mill can process various grinding minerals, non-ferrous metal beneficiation, and new building materials. It is often combined with a spiral classifier to form a closed-circuit ring mill, which has higher fine powder and grinding power.
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Machine learning uses classification algorithms (such as Support Vector Machines, Random Forests, etc.) to train on mineral samples and then apply these algorithms …
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Qmin, a machine learning-based application, was developed specifically for analyzing mineral chemistry data obtained through electron probe microanalysis (EPMA) to quickly classify minerals and quantify the uncertainty of classification results using Shannon entropy (da Silva et al., …
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Purpose Given the growing concern over soil heavy metal contamination, there is an increasing need for affordable and precise soil heavy metal information. In particular, efficient and cost-effective methods for detecting soil manganese (Mn), a heavy metal element that is also essential for life processes, hold significant importance. This study employs tree-based …
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The most widely used method for mineral type classification from a rock thin section is done by the observation of optical properties of a mineral in a polarized microscope …
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Fe 2+ and Fe 3+ estimation is routinely performed in MagMin_PT based on stoichiometric constraints, and to some extent using machine learning methods for different iron-bearing minerals. ... Finally, mineral classification diagrams, formulae proportions and geothermobarometry data can easily be exported as 'gif/jpeg/tiff' files and tables.
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The mineral classification is based on mineral compositions available in the database fed to the models during the training stage. This setting implies that the models cannot recognize any mineral different from those implemented during the training. ... Mineral classification using machine learning and images of microscopic rock thin section ...
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The classification of mineral images has been done using a variety of machine learning techniques, namely Naive Bayesian, Support Vector Machine (SVM) [2, 3], Decision …
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Performing conventional mineral classification with hyperspectral methods is time-consuming and requires significant human intervention, from the endmember selection to manual validation in ...
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fluorescence were excluded. Each mineral name was matched with its four-part Dana classification number in which the first number is mineral class, the second is mineral type, the third is the mineral group, and the fourth is the specific mineral species. Techniques: Vector similarity metrics. The most
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Machine Learning (ML) has found several applications in spectroscopy, including recognizing minerals and estimating elemental composition. ... We firstly reviewed and tested several ML approaches to mineral classification from the existing literature, and identified a novel approach for using Deep Learning algorithms for mineral classification ...
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Fe 2+ and Fe 3+ estimation is routinely performed in MagMin_PT based on stoichiometric constraints, and to some extent using machine learning methods for different iron-bearing minerals. ... Finally, mineral classification …
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Mineral classification in CRISM images is often approached with single scatter albedo or summary parameter RGB combinations. However, these methods neglect sub-pixel mineral mixtures, limit class dimensionality of whole images, and do not compensate for residual noise. The purpose of this study was to use well-established, available, and time-saving methods in …
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During the past few decades, ML based classification techniques such as k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Decision Trees (DT), Random Forest (RF), Artificial Neural Networks (ANN), etc. (McCoy and Auret, 2019, Acosta et al., 2019, Maxwell et al., 2018) have emerged as a very promising …
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Rock classification is essential for geological research and plays an important role in numerous fields, such as rock mechanics, petrology, mining engineering, magmatic processes, and applications associated with geosciences (Izadi and Sadri et al. 2017; Li et al. 2017; Xu and Zhou 2018).This classification can be accomplished via the characterization of …
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We present a robust and autonomous mineral classifier for analyzing igneous rocks. Our study shows that machine learning methods, specifically artificial neural networks, can be trained using spectral data acquired by in situ Raman spectroscopy in order to accurately distinguish among key minerals for characterizing the composition of igneous rocks.
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PE series jaw crusher is usually used as primary crusher in quarry production lines, mineral ore crushing plants and powder making plants.
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