A neural network approach for spatial variation assessment – A nepheline syenite case study

Silva, Camilo Andrés Mena; Ellefmo, Steinar Løve; Sandøy, Roar; Sørensen, Bjørn Eske; Aasly, Kurt

doi:10.1016/j.mineng.2019.106178

Cited by 5 publications

(8 citation statements)

References 56 publications

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“…As shown in Figure 4a, the majority of the methods used in the reviewed papers fall in the supervised learning category. More precisely, among the 17% of the reviewed works that used unsupervised learning models, only 6% solely leveraged unsupervised learning [48,55,83], but in the remaining 11%, a combination of the supervised and unsupervised learning techniques is utilized [54,61,73,79,92,93]. In such works, unsupervised learning methods are typically used for the feature extraction and preprocessing of the data to be used in a supervised learning process.…”

Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

“…In such works, unsupervised learning methods are typically used for the feature extraction and preprocessing of the data to be used in a supervised learning process. The bulk chemistry data from the mining company open-pit database was used as an input for the prediction of laboratory concentrate yield and modal mineralogy for the nepheline syenite deposit in Norway by adopting a neural network approach [79]. The data collected by the electron probe microanalyzer (EPMA) was analyzed with an ML method aimed to be established for calculating the amphibole formula [76].…”

Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

“…Classification tasks can be, for instance, determining the type of minerals [73,77,94,95], texture [66,89], or rock [86,93], class of mineral face [86], class of hematite crystals [78], distinguishing between quartz and resin in optical microscopy images [80], presence or absence of specific minerals in a sample [96], zeolite type [97], material fingerprints [98], class of regolith landform [99], and class of carbonates [100] based on a set of measurements or known features about a material. On the other hand, in regression problems, the goal is to estimate a continuous numerical value, for instance, prediction of concentrate yield and modal mineralogy [79] and estimation of drill-core mineral abundance [63], mineral density of elements [101], and calculation of amphibole formula [76].…”

Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

“…Interesting solutions by the implementation of an ML methodology to the prediction of material properties from the nepheline syenite deposit was discussed in [79]. The challenges with calculating the amphibole formula from electron microprobe analysis can be solved by applying ML [76].…”

Section: Problems In the Selected Studies Addressed Using ML Techniquesmentioning

confidence: 99%

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A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

2021

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Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

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Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

Section: Problems In the Selected Studies Addressed Using ML Techniquesmentioning

confidence: 99%

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A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

2021

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“…Nepheline is a feldspathoid mineral of composition (Na, K) AlSiO 4 and usually forms small grains, which are inter-crystallised with the feldspar [39][40][41]. Nepheline-syenite is a silica-undersaturated igneous rock containing feldspars and feldspathoids (nepheline, leucite, etc.…”

Section: Introductionmentioning

confidence: 99%

Effect of Mechanically Activated Nepheline-Syenite Additive on the Physical–Mechanical Properties and Frost Resistance of Ceramic Materials Composed of Illite Clay and Mineral Wool Waste

Pranckevičienė

Pundienė

2023

Materials

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This study investigates the coupling effect of mechanically activated nepheline-syenite (NS) and mineral wool melt waste (MWMW) on the physical–mechanical properties of a ceramic body. The results indicate that an optimal amount (10–20%) of NS additive promotes the formation of the smallest pore size from 0.001 to 0.01 µm, as well as improves physical, mechanical, and durability properties of the ceramic samples with MWMW, when fired at temperatures between 1000 and 1080 °C. As the NS content increases, the composition becomes more alkaline, leading to enhanced vitrification and the formation of a glass phase during firing. This reduces open porosity, modifies pore size distribution, and enhances compressive strength and frost resistance. An NS content of 15% produces the best results, increasing the smallest pore fraction and yielding favourable properties, such as reduced open porosity, water absorption and density, increased compressive strength, and does not affect the linear shrinkage. The frost resistance test demonstrates that the coupling effect of NS additive and MWMW improves the samples’ resistance to freeze–thaw cycles, with the best performance observed at 15% NS content. The study also highlights the usefulness of structural parameters and ultrasound testing for assessing and predicting the frost resistance of ceramic samples.

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Process mineralogy of unconventional mineral deposits examples of applications and challenges

Aasly

2024

Minerals Engineering

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A neural network approach for spatial variation assessment – A nepheline syenite case study

Cited by 5 publications

References 56 publications

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Effect of Mechanically Activated Nepheline-Syenite Additive on the Physical–Mechanical Properties and Frost Resistance of Ceramic Materials Composed of Illite Clay and Mineral Wool Waste

Process mineralogy of unconventional mineral deposits examples of applications and challenges

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