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

Jooshaki, Mohammad; Nad, A.; Michaux, Simon P.

doi:10.3390/min11080816

Cited by 37 publications

(15 citation statements)

References 91 publications

(229 reference statements)

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“…By analyzing the spectral signatures of different minerals, it is possible to map potential mining sites, estimate mineral abundance, and guide exploration efforts. Methods such as linear discriminant analysis (LDA) and ANNs are used for mineral identification from multispectral images; and SVM is used for mineral exploration, mine planning, and ore extraction from multispectral, RGB, and hyperspectral images (Jooshaki et al, 2021). MSI makes it possible to perform mineral recognition from slurry samples of the ore.…”

Section: Geologymentioning

confidence: 99%

Multispectral data mining: A focus on remote sensing satellite images

Lim,

Sreevalsan‐Nair,

Daya Sagar

2023

WIREs Data Min & Knowl

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This article gives a brief overview of various aspects of data mining of multispectral image data. We focus on specifically the remote sensing satellite images acquired using multispectral imaging (MSI), given the technology used across multiple knowledge domains, such as chemistry, medical imaging, remote sensing, and so on with a sufficient amount of variation. In this article, the different data mining processes are reviewed along with state‐of‐the‐art methods and applications. To study data mining, it is important to know how the data are acquired and preprocessed. Hence, those topics are briefly covered in the article. The article concludes with applications demonstrating the knowledge discovery from data mining, modern challenges, and promising future directions for MSI data mining research.This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining

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Section: Geologymentioning

confidence: 99%

Multispectral data mining: A focus on remote sensing satellite images

Lim,

Sreevalsan‐Nair,

Daya Sagar

2023

WIREs Data Min & Knowl

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“…ML is used in mineral processing and also used in mineral processing for prediction and potential difficulties of separation and the identification of minerals 39,40 . For mineral studies, Support vector machine, random forest, and artificial neural networks are frequently used supervised learning algorithms 41 . In the research paper, PSO-ELM used for prediction rockbrust and obtained good result which shows direction for future study in this field 42 .…”

Section: Supervised Learningmentioning

confidence: 99%

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Shetty,

Shabari Shedthi,

Shashishekar

2023

jmmf

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The ultimate objective of modern engineering applications in mining and material science is to develop good quality novel materials with desirable qualities. Machine Learning (ML) is used in the mining industry to provide solutions to complex problems of the mining industry and improve the efficiency of the overall system. ML methods are increasingly being used by materials scientists to uncover hidden trends in data and generate predictions. Furthermore, data centric techniques can provide useful insights into the basic processes that influence material behaviour while simultaneously reducing human labour in large data processing. The ability of persons to find new materials and infer complex relationships is important for the development of new materials. Large amounts of machine-readable data must be available to use statistical methodologies to speed materials research. In mining engineering, ML can be used for analyzing geographical data, assessing the risk of rock fall, predicting equipment failures and impact of mining activities on the environment etc. Material science data may be used in a variety of ways, including property prediction, the search for new materials and discovering synthesis methods. Selecting proper machine learning techniques to provide solutions is very important and that is discussed here. The purposes of this paper are to provide a comprehensive list of different ML techniques which are applied for the mining and material science domain.

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“…Machine Learning, a powerful method for analyzing vast amounts of data, has been developed in the past few years to the point where it can be applied to mineral processing applications [41]. The use of more sophisticated instrumentations in conjunction with ML has the potential to revolutionize the standard operating practice in mineral processing.…”

Section: Machine Learningmentioning

confidence: 99%

Digitalization Solutions in the Mineral Processing Industry: The Case of GTK Mintec, Finland

et al. 2022

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The technologies used in mineral process engineering are evolving. The digital mineral processing solutions are based on advances in our ability to instrumentally measure phenomena at several stages of the beneficiation circuit, manage the data in real-time, and to analyze these data using machine learning to develop the next generation of process control. The main purpose of this study is to overview various digital solutions for mineral processing plants and characterization laboratories while emphasizing their utilization in the current state of the digitization process of the GTK Mintec. This study highlights the specialized digital technologies that are particularly relevant for mineral processing and beneficiation. The digital solutions studied in this article include digital twin, machine vision, information management system, sensors, smart equipment, machine learning techniques, process control system, robotic cell, and Internet of Things applied across the whole chain of studying materials from the mineralogical examinations through the bench-scale studies to the pilot test trials. The aim is to provide a clear view on the different aspects of digitizing mineral processing plants based upon the lessons learned from the development plans in GTK Mintec.

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

Cited by 37 publications

References 91 publications

Multispectral data mining: A focus on remote sensing satellite images

Multispectral data mining: A focus on remote sensing satellite images

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Digitalization Solutions in the Mineral Processing Industry: The Case of GTK Mintec, Finland

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