Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung, Dahee; Choi, Yosoon

doi:10.3390/min11020148

Cited by 68 publications

(37 citation statements)

References 121 publications

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“…By studying statistical reports shared by mining departments, we concluded that mining accidents occurred in the past due to poor supervision, inadequate implementation of enactment, lack of security consciousness, and inadequate safety education (Jung & Choi, 2021). What are the factors which make mining threatening?…”

Section: Resultsmentioning

confidence: 99%

Safety Measurements and Risk Assessment of Coal Mining Industry Using Artificial Intelligence and Machine Learning

Matloob¹,

Li²,

Khan³

2021

OJBM

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Safety is a legal requirement of mining industry. Effective safety measurements and risk management can serve the mine administration to lessen the dangers recognized in the mine. Autonomous technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) models have been integrated with the mining industry to ensure a safe working environment since a decade ago in the form of fully unmanned workfaces and mining robots. These autonomous technologies provide effective risk assessment as well as many economic profits such as cost reduction, continuous production, reducing labors exposure in dangerous environments, and enhanced protection. The main objective of this paper is to highlight the unidentified hazards during mining and find the solutions to those hazards by using AI and Machine Learning. In addition, this paper will also contribute a worthy resource for explaining the AI advancements in risk assessment in the mining industry. Another advantage of this paper is to fill the gaps in modern techniques and AI advancements in the safety measurements of coal mining industry.

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Section: Resultsmentioning

confidence: 99%

Safety Measurements and Risk Assessment of Coal Mining Industry Using Artificial Intelligence and Machine Learning

Matloob¹,

Li²,

Khan³

2021

OJBM

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“…As pointed out before, in a systematic review, the main objective is to investigate the body of knowledge to address a set of research questions [42,43]. This must be done using concrete methods and procedures for the sake of transparency and ease of evaluating the objectivity and trustworthiness of the figures and outcomes reported for the potential readers [42].…”

Section: Main Research Questions and Review Methodsmentioning

confidence: 99%

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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“…78/2010 on Reclamation and Post-Mining [18], an effective reclamation program and reforestation of post-mining land are stated clearly, even with the closure of the mine [10]. Reclamation is the last stage in mining activities after the exploration and exploitation stages [19]. In this paper, the terminology of reclamation refers to activities to improve post-mined land conditions so that they can function again as originally intended or according to other productive uses that have been planned [20].…”

Section: Law and Regulation Of Mining In Indonesiamentioning

confidence: 99%

Managing and Reforesting Degraded Post-Mining Landscape in Indonesia: A Review

Pratiwi¹,

Narendra²,

Siregar³

et al. 2021

Land

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Tropical forests are among the most diverse ecosystems in the world, completed by huge biodiversity. An expansion in natural resource extraction through open-pit mining activities leads to increasing land and tropical forest degradation. Proper science-based practices are needed as an effort to reclaim their function. This paper summarizes the existing practice of coal mining, covering the regulatory aspects and their reclamation obligations, the practices of coal mining from various sites with different land characteristics, and the reclamation efforts of the post-mining landscapes in Indonesia. The regulations issued accommodate the difference between mining land inside the forest area and outside the forest area, especially in the aspect of the permit authority and in evaluating the success rate of reclamation. In coal-mining practices, this paper describes starting from land clearing activities and followed by storing soil layers and overburden materials. In this step, proper handling of potentially acid-forming materials is crucial to prevent acid mine drainage. At the reclamation stage, this paper sequentially presents research results and the field applications in rearranging the overburden and soil materials, controlling acid mine drainage and erosion, and managing the drainage system, settling ponds, and pit lakes. Many efforts to reclaim post-coal-mining lands and their success rate have been reported and highlighted. Several success stories describe that post-coal-mining lands can be returned to forests that provide ecosystem services and goods. A set of science-based best management practices for post-coal-mine reforestation is needed to develop to promote the success of forest reclamation and restoration in post-coal-mining lands through the planting of high-value hardwood trees, increasing trees’ survival rates and growth, and accelerating the establishment of forest habitat through the application of proper tree planting technique. The monitoring and evaluation aspect is also crucial, as corrective action may be taken considering the different success rates for different site characteristics.

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Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Cited by 68 publications

References 121 publications

Safety Measurements and Risk Assessment of Coal Mining Industry Using Artificial Intelligence and Machine Learning

Safety Measurements and Risk Assessment of Coal Mining Industry Using Artificial Intelligence and Machine Learning

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

Managing and Reforesting Degraded Post-Mining Landscape in Indonesia: A Review

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