A Data-Driven Approach to Control Fugitive Dust in Mine Operations

Kahraman, Muhammet Mustafa; Erkayaoğlu, Mustafa

doi:10.1007/s42461-020-00318-2

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Monitoring particulate matter (PM) in mining sites involves collecting air quality data while considering wind direction. This monitoring can be divided into three parts: (1) monitoring the mine atmosphere away from equipment operations but within the site, (2) monitoring PM dust at operating sites, including drilling, blasting, loading, transportation, and facilities, and (3) monitoring PM dust outside the mining area 19 .…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

Tripathi,

Aruna,

Parida

et al. 2024

Sci Rep

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The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m3, 27.89 µg/m3, 26.17 µg/m3, and 27.24 µg/m3, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models—Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)—for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

Tripathi,

Aruna,

Parida

et al. 2024

Sci Rep

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“…Still, another classification can also be found in literature: inhalable particles (size range from 10 to 20 µm), thoracic particles (size range between 4 and 10 µm), and alveolic particles (size range below 4 µm) [22]. Some studies suggest that PM 2.5 can be more hazardous to human health than PM 10 [1], leading to an increased variety of chronic diseases [23].…”

Section: Introductionmentioning

confidence: 99%

Occupational Exposure to Mineral Dust in Mining and Earthmoving Works: A Scoping Review

Duarte

Branco

Rodrigues

et al. 2022

Safety

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Anthropogenic activity is related to several environmental imbalances, including dust. Particulate matter can also hinder humans with numerous health consequences, such as asthma, cancer, and pneumoconiosis. With a particular focus on mineral dust, this review is intended to determine in which circumstances occupational exposure occurs in the mining and earthmoving industries. Research followed the guidelines provided by the preferred reporting items for systematic review and meta-analysis protocols and its extension for scoping reviews. Of the 8993 records identified, only 24 passed both exclusion and inclusion criteria. Within the pool of results, it was possible to identify the following variables related to dust exposure: job-related (activity, job category, and site), engineering (equipment, transport system), technical (distance), and physical (season and weather) variables. Due to the significant variance in protocol settings, it was challenging to perform a general analysis, resulting in a study-by-study approach. The most significant conclusion of this study is not related to the setting of occupational exposure, although it derives from it. The necessity of adopting standard procedures for data collection, independent of research objective, was demonstrated within the context of occupational exposure to mineral dust.

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Statistical Perspectives on Air Emission Inventory for Considering Fine Particle Reduction Potential in Korea: Shouldn’t We Also Focus on Local and Provincial-Specific Implementations?

Kwon

2021

Water Air Soil Pollut

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A Data-Driven Approach to Control Fugitive Dust in Mine Operations

Cited by 7 publications

References 34 publications

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

Occupational Exposure to Mineral Dust in Mining and Earthmoving Works: A Scoping Review

Statistical Perspectives on Air Emission Inventory for Considering Fine Particle Reduction Potential in Korea: Shouldn’t We Also Focus on Local and Provincial-Specific Implementations?

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