Applications of machine learning and the Internet of Things for monitoring and predicting particulate matter pollution in open pit mines: A systematic review

This research provides a comprehensive review of smart dust monitoring systems utilized in surface mining, emphasizing the advantages offered by IoT (Internet of Things) and machine learning methodologies. Smart dust technology introduces innovative approaches to real-time surveillance and data acquisition, addressing the challenges faced by the mining sector, particularly in terms of safety, environmental impact, and operational efficiency. The review summarizes the current research on the implementation of miniature sensors that collectively form a network to gather critical information on various parameters, including air quality, equipment functionality, and geological conditions. By employing machine learning algorithms, the collected data can be analyzed to predict potential equipment malfunctions, optimize resource utilization, and enhance decision-making processes. This approach significantly improves safety, reduces operational costs, and promotes sustainability in mining. As a foundational tool in mining engineering education, this technology also provides practitioners with insights into the potential opportunities created by modern IoT and machine learning applications in surface mining operations.

Applications of machine learning and the Internet of Things for monitoring and predicting particulate matter pollution in open pit mines: A systematic review Keywords:

Dust monitoring , Surface Mining , IoT (Internet of Things) , Machine Learning , Environmental Management , Real-Time Monitoring