A Review of Machine Learning Applications for Monitoring and Predicting Particulate Matter (PM) Pollution in Open-Pit Mines

This research provides an in-depth analysis of smart dust monitoring systems for surface mining, with a strong focus on machine learning techniques. These systems tackle key concerns in mining, including security, environmental impacts, and operational efficiency, through real-time data collection and analysis techniques. Existing studies state-of-the-art sensors for air quality, performance of machines, and geological conditions are synthesized. Machine learning techniques facilitate predictive maintenance, optimized resource allocation, and data-driven decision-making, significantly improving safety, cost-effectiveness, and sustainability. All such improvements mark significant improvements in security, financial viability, and environmentally friendly mining processes. In addition, its use in mining engineering serves as a source of information for professionals in the field, offering real-life insights for transforming surface mining processes through information-based approaches.

کلیدواژه ها:

Dust monitoring ، Surface Mining ، Machine Learning ، Environmental Management ، Real-Time Monitoring