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

سال انتشار: 1404
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 37

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شناسه ملی سند علمی:

EUAIME01_063

تاریخ نمایه سازی: 11 تیر 1404

چکیده مقاله:

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.

نویسندگان

Taha Salahjou

Mining Engineering Student, School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Ali Najmeddin

Assistant Professor, Department of Mining Engineering, School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran