AI-Driven Solutions for Real-Time Data Analysis in Mining

Sangwoo Jeon, Dharani Jaganathan, Saravanan Murugesan, Min Dugki, Mohamed Yasin

Abstract


AI-driven solutions are transforming data analysis practices in the mining industry by enabling real-time insights and decision-making. This paper explores the methodologies and technologies utilized in AI-powered data analysis systems, focusing on their applications in resource management, operational optimization, and predictive analytics. By presenting case studies, the paper highlights the benefits of employing AI-driven solutions for data analysis, including improved operational efficiency, enhanced safety, and better resource utilization. Additionally, the challenges of integrating these systems into existing mining operations are discussed, along with future prospects for their advancement.

Keywords


AI, real-time data analysis, mining, operational efficiency.

References


Bhaduri, Aranya, and Ajoy Kumar Moitra. “Artificial Intelligence in Energy Transition Mineral Exploration”. Mineral Metal Energy Oil Gas and Aggregate Journal, 2024, pp. 167–176.

Corrigan, Caitlin C., and Svetlana A. Ikonnikova. “A Review of the Use of AI in the Mining Industry: Insights and Ethical Considerations for Multi-Objective Optimization”. The Extractive Industries and Society, vol. 17, Elsevier, 2024, p. 101440.

Kokkinis, Athanasios, et al. “Review of Automated Operations in Drilling and Mining”. Machines, vol. 12, no. 12, MDPI, 2024, p. 845.

Mutovina, Natalya, et al. Application of Artificial Intelligence and Machine Learning in Expert Systems for the Mining Industry: Literature Review of Modern Methods and Technologies. Preprints, 2024.

Nguyen, Hoang, et al. Applications of Artificial Intelligence in Mining and Geotechnical Engineering. Elsevier, 2023.

Nong, Setshaba. Use of Artificial Intelligence, Machine Learning and Autonomous Technologies in Mining Industry, South Africa. University of the Witwatersrand, Johannesburg.

Oumaima, Otmani, et al. “Mining Safety Through Artificial Intelligence: A Survey”. Journal of Mines, Metals & Fuels, vol. 72, no. 6, 2024.

Pimpalkar, Archana S., and Ashwini C. Gote. “Utilization of Artificial Intelligence and Machine Learning in the Coal Mining Industry”. AIP Conference Proceedings, vol. 3188, AIP Publishing, 2024.

Satipaldy, Bauyrzhan, et al. “Geotechnology in the Age of AI: The Convergence of Geotechnical Data Analytics and Machine Learning”. Fusion of Multidisciplinary Research, An International Journal, vol. 2, no. 1, 2021, pp. 136–151.

Wang, Yongtao, et al. “Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model”. Energies, vol. 17, no. 20, MDPI, 2024, p. 5205.

Yavari, Ehsan, et al. AI-Enhanced Decision Support for Optimal Fleet Assignment in Open-Pit Mining.

Zvarivadza, Tawanda, et al. “On the Impact of Industrial Internet of Things (IIoT)-Mining Sector Perspectives”. International Journal of Mining, Reclamation and Environment, Taylor & Francis, 2024, pp. 1–39.


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