Intelligent Mining Systems & Engineering Technologies

Advanced Technologies in Mineral Resources — AI, data analytics, integrated modeling, and intelligent mining.

Session A Session B Session C Session D Session E Session F Session G

Sub-session description

Academic Scope

This sub-session focuses on the application of artificial intelligence and advanced engineering technologies to improve the efficiency, reliability, and sustainability of mining operations. The integration of intelligent control systems, automation technologies, and cyber-physical systems is transforming traditional mining practices into data-driven and highly adaptive operational environments.

The sub-session welcomes contributions related to intelligent mining equipment, AI-based fault diagnosis, predictive maintenance, advanced motor drives, power system monitoring, and energy optimization. Research addressing automation, autonomous systems, digital control technologies, and smart operational frameworks is particularly encouraged.

The session aims to promote innovative engineering solutions that enhance equipment performance, operational safety, resource efficiency, and the overall digital transformation of the mining industry.

ANN-based fault diagnosis and condition monitoring of electrical machines Advanced electrical motor drives and intelligent control systems Predictive maintenance of mining equipment using AI techniques Power system monitoring and energy optimization using AI Smart mining systems, automation, and cyber-physical systems

Session Metadata

CodeE1

FormatMixed format

Main Convener Assoc. Prof. PhD Ariunbolor Purvee, School of Gelogy and Minign Engineering, Mongolian University of Science and Technology

Co-Conveners Prof. Khavalbolot Kyelgyenbai, Mongolian University of Science and Technology

Target audience: Researchers in mining engineering, geoscience, and AI Engineers in mining operations and equipment systems Data scientists and AI specialists in industrial applications Industry professionals in digital mining and automation Graduate students and early-career researchers

Recommended Contributions

Original research, case studies, applied methodological papers, and evidence-based perspectives aligned with the scope of this sub-session.

Author Guidance

Abstracts should be submitted in English (250–300 words), clearly linked to this sub-session code, and should state objective, methodology, key results, and relevance.

Review Fit

Submissions should demonstrate scientific relevance, originality, methodological quality, clarity, and alignment with the session theme.

Registration

Fees & Bank Transfer

Use the details below to transfer your registration fee. After transferring, log in and confirm the payment on your submission page — the secretariat will verify the receipt.

💳 Conference fee

Regular

150 USD

≈ 539199 ₮

🏦 Bank transfer details