Special Issue on AI for Mining

Introduction

The mining industry, as a foundational and strategic sector of the national economy, is facing deteriorating resource endowment, increasing safety pressures, rising labor costs, and tightening environmental constraints. Conventional production models that rely heavily on empirical decision-making and management are inadequate for the evolving demands of high-quality development.

Artificial intelligence (AI) technologies—encompassing machine learning, deep learning, computer vision, natural language processing, large language models, and digital twins—are providing core technical support for the intelligent upgrading of the entire mining value chain. From geological exploration and mine design, through extraction operations and mineral processing, to safety management, equipment maintenance, and full-cycle decision-making, AI is reshaping the underlying logic and operational paradigm of the mining industry.

Against this background, Green and Smart Mining Engineering is pleased to launch the Special Issue on “AI for Mining: Accelerating Transformation in the Mining Industry.” This Special Issue aims to provide a platform for researchers, engineers, and industry practitioners to share recent advances at the intersection of mining engineering and artificial intelligence, explore innovative AI applications across all stages of mining operations, and promote the intelligent, efficient, safe, and resilient development of the mining industry.

Topics covered: 

This Special Issue welcomes original research articles, review articles, and short communications. Topics of interest include, but are not limited to:

AI in geological exploration and resource assessment

• Deep learning-based mineral resource prediction and metallogenic potential assessment
• Intelligent interpretation of geological data and 3D geological modeling
• AI-enabled fusion analysis of geophysical and geochemical data
• Drilling data-driven intelligent identification of orebody boundaries

Intelligent mining and mine automation

• Autonomous perception and unmanned operations of mining equipment and transport systems
• Reinforcement learning-based mining scheduling and path optimization
• Computer vision applications in blasting, loading, and hauling
• Intelligent collaborative control of the entire mining production process
• Intelligent mining processes and system integration for open-pit and underground mines
• AI-based rock fragmentation optimization and blast-induced vibration prediction

Intelligent mineral processing and extraction metallurgy

• Machine learning-based modeling and optimal control of mineral processing
• Ore image recognition and online grade analysis
• Intelligent monitoring of flotation processes and automatic reagent dosing
• Digital twin applications in mineral processing plants
• Collaborative optimization and intelligent decision-making across the entire beneficiation-metallurgy flowsheet

Mine safety and intelligent hazard early warning

• Deep learning-based microseismic signal recognition and hazard early warning
• Intelligent prediction models for gas, water inrush, and roof hazards
• Computer vision-driven identification of unsafe behaviors in mines
• Intelligent ventilation control and AI-assisted emergency rescue decision-making
• Machine learning-based geotechnical risk assessment, slope stability analysis, and rockburst prediction

Mining big data and intelligent decision-making

• Fusion governance of multi-source heterogeneous mining data and knowledge graph construction
• Applications of large language models (LLMs) in mining knowledge management
• Data-driven equipment health management and fault diagnosis
• AI-assisted online assessment of life-cycle costs and carbon emissions in mining
• Digital mine construction and digital transformation pathways

Emerging AI technologies in mining

• Generative AI for mine design and process optimization
• Deployment of edge computing and AIoT (Artificial Intelligence of Things) in mines
• Federated learning for collaborative modeling under mining data privacy constraints
• Metaverse and virtual reality applications in mine training and operations

Important deadline

Submission deadline: 31 July 2027

Expected publication date: 31 December 2027

Guest Editors

Assoc. Prof. Dr. Manoj Khandelwal, Federation University Australia, Australia. Email: m.khandelwal@federation.edu.au

Assoc. Prof. Dr. Junlong Shang, The University of Manchester, UK. Email: junlong.shang@manchester.ac.uk

Prof. Dr. Xiaobo Liu, University of Science and Technology Liaoning, China. Email: lxb_58@163.com

Prof. Dr. Shiwen Xie, Central South University, China. Email: sw.xie@csu.edu.cn

Asst. Prof. Dr. Chengkai Fan, Université Laval, Canada. Email: chengkai.fan@gmn.ulaval.ca