𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗮𝗻𝗱 𝗦𝗺𝗮𝗿𝘁 𝗠𝗶𝗻𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗳𝗼𝗿 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀

𝗖𝗔𝗟𝗟 𝗙𝗢𝗥 𝗣𝗔𝗣𝗘𝗥𝗦

𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗮𝗻𝗱 𝗦𝗺𝗮𝗿𝘁 𝗠𝗶𝗻𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗳𝗼𝗿 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀

𝗝𝗼𝘂𝗿𝗻𝗮𝗹:
International Journal of Mining, Reclamation and Environment

𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗿:
Taylor & Francis Group

𝗠𝗮𝗻𝘂𝘀𝗰𝗿𝗶𝗽𝘁 𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲:
31 October 2026

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲

The mining industry is rapidly transitioning from conventional practices to intelligent, data-driven, and autonomous operations. Growing global demand for strategic minerals essential to net-zero technologies—including electric vehicle batteries, solar photovoltaics, and hydrogen systems—is accelerating the need for more efficient, safer, and environmentally responsible mining practices.

This Special Issue focuses on the latest advancements in digital and smart mining technologies that are transforming the sector. Emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), robotics, machine learning, autonomous systems, digital twins, and advanced analytics are enabling mining organizations to improve productivity, operational reliability, decision-making, worker safety, and environmental sustainability.

Researchers, industry practitioners, and technology innovators are invited to contribute cutting-edge research and practical applications that support the development of connected, autonomous, and sustainable mining systems.

𝗧𝗼𝗽𝗶𝗰𝘀 𝗼𝗳 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁

Submissions may address, but are not limited to:

• Intelligent mining and autonomous vehicle technologies
• Advanced robotics and automation for zero-entry mining
• AI-driven decision support systems and operational optimization
• IoT-enabled real-time monitoring, predictive maintenance, and safety enhancement
• Digital twin applications in mine planning and operational management
• Machine learning for predictive analytics, anomaly detection, and process optimization
• Smart sensing technologies and multi-nodal data fusion
• Private mobile networks (5G/6G), wireless connectivity, and edge computing in mining
• Cybersecurity and resilience strategies for mining infrastructure
• Digital technologies for sustainable mine waste and tailings management
• Physical AI and robotics-enabled smart mining solutions
• Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) applications in mining operations and training

𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝗠𝗮𝘁𝘁𝗲𝗿𝘀

As the mining sector embraces digital transformation, smart technologies are becoming critical for achieving sustainability goals while maintaining operational excellence. The integration of AI, automation, connectivity, and digital intelligence offers significant opportunities to reduce environmental impacts, enhance safety, improve resource efficiency, and support the future of responsible mining.

This Special Issue aims to showcase innovative solutions that can drive the next generation of sustainable mining operations worldwide.

𝗚𝘂𝗲𝘀𝘁 𝗘𝗱𝗶𝘁𝗼𝗿𝘀

Selahattin Akdag
WA School of Mines (WASM), Curtin University, Australia
Email: sela.akdag@curtin.edu.au

Wei Xiang
School of Computing, Engineering & Mathematical Sciences, La Trobe University, Australia
Email: w.xiang@latrobe.edu.au

Frank den Hartog
Network Engineering and Cybersecurity, University of Canberra, Australia
Email: frank.denhartog@canberra.edu.au

𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀

• Manuscripts should be submitted through the International Journal of Mining, Reclamation and Environment online submission system.
• All submissions will undergo a rigorous peer-review process to ensure quality, originality, and relevance to the Special Issue.
• Authors should follow the journal’s official author guidelines before submission.

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗝𝗼𝘂𝗿𝗻𝗮𝗹

The International Journal of Mining, Reclamation and Environment publishes high-quality research on mining engineering, environmental management, mine reclamation, sustainability, technological innovation, and operational excellence in the global mining industry.

𝗣𝗼𝘀𝘁𝗲𝗱 𝗼𝗻 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗦𝗲𝘁𝘂 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰𝘀 — 𝗣𝗿𝗲𝗺𝗶𝗲𝗿 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗼𝗿 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 & 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻

Visit official website of the publisher