Artificial Intelligence in Future Mining (Cognitive Data Science in Sustainable Computing) - Softcover

Softcover

ISBN 10: 0443289115 ISBN 13: 9780443289118

Publisher: Academic Press, 2025

Synopsis

Artificial Intelligence in Future Mining explores the latest developments in the use of artificial intelligence (AI) in mining and how it will impact the industry’s future. The application of data science and artificial intelligence in future mining involves using advanced technologies to optimize operations, improve decision-making, and enhance safety and sustainability in the industry. After a brief history of AI in mining, the book's editors look closely at different AI techniques used. Chapters explore ocean mining, brine mining, and urban mining. With an eye towards sustainability, the editors then review the future of wastewater mining and green mining.

The book wraps up with chapters on safety and risk, resource planning, and a larger discussion of the opportunities and challenges of mining with AI in the future. This book is a must-have for researchers and professionals who find themselves at the intersection of mining, engineering, and data science.

• Provides high-level analyses as well as practical insights and real-world examples on the impact of AI on future mining
• Includes case studies on the application of data processing, the Internet of Things, and artificial intelligence in environmental sensing
• Provides in-depth discussion of the future implications of AI on the mining industry at the end of each chapter

About this title

About the Authors

Amir Razmjou is an Associate Professor at Edith Cowan University and the Leader of the Mineral Recovery Research Centre (MRRC).

Associate Professor Amir Razmjou (PhD from the University of New South Wales (UNSW), Sydney, Australia, 2012) is an experienced academic and industry professional with over 20 years of expertise in desalination, water treatment, membrane technology, and mineral processing. As a Board Director of the Membrane Society of Australasia (MSA) and Founder of the Mineral Recovery Research Centre (MRRC) at Edith Cowan

University (ECU), Western Australia, Associate Professor Razmjou has made significant contributions to the fields of mining and resource extraction, particularly in lithium processing.

He has published over 200 peer-reviewed articles and secured research funding

exceeding $9.2 million AUD. Dr. Razmjou has received awards such as the 2024 WA FHRI

Fund Innovation Fellow, the 2023 MSA Industry Innovation Award, and the 2021 UTS Chancellor Research Fellow. He has supervised more than 40 master’s and Ph.D. candidates and serves in editorial roles for journals such as Desalination, DWT, and JWPE. At MRRC, he has established a DLE line, including various processes such as membranes, ion exchange, and adsorption at laboratory and pilot scales. His research also includes developing and implementing advanced technologies for DLE’s pretreatment and posttreatment to enhance the Li/TDS ratio and purify the final product to battery-grade

quality"

Mohsen Asadnia is a Professor and group lader in Mechatronics-biomechanics and at Macquarie University, Australia. He received his PhD degree in Mechanical Engineering from Nanyang Technological University, Singapore. Prior to joining Macquarie University, Mohsen had several teaching and research roles with the University of Western Australia, Massachusetts Institute of Technology and Nanyang Technological University. His research interest lies in environmental/ biomedical sensors, Artificial Intelligence, and bio-inspired sensing.

From the Back Cover

Artificial Intelligence in Future Mining explores the latest developments in the use of artificial intelligence (AI) in mining and how it will impact the industry’s future. The application of data science and artificial intelligence in future mining involves using advanced technologies to optimize operations, improve decision-making, and enhance safety and sustainability in the industry. After a brief history of AI in mining, the editors look closely at different AI techniques used in mining. The following chapters explore ocean mining, brine mining, and urban mining. With an eye towards sustainability, the editors then review the future of wastewater mining and green mining. The book wraps up with chapters on safety and risk, resource planning, and a larger discussion of the opportunities and challenges of mining with AI in the future. This book is a must-have for researchers and professionals who find themselves at the intersection of mining, engineering, and data science

Edition details

• Publisher: Academic Press
• Publication date: 2025
• Language: English
• ISBN 10: 0443289115
• ISBN 13: 9780443289118
• Binding: Paperback
• Number of pages: 442
• Editor: Razmjou