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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science Publishing Co Inc
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Produkt-Hinweis
Broschur/Paperback
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Maße
Höhe: 228 mm
Breite: 155 mm
Dicke: 21 mm
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ISBN-13
978-0-443-28911-8 (9780443289118)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
Herausgeber*in
Amir Razmjou is an Associate Professor at Edith Cowan University and the Leader of the Mineral Recovery Research Centre (MRRC).
Professor, Mechatronics-Biomechanics and an ARC DECRA Fellow, Macquarie University, Australia
1. The Evolution of AI in Mining: A Historical Overview
2. AI-Powered Techniques for Improved Continental Mining
3. The Future of Ocean Mining with Artificial Intelligence
4. Revolutionizing Brine Mining through AI-Assisted Techniques
5. Urban Mining and AI: Challenges and Opportunities
6. Wastewater Mining: A New Frontier for AI in Mining
7. Green Mining with AI: A Path to Sustainability
8. Enhancing Safety and Minimizing Risk in Mining Processes with AI
9. AI-Assisted Resource Planning and Management in Mining
10. The Future of the Mining Industry with Artificial Intelligence: Opportunities and Challenge
