Artificial Intelligence in Future Mining
- 1st Edition - January 22, 2025
- Editors: Amir Razmjou, Mohsen Asadnia
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 8 9 1 1 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 8 9 1 2 - 5
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… Read more
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Request a sales quoteArtificial 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.
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
Academics, researchers, and industry professionals in fields that intersect with mining, engineering, and AI/data science, Mining industry, government agencies, industry organizations/associations, AI researchers
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Introduction
- Chapter 1. The evolution of artificial intelligence in mining
- Abstract
- Chapter outline
- 1.1 Introduction
- 1.2 Early applications of artificial intelligence in mining
- 1.3 Applications of artificial intelligence in mineral mining method selection
- 1.4 Artificial intelligence application for operation automation in mineral mining
- 1.5 Artificial intelligence in ethical and green mineral processing
- 1.6 The climate-smart mining
- References
- Chapter 2. Advances in acid mine drainage management through artificial intelligence
- Abstract
- Chapter outline
- 2.1 Introduction
- 2.2 Acid mine drainage processes
- 2.3 Management of acid mine drainage processes
- 2.4 Circular economy and resource recirculation
- 2.5 Sustainability and environmental impact assessment aspects
- 2.6 Artificial intelligence in acid mine drainage risk prediction
- 2.7 Conclusions
- References
- Chapter 3. Advancing mining maintenance: integrating machine learning for proactive corrosion management
- Abstract
- Chapter outline
- 3.1 Introduction
- 3.2 Pipeline corrosion in mining
- 3.3 Internal corrosion
- 3.4 External corrosion
- 3.5 Machine learning application
- 3.6 Overview of adopted machine learning techniques
- 3.7 Supervised machine learning algorithms
- 3.8 Unsupervised machine learning algorithm
- 3.9 Reinforcement machine learning algorithm
- 3.10 Machine learning techniques in corrosion modeling
- 3.11 Conclusions
- References
- Chapter 4. Revolutionizing brine mining through artificial intelligence-assisted techniques
- Abstract
- Chapter outline
- 4.1 Introduction
- 4.2 Types and definitions of brine resources and brine mining techniques
- 4.3 Principles and benefits of artificial intelligence-assisted brine mining
- 4.4 Artificial intelligence-assisted techniques in brine mining case studies
- 4.5 Challenges and limitations of artificial intelligence-assisted brine mining techniques
- 4.6 Future directions for artificial intelligence-assisted brine mining techniques
- Acknowledgments
- References
- Chapter 5. Urban mining and artificial intelligence: challenges and opportunities
- Abstract
- Chapter outline
- 5.1 Introduction
- 5.2 Importance of urban mining
- 5.3 Resources of urban mining
- 5.4 Artificial intelligence approach in urban mining
- 5.5 Conclusion
- References
- Chapter 6. Wastewater mining: a new frontier for artificial intelligence in mining
- Abstract
- Chapter outline
- 6.1 Introduction
- 6.2 Understanding mining wastewater
- 6.3 Role of artificial intelligence in mining wastewater
- 6.4 Challenges and future directions
- 6.5 Conclusions
- AI disclosure
- Abbreviations
- References
- Chapter 7. Green mining with artificial intelligence: a path to sustainability
- Abstract
- Chapter outline
- 7.1 Introduction
- 7.2 Sustainable development goal-based artificial intelligence and Internet of Things introduction in the mining sector
- 7.3 Artificial intelligence/Internet of Things’s impact on sustainable development goal in the mining sector
- 7.4 Effect of Internet of Things and artificial intelligence on robots and automation in the mining sector according to sustainable development
- 7.5 Capabilities and limitations of artificial intelligence in sustainable development for mine designing and planning
- 7.6 The future of robotics and automation in mining
- 7.7 Discussion
- 7.8 Conclusion
- References
- Chapter 8. Enhancing safety and minimizing risk in mining processes with artificial intelligence
- Abstract
- Chapter outline
- 8.1 Introduction
- 8.2 Real-time monitoring and predictive analytics
- 8.3 Autonomous vehicles and equipment for hazardous environments
- 8.4 Conclusion
- References
- Chapter 9. The future of the mining industry with artificial intelligence
- Abstract
- Chapter outline
- 9.1 Introduction
- 9.2 Recent advancements in artificial intelligence and autonomous solutions in mining operations
- 9.3 Optimizing operations
- 9.4 Hazard management
- 9.5 Potential applications of artificial intelligence in mining industry
- 9.6 Conclusion and future directions
- References
- Index
- No. of pages: 260
- Language: English
- Edition: 1
- Published: January 22, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443289118
- eBook ISBN: 9780443289125
AR
Amir Razmjou
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"
Affiliations and expertise
Amir Razmjou is an Associate Professor at Edith Cowan University and the Leader of the Mineral Recovery Research Centre (MRRC).MA
Mohsen Asadnia
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.
Affiliations and expertise
Professor, Mechatronics-Biomechanics and an ARC DECRA Fellow, Macquarie University, Australia