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.
1. Introduction - p. xix 2. Chapter 1. The evolution of artificial intelligence in mining - p. 1 3. Abstract - p. 1 4. Chapter outline - p. 1 5. 1.1 Introduction - p. 1 6. 1.2 Early applications of artificial intelligence in mining - p. 6 7. 1.3 Applications of artificial intelligence in mineral mining method selection - p. 10 8. 1.4 Artificial intelligence application for operation automation in mineral mining - p. 11 9. 1.5 Artificial intelligence in ethical and green mineral processing - p. 20 10. 1.6 The climate-smart mining - p. 21 11. References - p. 67 12. Chapter 2. Advances in acid mine drainage management through artificial intelligence - p. 78 13. Abstract - p. 78 14. Chapter outline - p. 78 15. 2.1 Introduction - p. 78 16. 2.2 Acid mine drainage processes - p. 79 17. 2.3 Management of acid mine drainage processes - p. 95 18. 2.4 Circular economy and resource recirculation - p. 110 19. 2.5 Sustainability and environmental impact assessment aspects - p. 114 20. 2.6 Artificial intelligence in acid mine drainage risk prediction - p. 124 21. 2.7 Conclusions - p. 156 22. References - p. 157 23. Chapter 3. Advancing mining maintenance: integrating machine learning for proactive corrosion management - p. 179 24. Chapter 4. Revolutionizing brine mining through artificial intelligence-assisted techniques - p. 196 25. Chapter 5. Urban mining and artificial intelligence: challenges and opportunities - p. 229 26. Chapter 6. Wastewater mining: a new frontier for artificial intelligence in mining - p. 249 27. Chapter 7. Green mining with artificial intelligence: a path to sustainability - p. 310 28. Chapter 8. Enhancing safety and minimizing risk in mining processes with artificial intelligence - p. 355 29. Chapter 9. The future of the mining industry with artificial intelligence - p. 384 30. Index - p. 409
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.
1. Introduction - p. xix 2. Chapter 1. The evolution of artificial intelligence in mining - p. 1 3. Abstract - p. 1 4. Chapter outline - p. 1 5. 1.1 Introduction - p. 1 6. 1.2 Early applications of artificial intelligence in mining - p. 6 7. 1.3 Applications of artificial intelligence in mineral mining method selection - p. 10 8. 1.4 Artificial intelligence application for operation automation in mineral mining - p. 11 9. 1.5 Artificial intelligence in ethical and green mineral processing - p. 20 10. 1.6 The climate-smart mining - p. 21 11. References - p. 67 12. Chapter 2. Advances in acid mine drainage management through artificial intelligence - p. 78 13. Abstract - p. 78 14. Chapter outline - p. 78 15. 2.1 Introduction - p. 78 16. 2.2 Acid mine drainage processes - p. 79 17. 2.3 Management of acid mine drainage processes - p. 95 18. 2.4 Circular economy and resource recirculation - p. 110 19. 2.5 Sustainability and environmental impact assessment aspects - p. 114 20. 2.6 Artificial intelligence in acid mine drainage risk prediction - p. 124 21. 2.7 Conclusions - p. 156 22. References - p. 157 23. Chapter 3. Advancing mining maintenance: integrating machine learning for proactive corrosion management - p. 179 24. Chapter 4. Revolutionizing brine mining through artificial intelligence-assisted techniques - p. 196 25. Chapter 5. Urban mining and artificial intelligence: challenges and opportunities - p. 229 26. Chapter 6. Wastewater mining: a new frontier for artificial intelligence in mining - p. 249 27. Chapter 7. Green mining with artificial intelligence: a path to sustainability - p. 310 28. Chapter 8. Enhancing safety and minimizing risk in mining processes with artificial intelligence - p. 355 29. Chapter 9. The future of the mining industry with artificial intelligence - p. 384 30. Index - p. 409
