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Artificial intelligence in future mining /

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 optimi...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Razmjou, Amir (Editor), Asadnia, Mohsen (Editor)
Format: eBook
Language:English
Published: London ; San Diego, CA : Academic Press, [2025]
Series:Cognitive data science in sustainable computing.
Subjects:
Mineral industries > Technological innovations.
Artificial intelligence.
Data mining.
Intelligence artificielle.
Exploration de données (Informatique)
Mines > Industrie > Innovations.
artificial intelligence.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Artificial Intelligence in Future Mining
  • Copyright Page
  • Contents
  • List of contributors
  • Preface
  • Introduction
  • 1 The evolution of artificial intelligence in mining
  • 1.1 Introduction
  • 1.1.1 Definition of artificial intelligence and its significance in the mining industry
  • 1.1.2 Brief overview of the history of mineral mining and technological advancements
  • 1.2 Early applications of artificial intelligence in mining
  • 1.2.1 Historical overview of early artificial intelligence and automation in mining
  • 1.2.2 Early attempts at data analysis and optimization in mineral mining
  • 1.3 Applications of artificial intelligence in mineral mining method selection
  • 1.4 Artificial intelligence application for operation automation in mineral mining
  • 1.4.1 Application of artificial intelligence in mineral prospecting and exploration
  • 1.4.2 Application of artificial intelligence in mine planning
  • 1.4.3 Application of artificial intelligence in machine operation in mineral mining
  • 1.4.4 Application of artificial intelligence in drilling and blasting in mineral mining
  • 1.4.5 Application of artificial intelligence in mineral processing
  • 1.4.6 Application of artificial intelligence in environmental issues in mineral mining
  • 1.4.6.1 Environmental impact of artificial intelligence-driven mining
  • 1.4.6.2 Artificial intelligence in environmental issues in mineral mining
  • 1.5 Artificial intelligence in ethical and green mineral processing
  • 1.6 The climate-smart mining
  • 1.6.1 Climate mitigation in climate-smart mining: strategies and impact
  • 1.6.2 Climate adaptation strategies in climate-smart mining: building resilience for sustainable resource extraction
  • 1.6.3 Reducing material impacts in climate-smart mining: strategies for sustainable resource extraction.
  • 1.6.4 Creating marketing opportunities in climate-smart mining: sustainable resource extraction in a green economy
  • 1.6.5 Renewable energy integration in climate-smart mining: a path to sustainable resource extraction
  • 1.6.6 Resource efficiency in climate-smart mining: optimizing sustainable resource extraction
  • 1.6.7 Reuse and recycling of low-carbon minerals in climate-smart mining: towards a circular resource economy
  • 1.6.8 Leveraging carbon finance instruments in climate-smart mining: a path to sustainable resource extraction
  • 1.6.9 Energy efficiency in the mineral value chain: a cornerstone of climate-smart mining
  • 1.6.10 Innovation waste solutions in climate-smart mining: advancing sustainable resource extraction
  • 1.6.11 Low-carbon mineral supply chain management: a key driver of climate-smart mining
  • 1.6.12 Robust geological data management: a cornerstone of climate-smart mining
  • 1.6.13 Gender and multistakeholder engagement: key drivers of climate-smart mining
  • 1.6.14 Strong governance and regulatory framework: cornerstones of climate-smart mining
  • 1.6.15 Forest-smart mining with landscape management: a cornerstone of climate-smart mining
  • 1.6.16 De-risking investments for low-carbon minerals: a key driver of climate-smart mining
  • References
  • 2 Advances in acid mine drainage management through artificial intelligence
  • 2.1 Introduction
  • 2.2 Acid mine drainage processes
  • 2.2.1 Formation and characteristics
  • 2.2.2 Sources
  • 2.2.3 Mine impacted water classification
  • 2.2.4 Environmental impact control and prevention
  • 2.3 Management of acid mine drainage processes
  • 2.3.1 Acid mine drainage management challenges
  • 2.3.1.1 Global
  • 2.3.1.2 Australia
  • 2.3.2 Corporate governance and frameworks
  • 2.3.3 Classification frameworks for mine waste materials
  • 2.4 Circular economy and resource recirculation.
  • 2.4.1 Water
  • 2.4.2 Sulfuric acid, metals, and rare earth elements
  • 2.4.3 Sludge and mining residues reuse
  • 2.5 Sustainability and environmental impact assessment aspects
  • 2.5.1 Sustainability and climate change in acid mine drainage
  • 2.5.2 Considerations in emission analysis of acid mine drainage treatment
  • 2.6 Artificial intelligence in acid mine drainage risk prediction
  • 2.6.1 Operational approaches to acid mine drainage prediction
  • 2.6.1.1 Geochemical tests
  • 2.6.1.2 Geochemical-hydrological models
  • 2.6.1.3 Physical methods
  • 2.6.1.4 Biological approaches
  • 2.6.1.5 Challenges with conventional approaches
  • 2.6.2 Artificial intelligence
  • 2.6.2.1 Mine impacted drainage quality prediction
  • 2.6.2.2 Quantitative prediction of acid mine drainage
  • 2.6.2.3 Challenges in the adoption of artificial intelligence in acid mine drainage management
  • 2.7 Conclusions
  • References
  • 3 Advancing mining maintenance: integrating machine learning for proactive corrosion management
  • 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
  • 3.7.1 Neural networks and deep learning
  • 3.7.2 Ensemble, tree-based, and boosting methods
  • 3.7.3 Regression algorithms
  • 3.7.4 Support vector methods
  • 3.7.5 Time series and similarity-based methods
  • 3.8 Unsupervised machine learning algorithm
  • 3.9 Reinforcement machine learning algorithm
  • 3.10 Machine learning techniques in corrosion modeling
  • 3.11 Conclusions
  • References
  • 4 Revolutionizing brine mining through artificial intelligence-assisted techniques
  • 4.1 Introduction
  • 4.2 Types and definitions of brine resources and brine mining techniques
  • 4.2.1 Categories of brine resources.
  • 4.2.2 Industrial processes for brine mining
  • 4.2.2.1 Solar or vacuum evaporation with sequential precipitation
  • 4.2.2.2 Electrodialysis/membrane process
  • 4.2.2.3 Membrane distillation crystallization
  • 4.2.2.4 Adsorption/desorption
  • 4.2.2.5 Electrochemical processes
  • 4.3 Principles and benefits of artificial intelligence-assisted brine mining
  • 4.3.1 General concept of artificial intelligence-techniques
  • 4.3.2 Increasing efficiency and productivity using artificial intelligence-techniques
  • 4.3.3 Cost reduction
  • 4.3.4 Improving safety and environmental sustainability
  • 4.4 Artificial intelligence-assisted techniques in brine mining case studies
  • 4.4.1 Exploration and production
  • 4.4.2 Robotics and automation
  • 4.4.3 Monitoring, controlling and predictive maintenance
  • 4.4.4 Characterization of brine
  • 4.5 Challenges and limitations of artificial intelligence-assisted brine mining techniques
  • 4.5.1 Data availability and quality
  • 4.5.2 Technical expertise and training
  • 4.6 Future directions for artificial intelligence-assisted brine mining techniques
  • 4.6.1 Integration with other technologies
  • 4.6.2 Collaboration and knowledge sharing
  • 4.6.3 Regulatory frameworks and standards
  • Acknowledgments
  • References
  • 5 Urban mining and artificial intelligence: challenges and opportunities
  • 5.1 Introduction
  • 5.2 Importance of urban mining
  • 5.3 Resources of urban mining
  • 5.3.1 E-waste in urban mining
  • 5.3.2 Water/wastewater treatment in urban mining
  • 5.3.3 Building in urban mining
  • 5.4 Artificial intelligence approach in urban mining
  • 5.5 Conclusion
  • References
  • 6 Wastewater mining: a new frontier for artificial intelligence in mining
  • 6.1 Introduction
  • 6.2 Understanding mining wastewater
  • 6.2.1 Overview of mining wastewater
  • 6.2.2 Mining wastewater characteristics.
  • 6.2.3 Environmental impact of mining activities and wastewater
  • 6.2.4 Economic benefit of mining wastewater
  • 6.2.5 Mining wastewater treatment techniques
  • 6.3 Role of artificial intelligence in mining wastewater
  • 6.3.1 Artificial intelligence in process controlling and optimization
  • 6.3.2 Artificial intelligence in outlier detection
  • 6.3.3 Artificial intelligence in water quality monitoring
  • 6.3.4 Artificial intelligence in mineral extraction
  • 6.3.5 Popular artificial intelligence algorithm in water and wastewater research
  • 6.3.6 Artificial intelligence case studies in mining
  • 6.3.6.1 Artificial intelligence in the prediction of coal quality
  • 6.3.6.2 Monitoring the impact of various restoration measures on the Du River ecology
  • 6.3.6.3 Improving and monitoring the Têt River's water quality
  • 6.3.6.4 Analyzing the fate and transport of Cr in rivers and crops
  • 6.3.6.5 Support vector machines application in classifying mineralized zones
  • 6.3.6.6 Machine learning optimization for mine water treatment
  • 6.4 Challenges and future directions
  • 6.4.1 Challenges
  • 6.4.2 Future directions
  • 6.4.2.1 Energy source
  • 6.4.2.2 Process control
  • 6.4.2.3 Emerging technologies
  • 6.5 Conclusions
  • AI disclosure
  • Abbreviations
  • References
  • 7 Green mining with artificial intelligence: a path to sustainability
  • 7.1 Introduction
  • 7.1.1 The notion of sustainable development
  • 7.1.2 Necessity of sustainable mining
  • 7.1.3 Mining's effects on metrics for sustainable development
  • 7.2 Sustainable development goal-based artificial intelligence and Internet of Things introduction in the mining sector
  • 7.2.1 Advanced mining automation
  • 7.3 Artificial intelligence/Internet of Things's impact on sustainable development goal in the mining sector.