Advanced Analytics for Pit-To-Port Optimization in the Mining Industry
1st Edition
By Ali Soofastaei
Copyright Year © 2026
ISBN:
Publisher: CRC Press
Published:
Number of Pages:
Access this book on the publisher's site
Description
Book Description
Advanced Analytics for Pit-to-Port Optimization in the Mining Industry provides a comprehensive framework for improving end-to-end mining performance through data, AI, digital twins, simulation, optimization, and decision intelligence. Covering the full value chain from orebody and pit operations through processing, logistics, stockyards, port operations, ship loading, and commercial delivery, the book explains how analytics can be used to diagnose bottlenecks, stabilize ore flow, reduce variability, improve equipment productivity, optimize product quality, reduce energy and carbon intensity, and increase business value. Written for mining engineers, operations leaders, data scientists, researchers, consultants, and advanced students, it combines technical methods with practical implementation guidance, governance models, case-study logic, and templates for industrial deployment. Key Features: •End-to-end coverage from orebody and pit operations to logistics, port stockyards, ship loading, and customer delivery. •Balanced treatment of mining engineering, systems engineering, data architecture, AI, optimization, simulation, and business value. •Strong emphasis on variability, bottlenecks, queueing, material tracking, product quality, and the operational interfaces where value is often lost. •Practical guidance for implementation, including maturity assessment, use-case prioritization, governance, operating model design, benefit tracking, and model assurance. •Examples and templates that can support industry projects, postgraduate teaching, executive briefings, and technical workshops.
Table of Contents
Chapter 1. The Pit-to-Port Mining Value Chain Chapter 2. Systems Thinking for Mining Operations Chapter 3. Value Drivers, KPIs, and Performance Architecture Chapter 4. Analytics Maturity in Mining Organizations Chapter 5. Operational Data Sources in Pit-to-Port Systems Chapter 6. Data Quality, Contextualization, and Master Data Chapter 7. Data Architecture for Advanced Mining Analytics Chapter 8. Governance, Security, and Model Assurance Chapter 9. Orebody Knowledge, Geometallurgy, and Value-Based Planning Chapter 10. Drill and Blast Analytics Chapter 11. Loading, Hauling, and Fleet Optimization Chapter 12. Safety, Autonomy, and Human Factors in Pit Analytics Chapter 13. Crushing, Conveying, and Ore Handling Analytics Chapter 14. Stockpile Strategy, Blending, and Material Tracking Chapter 15. Processing Performance and Mine-to-Plant Integration Chapter 16. Product Quality, Reconciliation, and Customer Specifications Chapter 17. Mine-to-Port Logistics and Capacity Synchronization Chapter 18. Port Stockyard and Terminal Operations Analytics Chapter 19. Berth Scheduling, Ship Loading, and Demurrage Reduction Chapter 20. Integrated Pit-to-Port Planning and Scheduling Chapter 21. Statistical Analytics and Causal Reasoning Chapter 22. Machine Learning for Predictive Mining Operations Chapter 23. Simulation and Digital Twins for Pit-to-Port Systems Chapter 24. Mathematical Optimization and Prescriptive Analytics Chapter 25. Decision Intelligence, Generative AI, and Autonomous Operations Chapter 26. Business Case Development and Benefit Realization Chapter 27. Energy, Carbon, Water, and ESG Analytics Chapter 28. Change Management and Analytics Operating Model Chapter 29. Case Studies, Templates, and Implementation Roadmap Chapter 30. The Future of Pit-to-Port Optimization
Editor
Dr. Ali Soofastaei
Biography
Dr. Ali Soofastaei is a global artificial intelligence (AI) projects leader, an international keynote speaker, and a professional author.
He completed his Ph.D. and Postdoctoral Research Fellow at The University of Queensland, Australia, in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As a scientific supervisor, for many years, he has provided practical guidance to undergraduate and postgraduate students in mechanical and mining engineering and information technology.
Dr. Soofastaei has more than fifteen years of academic experience as an Assistant Professor and leader of global research activities. Results from his research and development projects have been published in international journals and keynote presentations; He has presented his practical achievements at conferences in the United States, Europe, Asia, and Australia. He has been involved in industrial research and development projects in several industries, including oil and gas (Royal Dutch Shell); steel (Danieli); and mining (BHP, Rio Tinto, Anglo American, and Vale). His extensive practical experience in the industry has equipped him to work with complex industrial problems in highly technical and multi-disciplinary teams. Dr. Soofastaei is working actively with some prestigious global publishers same as Mc Graw-Hill Education, Intech Open, Springer, and CRC Press as an author and academic editor.
