About

Angelina Anani is an associate professor at the University of Arizona, teaching both graduate and undergraduate courses in mining engineering. She holds a BS (summa cum laude) and a PhD in Mining Engineering from the Missouri University of Science and Technology. With over a decade of research and teaching experience across three universities in two countries, her expertise encompasses mine planning, mine safety, and the modeling and optimization of mining systems, with a focus on sustainability and efficiency. Anani has received notable recognition including the 2022 Freeport-McMoRan Inc. Academic Career Development Grant from the Society for Mining, Metallurgy & Exploration and the 2023 Outstanding Young Professional Award from the Mining and Exploration Division of SME. She is actively involved in professional societies such as the Society of Mining Professors and Women in Mining.

Research topics

• Engineering
• Computer Science
• Artificial Intelligence
• Machine Learning
• Mining engineering
• Data Mining
• Geotechnical engineering
• Geology
• Computer Security
• Statistics
• Forensic engineering
• Waste management
• Mathematics
• Petroleum engineering
• Risk analysis (engineering)
• Construction engineering

Selected publications

• Simulation-based evaluation of energy and operational efficiency for mine haul truck fuel alternatives

  SIMULATION · 2026-02-09

  article

  The growing impact of climate change has prompted global efforts toward decarbonization, including ambitious targets set by the 2016 Paris Agreement. In the mining sector, the main contributors to greenhouse gas (GHG) emissions are loading and haulage, typically carried out using large diesel trucks. This study evaluates alternatives to diesel fuel for mining trucks, focusing on electricity, renewable diesel, and liquefied natural gas (LNG). Using discrete-event simulation (DES), we assessed these fuels based on operational data from an open-pit copper mine over a 24-hour period. Results reaffirm diesel as the current industry standard but highlight electric trucks as the most effective at reducing CO 2 emissions by up to 95%, while also lowering operating costs. However, their success depends heavily on charging infrastructure and efficient energy management. Renewable diesel offers an approximately 90% reduction in CO 2 emissions and is compatible with existing equipment, making it a viable transitional option for operations not yet ready for electrification. LNG, often promoted as a cleaner fuel, resulted in a 69% increase in fuel consumption and a 48% higher CO 2 emissions in this study, questioning its role in decarbonization. Overall, renewable diesel and low-energy electric trucks appear to be the most promising paths toward greener mining. The optimal solution varies by site, depending on infrastructure, operational demands, and decarbonization goals. Further studies are needed to conduct a comparative analysis of different truck fuel types under varying mining conditions, including evaluations of cost-effectiveness and full environmental impact.

  PublisherDOI

• Decision-making for dust suppressant selection in mining roads: a comparative study using FAHP and PROMETHEE II

  International Journal of Mining Reclamation and Environment · 2026-02-16

  article
  PublisherDOI

• A Framework for Business Interruption Risk Modeling in Polymetallic Nodule Deep Sea Mining

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Surface Mine Planning Adaptations for the Integration of Autonomous Haulage Systems: A Review

  Preprints.org · 2026-05-19

  preprintOpen access

  Autonomous Haulage Systems (AHS) are becoming increasingly popular in recent years as mining operations seek to improve productivity and remove workers from hazardous environments. The integration of this technology in a systematic manner implies not only change management in operations, but also deeper perspective into mine planning implications. Currently, existing literature describes AHS and their implementation guidelines with focus on operational safety and autonomous system architecture, without systematically addressing required planning-level adaptations. This study aims to identify how mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review is conducted using the PRISMA methodology with emphasis on identifying the principal aspects of AHS that must be considered in mine planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, alongside ongoing debates regarding optimal road width and load channelization. The study highlights the need for (i) holistic approaches to haul road and mine design, that are aware of technology, geotechnical, and mineral aspects with a data driven perspective (ii) human-systems integration and new needs in human-autonomous collaboration, and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

  PublisherDOI

• A Scientific Visualization Method of Spatiotemporal Displacement Data for Underground Mines: A Case Study at the San Xavier Laboratory

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Mine Planning Adaptations for the Integration of Autonomous Haulage Systems

  Preprints.org · 2026-02-26

  preprintOpen access

  Autonomous Haulage Systems (AHS) are becoming increasingly popular in recent years as mining operations seek to improve productivity and remove workers from hazardous environments. The integration of this technology in a systematic manner implies not only change management in operations, but also deeper perspective into mine planning implications. Currently, existing literature describes AHS and their implementation guidelines with focus on operational safety and autonomous system architecture, without systematically addressing required planning-level adaptations. This study aims to identify how mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review is conducted using the PRISMA methodology with emphasis on identifying the principal aspects of AHS that must be considered in mine planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, alongside ongoing debates regarding optimal road width and load channelization. The study highlights the need for (i) holistic approaches to haul road and mine design, that are aware of technology, geotechnical, and mineral aspects with a data driven perspective (ii) human-systems integration and new needs in human-autonomous collaboration, and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

  PublisherOA PDFDOI

• Holistic insights into uncertainty-based modeling approaches for open pit mine planning

  International Journal of Mining Reclamation and Environment · 2026-01-30

  article
  PublisherDOI

• From Single-Sensor Constraints to Multisensor Integration: Advancing Sustainable Complex Ore Sorting

  Minerals · 2025-10-23 · 3 citations

  articleOpen access

  Processing complex ore remains a challenge due to energy-intensive grinding and complex beneficiation and pyrometallurgical treatments that consume large amounts of water whilst generating significant waste and polluting the environment. Sensor-based ore sorting, which separates ore particles based on their physical or chemical properties before downstream processing, is emerging as a transformative technology in mineral processing. However, its application to complex and heterogeneous ores remain limited by the constraints of single-sensor systems. In addition, existing hybrid sensor strategies are fragmented and a consolidated framework for implementation is lacking. This review explores these challenges and underscores the potential of multimodal sensor integration for complex ore pre-concentration. A multi-sensor framework integrating machine learning and computer vision is proposed to overcome limitations in handling complex ores and enhance sorting efficiency. This approach can improve recovery rates, reduce energy and water consumption, and optimize process performance, thereby supporting more sustainable mining practices that contribute to the United Nations Sustainable Development Goals (UNSDGs). This work provides a roadmap for advancing efficient, resilient, and next-generation mineral processing operations.

  PublisherDOI

• A Comparative Exploration of Machine Learning Techniques for Compressive Strength Prediction in Copper Mine Tailing Concretes

  Mining Metallurgy & Exploration · 2025-04-25

  article
  PublisherDOI

• Sustainable copper supply chains: Evaluating ESG risks through the lens of regulatory compliance and risk assessment strategies

  The Extractive Industries and Society · 2025-04-12 · 7 citations

  article1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Sefiu O. Adewuyi

  University of Arizona

  37 shared

• Nathalie Risso

  Arizona Geological Survey

  14 shared

• Kray Luxbacher

  University of Arizona

  13 shared

• Ignacio Ortiz Flores

  11 shared

• Edward C. Wellman

  University of Arizona

  9 shared

• Wedam Nyaaba

  University of Illinois Chicago

  9 shared

• Kwame Awuah-Offei

  Missouri University of Science and Technology

  7 shared

• Haitao Li

  5 shared

Education

• Doctor of Philosophy, Mining Engineering

  Missouri University of Science and Technology

  2015

Awards & honors

• 2022 Freeport-McMoRan Inc. Academic Career Development Grant…
• 2023 Outstanding Young Professional Award from the Mining an…