About

Kray Luxbacher is the Executive Director and Head of the School of Mining Engineering & Mineral Resources at the University of Arizona, where he also holds the Gregory H. and Lisa S. Boyce Leadership Chair in Mining & Geological Engineering. He is a member of the Graduate Faculty and has earned multiple degrees from Virginia Tech, including a PhD in Mining Engineering, with a dissertation focused on time-lapse passive seismic velocity tomography of longwall coal mines. His educational background also includes a Graduate Certificate in Engineering Education, an MS in Mining Engineering, and a BS in Mining Engineering, all from Virginia Tech. His research encompasses a broad range of topics within mining and geological engineering, including seismic tomography, mine fire classification, methane gas modeling, and the development of advanced monitoring and safety systems for underground mines. Luxbacher has contributed to the field through numerous publications, including journal articles, book chapters, and conference proceedings, addressing issues such as mine ventilation, fire safety, seismic imaging, and the application of neural networks in mining safety. He holds a Professional Engineer license from the Virginia Department of Professional and Occupational Regulation and has been actively involved in advancing mining safety, automation, and innovative methodologies for mine monitoring and risk assessment.

Research topics

• Computer Science
• Environmental science
• Engineering
• Artificial Intelligence
• Waste management
• Geology
• Simulation
• Econometrics
• Mathematics
• Statistics
• Chemistry

Selected publications

• Eliminating Barriers for the Implementation of Automation in the Mining Industry

  Mining Metallurgy & Exploration · 2026-03-30

  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

• Quantifying Topographic Influences on Rockfall Trajectories in Open Pit Mines Using Stochastic Rockfall Modeling

  2025-06-08

  articleSenior author

  ABSTRACT: Rockfall remains one of the most critical and least understood hazards faced in open pit mining. The factors controlling when and where rockfalls occur are abundant and highly variable site to site, hindering development of effective tools for proactive rockfall hazard mapping at scales relevant to mining operations. Observations from controlled rockfall tests on mined pit slopes conducted by NIOSH support previous studies, highlighting the significant influence of sub-bench-scale topographic features on rockfall trajectories. Specifically, bench face protrusions at a lower angle than the overall bench face, which tend to "launch" rocks into trajectories with greater horizontal velocity components. While often described by previous researchers, the phenomenon is rarely evaluated quantitatively. The influence of launch features on rockfall trajectories and kinetics can be evaluated using detailed stochastic 2D rockfall modeling. This study presents the preliminary results of such modeling, which focuses on primary rockfall impact distances beneath these launch features using 27 systematic combinations of launch feature widths, angles, and heights above the catch bench, with model parameters calibrated to the NIOSH rockfall tests. This analysis serves as a critical first step in creating generalizable, predictive rockfall hazard maps incorporating launch feature geometries mapped from as-built high-resolution pit slope surveys.

  PublisherDOI

• Forecasting Rockfall Retention Performance of catch Bench configurations in Mining Models Calibrated to Rockfall Field Data

  2025-06-08

  articleSenior author

  ABSTRACT: Rockfall hazards pose significant safety risks in open-pit mining, endangering both personnel and equipment. To mitigate these risks, catch benches are designed to retain falling rocks, with a common industry target of 90% retention on the first bench. However, quantifying retention rates remains challenging due to variable slope geometries and rockfall behaviors, necessitating calibrated predictive modeling. This study uses RocFall2 to simulate rockfall behavior based on field data from the Highwall Safety Project. A calibration was performed using single-bench tests, determining a 90% retention distance of 10.9 m (~35.76 ft) from the bench toe for 6-inch synthetic rocks. The calibrated model was then applied to a five-bench configuration, which showed a first bench retention efficiency of 88.7%, with cumulative retention reaching 100% by the fifth bench. To explore the impact of bench geometry, a second simulation was conducted using the Modified Ritchie Criterion (MRC), reducing the catch bench width to 9.4 m. In this scenario, a six-bench system was needed to achieve full containment. The results reveal that achieving similar retention outcomes with narrower benches requires more sequential benches. These findings demonstrate the effectiveness of multi-bench systems while highlighting areas for improvement in bench geometry and energy dissipation strategies.

  PublisherDOI

• Leeb Hardness Testing for Rock Strength Estimation: A Case Study from the Bingham Canyon Mine

  2025-06-08

  article

  ABSTRACT: Understanding rock strength is essential for assessing mine stability. Uniaxial Compressive Strength (UCS) is a critical measure of rock strength, though the frequency of laboratory testing is typically limited due to cost and time constraints. Rock hardness (R-value) is often used as a first order estimate of UCS in mining rock mechanics. R-values are measured using qualitative assessments with the geologic hammer and are easy to collect but inherently subjective. Additionally, Point Load Testing (PLT) provides a strength index that can be correlated with UCS values, but the test is destructive, and can be time-consuming. Originally developed for the metals industry, the Leeb Hardness (LH) tester offers a rapid, quantitative, safer, hand-held and non-destructive alternative for measuring rock hardness. While LH data will not replace the need for UCS testing, the high-density nature of LH data also makes it ideal for predictive modeling and as a reliable input for 3D geotechnical models. This study utilizes drill core data from the Bingham Canyon Mine and focuses on establishing a correlation between LH values, traditional hardness estimates (R-values), and PLT data using linear regression and correlation coefficient analyses for each rock type. Analyzing the Quartzite (QZ) and Monzonite (MZ) data, LH showed moderate correlations with PLT. Trends in data infer PLT values underestimate strength compared to LH, however limitations for the LH tester are also explored and discussed in this study. Future work will focus on developing the LH-UCS correlation for the specific lithologies present at Bingham Canyon, enabling more accurate geotechnical characterizations and informing stability assessments for ongoing and future mining operations.

  PublisherDOI

• Enhancing Iron Ore Grindability through Hybrid Thermal-Mechanical Pretreatment

  Minerals · 2024-10-14 · 3 citations

  articleOpen accessSenior author

  Grinding is an important process of ore beneficiation that consumes a significant amount of energy. Pretreating ore before grinding has been proposed to improve ore grindability, reduce comminution energy, and enhance downstream operations. This paper investigates hybrid thermal mechanical pretreatment to improve iron ore grinding behavior. Thermal pretreatment was performed using conventional and microwave approaches, while mechanical pretreatment was conducted with a pressure device using a piston die. Results indicate that conventional (heating rate: 10 °C; maximum temperature: 400 °C), microwave (2.45 GHz, 1.7 kW, 60 s), and mechanical (14.86 MPa, zero delay time) pretreatments improved the studied iron ore grindability by 4.6, 19.8, and 15.4%, respectively. Meanwhile, conventional-mechanical and microwave-mechanical pretreatments enhanced the studied iron ore grindability by 19.2% and 22.6%, respectively. These results suggest that stand-alone mechanical pretreatment or microwave pretreatment may be more beneficial in improving the grinding behavior of the studied fine-grain iron ore sample. The results of the mechanical pretreatment obtained in this study may be used in a simulation of the HPGR system for grinding operations of similar iron ore

  PublisherDOI

• Advancing sustainable and circular mining through solid-liquid recovery of mine tailings

  Process Safety and Environmental Protection · 2024-06-20 · 36 citations

  articleSenior author
  PublisherDOI

• Forecasting of methane gas in underground coal mines: univariate versus multivariate time series modeling

  Stochastic Environmental Research and Risk Assessment · 2023 · 21 citations

  • Environmental science
  • Statistics
  • Econometrics
  PublisherDOI

• Real Time Mine Fire Classification to Support Firefighter Decision Making

  Fire Technology · 2022 · 17 citations

  • Computer Science
  • Artificial Intelligence
  • Engineering
  PublisherDOI

• Time Series Modeling of Methane Gas in Underground Mines

  Mining Metallurgy & Exploration · 2022-08-02 · 14 citations

  articleSenior author
  PublisherDOI

Frequent coauthors

• Sefiu O. Adewuyi

  University of Arizona

  15 shared

• Angelina Anani

  Rogers (United States)

  13 shared

• Edmund C. Jong

  13 shared

• Erik Westman

  Virginia Tech

  10 shared

• Steven Schafrik

  University of Kentucky

  8 shared

• Guang Xu

  Missouri University of Science and Technology

  7 shared

• Z. Agioutantis

  University of Kentucky

  6 shared

• Nino Ripepi

  6 shared

Labs

• School of Mining Engineering and Mineral ResourcesPI

Education

• Ph.D. in Mining Engineering, Mining and Minerals Engineering

  Virginia Tech

  2008

• M.S. in Mining Engineering, Mining and Minerals Engineering

  Virginia Tech

  2005

• B.S. in Mining Engineering, Mining and Minerals Engineering

  Virginia Tech

  2002

Awards & honors

• Metal Mining Automation and Advanced Technologies Workgroup