Kray Luxbacher

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

Selected publications

• Adsorption Models for Methane in Shales: Review, Comparison, and Application

  Energy & Fuels · 2017-08-31 · 154 citations

  article

  Finding an optimized adsorption model to estimate the true adsorbed quantity of methane in shale at reservoir conditions is fundamental for estimating the gas-in-place (GIP), and developing an accurate shale gas transport model. However, describing true methane adsorption behavior in shale is challenging because the density or volume of the adsorbed phase cannot be measured directly using current technology. There are several models available to describe the observed adsorption isotherms and ext…

  PublisherDOI

• Compartment fire predictions using transpose convolutional neural networks

  Fire Safety Journal · 2019-07-25 · 86 citations

  articleSenior author
  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

  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

Similar researchers at University of Arizona

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• Bo Hyun Kimh-16
• Isabel Bartonh-16