Science Foundation AZ

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• Mineral Processing
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• San Xavier Mining Laboratory

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Research Project

Mining Technology

Sean Dessureault, Principal Investigator

The proposed research program focus on both short and long term challenges.  A key short-term challenge is the continued development of coal mining technology and the high-skilled workforce needed to render technology’s benefits.  Economic modeling in a carbon constrained economy and a better understanding of the Chinese market are untapped long-term research issues highly relevant to the sponsor, Peabody energy.

• Dozer push optimization: Cutting at a downward angle (consumes more energy on the return segment of the cycle) versus cutting horizontal (consumes more energy on the carry/push segment of the cycle).  This project can use mathematical models verified through energy (gallons of fuel) consumption measurement and 3-D volumetric measurement (using the mine’s on-site equipment). 
  
  
• Production simulation: Investments in IT to track productivity enables the use of reliable production simulation that can pull statistical distributions of production rates and delays directly from original sources.  This would create a new generation of production simulation that is more reliable and sustainable. 
  
  
• Low-bandwidth mine-wide productivity tracking: (i.e. Dispatch light for underground): New ultra-low frequency communications systems will become ubiquitous in underground coal operations for use in emergency situations.  These systems have low bandwidth due to the ultra-low frequency carrier wave.  This project would design the system’s data flows to reduce packet size while maximizing production and delay tracking.  This would allow the underground low-bandwidth wireless telemetry network to be used for both safety and productivity tracking. 
  
  
• Investigation of new data mining models: Most commercial data mining algorithms were developed for marketing.  A research project to adapt existing data mining models for engineering optimization is proposed, namely, algorithms for cast blast optimization, optimum operator-equipment matches, and in-pit process productivity factor ranking.
  
  
• Condition-based maintenance using real-time data: Peabody Energy has been investing in technology that collects data on the health and alarm codes from mobile equipment.  Such data can be integrated with actual maintenance and production records.  Data mining models can then be used to identify patterns that were experience prior to maintenance events of a particular cost threshold.