Application of Machine Learning and Computational Fluid Dynamics to Design Underground Auxiliary Ventilation System PDF Download

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Application of Machine Learning and Computational Fluid Dynamics to Design Underground Auxiliary Ventilation System

Application of Machine Learning and Computational Fluid Dynamics to Design Underground Auxiliary Ventilation System PDF Author: Akash Adhikari
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Languages : en
Pages : 0

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Book Description
The mining industry relies heavily on relatively near-surface orebodies that represent low-risk, high-reward mineral resources in terms of discovery and operation. Future mines will likely go deeper (underground) due to the depletion of near-surface mineral reserves and to meet increasing natural resource demands. Deeper underground mines pose several operational challenges. The deeper mines will not only have to deal with these challenges but also strive to achieve zero injuries, increase efficiency, and reduce operating costs. To this end, state-of-the-art technologies and data analytics tools are critical to improving operational efficiencies and enhancing mine safety and health, which are difficult to achieve with conventional approaches. This research explores the potential of machine learning (ML) and computational fluid dynamics (CFD) tools in enhancing mine safety and health, and improving operational efficiency, specifically, solving challenges associated with underground blasting operations and auxiliary ventilation systems.