Utilizing Nonlinear Autoregressive with Exogenous Input Neural Networks to Evaluate the Thermal Flywheel Effect Along Intake Shafts at Nevada Mines-Reference-Cited by-同舟云学术

Utilizing Nonlinear Autoregressive with Exogenous Input Neural Networks to Evaluate the Thermal Flywheel Effect Along Intake Shafts at Nevada Mines

Published:2021-03-03 Issue:3 Volume:38 Page:1395-1410
ISSN:2524-3462
Container-title:Mining, Metallurgy & Exploration
language:en
Short-container-title:Mining, Metallurgy & Exploration

Author:

Scalise Kyle A.^ORCID,Kocsis Karoly (Charles)

Funder

National Institute for Occupational Safety and Health

Publisher

Springer Science and Business Media LLC

Subject

Materials Chemistry,Metals and Alloys,Mechanical Engineering,Geotechnical Engineering and Engineering Geology,General Chemistry,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42461-021-00411-0.pdf

Reference23 articles.

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2. Baggs D, Mortensen N (2006) Thermal mass in building design. Des 4: Thermal Mass in Building Design, 1-9.

3. Beale MH, Hagan MT, Demuth HB (2010) Nerual network toolbox 7. User guide. MATLAB.

4. Bluhm S, von Glehn F, Smit H (2003) Important basics of mine ventilation and cooling planning. Managing the Basics Conference, (p. 7).

5. Brake D (2008) Psychrometry, mine heat loads, mine climate & cooling. Mine Ventilation Australia, Brisbane

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1. Multiple logistic regression based prediction of heat flow direction in an intake incline of shallow depth by integrating thermal flywheel effect: A case study;Applied Thermal Engineering;2022-08

2. Managing Heat in Underground Mines: the Importance of Incorporating the Thermal Flywheel Effect into Climatic Modeling;Mining, Metallurgy & Exploration;2020-10-09