Validation of the Predicted Heat Strain Model in Hot Underground Mines
AffiliationUniv Arizona, Dept Min & Geol Engn
MetadataShow full item record
CitationLazaro, P., & Momayez, M. (2019). Validation of the Predicted Heat Strain Model in Hot Underground Mines. Mining, Metallurgy & Exploration, 36(6), 1213-1219. doi: 10.1007/s42461-019-0102-6
JournalMINING METALLURGY & EXPLORATION
RightsCopyright © Society for Mining, Metallurgy & Exploration Inc. 2019.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractHeat-related illnesses (HRI) are relatively common in both hot surface and underground mining operations. When workers are exposed to extreme heat or strenuous work in a hot environment, they become prone to heat stress. Heat strain is the result of the body's response to external and internal heat stress. It is therefore vital for the conditions leading to heat strain be detected and treated in a timely manner. Heat-related illnesses are manifested by exhaustion and heat stroke. The predicted heat strain (PHS) [ISO 7933 (2004)] model has been developed to predict the health condition of the worker in terms of core body temperature and water loss. The PHS model tested in this study is based on eight physical parameters that are measured at different intervals during a work shift. They include air temperature, humidity, radiation, air velocity, metabolic rate, clothing insulation, posture, and acclimatization. The model predictions are then compared with a direct physiological measurement, such as core body temperature. We present the results of an extensive study that monitored and predicted body's response to heat stress under different environmental and working conditions. The PHS model provided reliable results in most instances in comparison with other prediction methods currently in use in the field.
Note12 month embargo; published online: 27 June 2019
VersionFinal accepted manuscript