Thermofluid Analysis and Biostatistical Modelling of Thermal Exposure Among Workers in Gas-Fired Power Plants
DOI:
https://doi.org/10.33003/fjs-2026-1015-5946Keywords:
Gas-Fired Power Plant, Thermo-fluid Analysis, Occupational Heat Stress, Wet-Bulb Globe Temperature, Logistic Regression, Thermal ExposureAbstract
Thermal exposure in gas-fired power plants is shaped by combustion-related heat release, hot equipment surfaces, radiation, ventilation, humidity, workload, clothing, and the duration of task exposure, making a single ambient temperature measurement insufficient for evaluating worker heat strain. This study presents a scenario-based thermofluid and biostatistical modelling framework for quantifying thermal exposure in representative gas-fired combined-cycle power plant work zones. The thermofluid component uses steady energy balances, convective heat transfer, radiative exchange, humidity effects, wet-bulb globe temperature (WBGT), metabolic heat production, and clothing adjustment to characterise worker-facing thermal conditions. A synthetic worker-shift dataset of 300 observations was generated from the modelled exposure ranges to demonstrate logistic regression for heat-strain probability. Three learning-outcome objectives were specified: to quantify zone-level thermal exposure; to estimate the association between thermal-workload variables plus modelled heat strain; to evaluate predictive behaviour across exposure scenarios. Modelled mean WBGT ranged from 28.9 °C in the control room to 34.9 °C around the heat-recovery steam generator (HRSG), while the HRSG zone produced the highest modelled maximum of 37.9 °C. In the synthetic demonstration dataset, each 1 °C increase in WBGT was associated with 38.4% higher odds of modelled heat strain (OR 1.384, 95% CI 1.234–1.552, p < .001). Metabolic rate also showed a positive association (OR 1.006 per W, p = .022). The findings demonstrate how thermofluid outputs can be translated into worker-level risk estimates, while emphasising that the statistical results are illustrative until validated against measured occupational data.
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Copyright (c) 2026 Samuel Ndukwe, Danjuma Adeiza Lawal; Akhaze Musa Nicholas; Obedience Okoji Ndukwe
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