Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers' Clothing.

Sumit Mandal,Rui Li,Nur-Us-Shafa Mazumder,Robert J Agnew,Guowen Song,Indu Bala Grover

doi:10.3390/ijerph18136991

Abstract

Most of the fatalities and injuries of oilfield workers result from inadequate protection and comfort by their clothing under various work hazards and ambient environments. Both the thermal protective performance and thermo-physiological comfort performance of textile fabrics used in clothing significantly contribute to the mitigation of workers’ skin burns and heat-stress-related deaths. This study aimed to apply the ANN modeling approach to analyze clothing performance considering the wearers’ sweat moisture and the microclimate air gap that is generated in between their body and clothing. Firstly, thermal protective and thermo-physiological comfort performance of fire protective textiles used in oilfield workers’ clothing were characterized. Different fabric properties (e.g., thickness, weight, fabric count), thermal protective performance, and thermo-physiological comfort performance were measured. The key fabric property that affects thermal protective and thermo-physiological performance was identified as thickness by statistical analysis. The ANN modeling approach could be successfully implemented to analyze the performance of fabrics in order to predict the performance more conveniently based on the fabric properties. It is expected that the developed models could inform on-duty oilfield workers about protective and thermo-physiological comfort performance and provide them with occupational health and safety.

Highlights

According to the U.S Department of Labor statistics, 1566 workers died from injuries in the oil-and-gas drilling industry and related fields from 2008 through 2017 [1]
The statistical significance tests and 95% confidence interval (CI) tests were carried out to identify the difference in Heat transfer performance (HTP)/total heat loss (THL) under different levels of moisture and air gaps; in this study, if the p-value of mean difference in HTP/THL at two different conditions of moisture or air gap was less than 0.05, the difference was interpreted as a significant difference
This study investigated the thermal protective and thermo-physiological comfort performance of fabrics used in oilfield workers’ protective clothing using an artificial neural network (ANN) modeling approach

Summary

Introduction

According to the U.S Department of Labor statistics, 1566 workers died from injuries in the oil-and-gas drilling industry and related fields from 2008 through 2017 [1]. A recent explosion in one of Oklahoma’s oil-and-gas rigs resulted in the deaths of five workers [2]. Oklahoma is among the 10 most wildfire-prone states in the USA (Insurance Information Institute, New York, NY, USA, 2018 statistics). The majority of these fatalities and burn injuries resulted from inadequate protection and comfort provided by oilfield workers’ thermal protective clothing [3,4]. The thermal environment where the on-duty oilfield workers work most often decides the thermal protective performance of their fire protective textiles [5]. The thermal environments faced by these workers have been investigated by many researchers to understand

Objectives

Methods

Results

Conclusion