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

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

doi:10.3390/ijerph18136991

Cited by 11 publications

(10 citation statements)

References 46 publications

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“…Because a model trained with too few neurons in the hidden layer can not differentiate between complex patterns, and it might lead to a linear estimate of the actual relationship between the input and output variables; whereas, if the model is trained with too many neurons, the model follows a noise in the data set, and it might predict an inaccurate output [43]. Therefore, we trained the feed-forward ANN models with two to ten hidden neurons, and the best predictive ANN models was found with three hidden neurons (Figure 3) [20,50]. In the present study, MATLAB software randomly used 60% of the data (significant properties and transmitted energy) for the training, 20% of the data for the validation, and the remaining 20% of the data to test the predicting performance of the ANN models.…”

Section: Methodsmentioning

confidence: 99%

Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

Mandal

Song

Rossi

et al. 2021

Journal of Industrial Textiles

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This study aims to characterize and model the thermal protective fabrics usually used in workwear under Molotov cocktail exposure. Physical properties of the fabrics were measured; and, thermal protective performances of the fabrics were evaluated under a fire exposure generated from the laboratory-simulated Molotov cocktail. The performance was calculated in terms of the amount of thermal energy transmitted through the fabrics; additionally, the time required to generate a second-degree burn on wearers’ bodies was predicted from the calculated transmitted thermal energy. For the characterization, the parameters that affected the protective performance were identified and discussed with regards to the theory of heat and mass transfer. The relationships between the properties of the fabric systems and the protective performances were statistically analyzed. The significant fabric properties affecting the performance were further employed in the empirical modeling techniques − Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for predicting the protective performance. The Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of the developed MLR and ANN models were also compared to identify the best-fit model for predicting the protective performance. This study found that thermal resistance and evaporative resistance are two significant properties (P-Values < 0.05) that negatively affect the transmitted thermal energy through the fabric systems. Also, R2 and RMSE values of ANN model were much higher (R2 = 0.94) and lower (RMSE = 37.42), respectively, than MLR model (R2 = 0.73; RMSE = 191.38); therefore, ANN is the best-fit model to predict the protective performance. In summary, this study could build an in-depth understanding of the parameters that can affect the protective performance of fabrics used in the workwear of high-risk sectors employees and would provide them better occupational health and safety.

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Section: Methodsmentioning

confidence: 99%

Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

Mandal

Song

Rossi

et al. 2021

Journal of Industrial Textiles

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“…It should be noted that in the close vicinity to a regular flare or an unintentionally ignited cold flare, the radiative heat flux levels are much higher than the values analyzed in the present study. However, inside the industrial site perimeter, the zone closest to such equipment is generally considered a sterile zone with very infrequent work, and the workers are generally equipped with clothing that protects against much higher heat fluxes [36,37]. Thus, exposure to very high heat fluxes that may generate deeper burns, e.g., deep partial-thickness burns [38], is outside the current study, which focused on low heat radiation levels.…”

Section: Skin Damage Modelingmentioning

confidence: 99%

Analysis of Expected Skin Burns from Accepted Process Flare Heat Radiation Levels to Public Passersby

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2021

Energies

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Hot flaring, even from quite high flare stacks, may result in significant heat radiation outside a facility to, e.g., public roads where random passersby may be exposed. The present study suggests a novel method for analyzing a typical flare heat radiation exposure and investigates skin burns that may be inflicted on an exposed person if a facility needs to depressurize in an emergency situation. A typical radiation field from an ignited natural gas vent was taken as the boundary condition, and these values were compared to radiation levels mentioned by the American Petroleum Institute (API 521), e.g., 1.58 kW/m2 and above. Due to facility perimeter fences along roads in larger industry areas, it was assumed that an exposed person may flee along a road rather than in the ideal direction away from the flare. It was assumed that naked skin, e.g., a bare shoulder or a bald head is exposed. The Pennes bioheat equation was numerically solved for the skin layers while the person escapes along the road. Sun radiation and convective heat exchange to the ambient air were included, and the subsequent skin injury was calculated based on the temperature development in the basal layer. Parameters affecting burn severity, such as heat radiation, solar radiation, and convective heat transfer coefficient, were analyzed. For small flares and ignited small cold vents, no skin burn would be expected for 1.58 kW/m2 or 3.16 kW/m2 maximum heat radiation at the skin surface. However, higher flare rates corresponding to, e.g., 4.0 kW/m2 maximum flare heat radiation to the skin, resulted both in higher basal layer temperatures and longer exposure time, thus increasing the damage integral significantly. It is demonstrated that the novel approach works well. In future studies, it may, e.g., be extended to cover escape through partly shielded escape routes.

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“…From the R square values, it can be seen that thickness has the highest value compared to linear density and fabric count. Therefore, it can be said that thickness is the most important property while considering the tensile strength of the fabric [59]. Fabric count seems the second most important property.…”

Section: Effect Of Radiant Heat On Tensile Strength Of the Fabrics In...mentioning

confidence: 99%

“…Therefore, the loss of tensile strength was considerably lower than dry and single layer moist conditions. The presence of moisture in the thermal liner could increase the heat capacity of the fabrics, which resulted in a significant amount of thermal energy storage within the fabric system [6,59,63,64].…”

Section: Effect Of Moisture and Radiant Heat On Tensile Strength Of O...mentioning

confidence: 99%

Characterizing the Tensile Strength of the Fabrics Used in Firefighters’ Bunker Gear under Radiant Heat Exposure

et al. 2022

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More than 60,000 firefighters’ injuries were reported by the National Fire Protection Association in the U.S. in 2019. Inadequate protection by bunker gear could be a reason for most of the injuries. Firefighters repeatedly encounter thermal hazards due to their job responsibilities. Degradation could occur on bunker gear fabric during thermal exposure. It has been found that the presence of moisture affects performance as well, which may come from wearers’ sweat. Proper evaluation of the tensile strength of the fabrics used in bunker gear could provide information essential for maintenance the overall integrity of the gear. An evaluation of the tensile strength of fabrics when exposed to 10, 15, and 20 kW/m2 radiant heat flux in the presence of moisture is reported. In each fabric system, a total of sixty-four different samples were prepared for four different types of fabric and four levels of moisture which were exposed to three different radiant heat flux for five minutes. Heat flux and moisture levels have significant impact on tensile strength. The effect of moisture on tensile strength in a three-layered fabric system is higher than that for a single layer fabric. An understanding of the impact of heat and moisture on fabric strength has been achieved.

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

Cited by 11 publications

References 46 publications

Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

Analysis of Expected Skin Burns from Accepted Process Flare Heat Radiation Levels to Public Passersby

Characterizing the Tensile Strength of the Fabrics Used in Firefighters’ Bunker Gear under Radiant Heat Exposure

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