Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

This study aims to characterize and model the thermal protective fabrics usually used in workwear under Molotov cocktail exposure. Physical properties of were measured; and, performances evaluated a fire exposure generated from laboratory-simulated cocktail. The performance was calculated terms amount energy transmitted through fabrics; additionally, time required generate second-degree burn on wearers’ bodies predicted energy. For characterization, parameters that affected identified discussed with regards theory heat mass transfer. relationships between fabric systems statistically analyzed. significant affecting further employed empirical modeling techniques − Multiple Linear Regression (MLR) Artificial Neural Network (ANN) for predicting performance. Coefficient Determination (R 2 ) Root Mean Square Error (RMSE) developed MLR ANN models also compared identify best-fit found resistance evaporative are two (P-Values < 0.05) negatively affect systems. Also, R RMSE values much higher = 0.94) lower (RMSE 37.42), respectively, than 0.73; 191.38); therefore, is predict In summary, this could build an in-depth understanding can high-risk sectors employees would provide them better occupational health safety.