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.

这项研究旨在表征和建模通常在暴露于莫洛托夫鸡尾酒的工作服中使用的隔热面料。测量了织物的物理性能。并且，在实验室模拟的莫洛托夫鸡尾酒产生的火源下评估了织物的热防护性能。根据通过织物传递的热能的数量来计算性能；此外，根据计算出的传递热能预测了在穿戴者身上产生二次灼伤所需的时间。为了进行表征，根据传热和传质理论确定并讨论了影响防护性能的参数。对织物体系性能与防护性能之间的关系进行了统计分析。影响织物性能的重要织物特性在经验建模技术中进一步采用-多元线性回归（MLR）和人工神经网络（ANN）来预测防护性能。测定系数（R²）和开发的MLR和ANN模型的均方根误差（RMSE）也进行了比较，以确定预测防护性能的最佳拟合模型。这项研究发现，热阻和蒸发阻是两个重要特性（P值<0.05），会对通过织物系统传递的热能产生负面影响。此外， 与MLR模型（R ²）相比，ANN模型的R ²和RMSE值分别高得多（R ² = 0.94）和低（RMSE = 37.42）。 = 0.73; RMSE = 191.38）; 因此，人工神经网络是预测防护性能的最佳模型。总而言之，本研究可以深入了解可能影响高风险行业员工工作服所用织物的防护性能的参数，并为他们提供更好的职业健康和安全性。