Hazardous extent predictions that ensure process safety while avoiding overestimation have been a challenge for hazardous area classification. Specific leak scenarios can be addressed to build rapid empirical models to accurately determine hazardous extent considering several factors that are not included in general approaches. In view of that, this work aims to propose a novel CFD-based empirical model for gas emissions in open and unobstructed areas. It considers a wide range of variables such as storage temperature and pressure, orifice diameter, molecular weight, gas concentration, and wind velocity. A sensitivity analysis was performed to evaluate each variable's contribution to the gas cloud extent. The linear regression model resulting from the combination of all variables contribution was coupled with Ewan and Moddie's model to minimize the prediction errors due to the non-monotonic wind effects. The suggested algorithm accurately calculates the hazardous extent with a coefficient of determination equals to 0.9842 and a RMSE of 0.0151 for a dataset of 600 cases of generic gases release. The proposed model was also validated for 60 cases of hydrogen releases under different storage conditions, giving a coefficient of determination equal to 0.9829 and a RMSE of 0.0680, indicating a good agreement with the data.

在确保过程安全的同时避免过高估计的危险程度预测一直是危险区域分类的挑战。可以解决特定的泄漏情况，以建立快速的经验模型，以考虑一般方法中未包括的几个因素来准确确定危险程度。有鉴于此，这项工作旨在为空旷和无障碍地区的气体排放提出一种新颖的基于CFD的经验模型。它考虑了广泛的变量，例如存储温度和压力，孔口直径，分子量，气体浓度和风速。进行了敏感性分析，以评估每个变量对气云范围的贡献。由所有变量贡献的组合得出的线性回归模型与Ewan和Moddie' s模型可最大程度地减少由于非单调风效应引起的预测误差。对于包含600种普通气体释放量的数据集，建议的算法以确定系数等于0.9842，RMSE为0.0151的方式准确计算危险程度。所提出的模型还针对60种不同储存条件下的氢气释放案例进行了验证，确定系数等于0.9829，RMSE为0.0680，表明与数据有很好的一致性。