With the extensive use of liquefied petroleum gas (LPG) in the catering industry, leakage explosions have become frequent. This study employs numerical simulations to investigate the diffusion patterns of LPG leakage under various ventilation conditions. The results show that there is a logarithmic relationship between the wind speed and the volume of a propane gas cloud under natural ventilation. In the wind speed ranges of 1.5 to 3.3 m/s and 7.9 to 10.7 m/s, a small increase in wind speed leads to a significant reduction in gas cloud volume (97.2% and 95.05%, respectively). Under forced ventilation, the volume of the gas cloud decreases by 90.6%, from 6.67 m³ at higher air exchange rates (22.1 and 24.3 times/h), reducing explosion risks. When leakage occurs at the stove, the optimal combination for dispersing the propane combustible gas cloud is window opening at position 1 and fan at position a. The volume of the gas cloud at window position 1 increases exponentially with the distance between the fan and the leak source. The fan is installed within 2.786 m from the leak source to ensure that the gas cloud volume remains below 0.5 m³. These findings provide valuable insights for the design and the optimization of ventilation systems and layouts in commercial kitchens.

Aiming at the problems of complex calculation processes, insufficient risk data, and reliance on experts’ subjective judgments that exist in traditional probability analysis methods, this paper proposes a probability analysis method for hazardous chemical storage tank leakage accidents based on neural networks and fuzzy dynamic fault trees (Fuzzy DFT). This method combines fuzzy set theory (FST) and Bootstrap technology to accurately quantify the failure probabilities of basic events (BEs) and reduce the dependence on experts’ subjective judgments. Furthermore, an artificial neural network (ANN) model for tank failures is constructed. This model can accurately calculate the probability of tank leakage accidents by taking into account the dependency relationships among basic events. Finally, a long short-term memory (LSTM) network is utilized to analyze the dynamic evolution trend of the probability of storage tank accidents over time. In this paper, this method is applied to the case of the “11.28” Shenghua vinyl chloride leakage accident. The results show that the calculation results of this method are highly consistent with the actual situation of the accident, indicating that it is a scientific and effective method for analyzing the probability of hazardous chemical storage tank leakage accidents.

In order to conveniently and efficiently determine the Permissible Exposure Limits (PELs) of organic chemicals in the workplace, this study employed Quantitative Structure–Activity Relationship (QSAR) modeling to predict properties related to occupational health and safety. The predictive study was conducted by correlating the PELs of 75 hydrocarbons and their oxygen-containing derivatives with the molecular structures of the organic compounds. Meanwhile, this study conducted a comprehensive and in-depth comparative analysis of the four developed predictive models. The sample set was partitioned using the Affinity Propagation (AP) clustering algorithm. Four characteristic molecular descriptors were selected by integrating the Genetic Algorithm (GA) with the variance inflation factor (VIF) value. Subsequently, the Multiple Linear Regression (MLR) model and two nonlinear models, namely the Support Vector Machine (SVM) and the Extreme Gradient Boosting (XGBoost), were developed and used for predictive comparison. Furthermore, the performance of the models was evaluated through both internal and external validation methods, and the Williams plots were constructed to define the model’s applicability domain. The results indicated that the XGBoost model achieved high performance, with a coefficient of determination (R²) of 0.9962 on the training set and 0.8892 on the testing set. The corresponding root mean square errors (RMSE) were 0.1012 and 0.6623 for the training and testing sets, respectively. The internal validation coefficient (Q²loo) was 0.8975, while the external validation coefficient (Q²ext) was 0.832. Moreover, the majority of the sample data (approximately 96%) fell within the application domain defined by ±3 times the standard residue-to-critical arm ratio, where h* = 0.2. This demonstrates that the XGBoost model exhibits excellent fitting capability, stability, and predictive power, thereby uncovering a significant nonlinear relationship between the molecular structure of compounds and the PELs. As outlined above, the utilization of the QSAR method for predicting the PELs of hydrocarbons and their oxygen-containing derivatives constitutes a highly effective approach.

This study employs both experimental and numerical simulation methods to systematically investigate the influence of sudden expansion diameter ratios on methane–air premixed flame propagation, explosion overpressure, and the evolution of turbulent structures. The results show that with the increase in the diameter ratio, the flame propagation velocity and explosion overpressure present a nonlinear trend of first increasing, then decreasing, and then increasing. Specifically, when the diameter ratio is 1.5, an optimal balance between turbulence enhancement and energy dissipation is achieved, and the overpressure attenuation rate is 47.61%. However, when the diameter ratio increases to 2.0, the turbulence intensity significantly escalates, the peak flame propagation speed increases by 81%, the peak explosion overpressure increases by 69%, and the overpressure attenuation efficiency decreases, which brings greater safety challenges. Moreover, when the diameter ratio is between 1.5 and 2.0, the turbulence intensity of the premixed gas explosion flow field is significantly increased, and the stable “tulip flame” propagation velocity range is extended from 16~35 m/s to 16~42 m/s. When the diameter ratio is 2.0, a distinctive four-vortex structure is formed, with strong turbulent mixing and fast energy dissipation. The vortex structure evolves with the diameter ratio, transitioning from a symmetric and stable double-vortex form to a complex multi-vortex system. The research results provide theoretical support for the prevention of explosions.

The scarcity of reliably determined STELs for numerous chemicals severely impedes occupational health risk assessment. To address this gap, this study establishes and validates a suite of robust quantitative structure–activity relationship (QSAR) models to efficiently predict STELs for hydrocarbons and their derivatives. A dataset of 60 compounds was partitioned using Affinity Propagation clustering, and the validity of this division was verified using Tanimoto similarity analysis and Uniform Manifold Approximation and Projection (UMAP). Four optimal molecular descriptors, indicative of molecular size and spatial configuration, were identified using a genetic algorithm. These descriptors served as inputs for one linear model—multiple linear regression (MLR)—and three nonlinear models: support vector machine (SVM), back-propagation artificial neural network (BP-ANN), and extreme gradient boosting (XGBoost). All models were rigorously validated according to OECD principles. The results demonstrated that the XGBoost model achieved superior performance, with key metrics ($R^2$, $Q_{\mathrm{loo}}^2$, $Q_{\mathrm{ext}}^2$) all exceeding 0.9. Interpretability analysis using SHAP (SHapley Additive exPlanations) revealed that molecular size and symmetry descriptors (E3u, G2m) positively correlate with STEL, while the degree of unsaturation (n = CHR) shows a significant negative influence, providing novel mechanistic insights into the structure–toxicity relationship. Notably, 96% of the predictions fell within the defined applicability domain, confirming the model’s reliability. This study therefore serves as a rapid, accurate, interpretable, and reliable computational tool, with the potential to significantly inform and enhance occupational health and safety decision-making, especially for novel or data-poor chemicals.