Xiongjun Yuan

To reveal the mechanisms by which obstacle distribution affects propane–air premixed deflagration under different blockage ratios, large eddy simulation (LES) was employed to investigate flame propagation and pressure rise in a confined duct with four obstacle distributions and four blockage ratios. The coupled effects of obstacle layout and blockage ratio on flame morphology, propagation velocity, vorticity evolution, and pressure rise rate were analyzed. The results show that obstacle distribution significantly changes flame front structures: One-side obstacles produce claw-like flames, center layout obstacles generate tongue-like flames with large vortex regions at low-to-moderate blockage ratios, and both-side or around layout obstacles form mushroom-like flames. At high blockage ratios, around layout obstacles redirect the flow into a high-speed axial jet, leading to the highest flame velocity and maximum pressure rise rate. These findings indicate that the dominant flame acceleration mechanism shifts from vortex-induced flame wrinkling at low-to-moderate blockage ratios to axial-jet-driven flame acceleration at high blockage ratios, providing guidance for obstacle layout optimization and explosion risk mitigation in confined propane–air systems.

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 research employed the quantitative structure-property relationship (QSPR) approach to predict the superheat limit temperature (SLT) of 64 hydrocarbons and their derivatives. Three models were constructed by using machine learning methods, namely a multiple linear regression (MLR) model, an extreme learning machine (ELM) model, and a support vector machine model based on particle swarm optimization (PSO-SVM). The multiple correlation coefficient (R²), the root mean square error (RMSE), and the mean absolute error (MAE) were adopted to evaluate the model fitting ability. The leave-one-out cross-validation coefficient (Q²_lOO) was employed to assess the model stability, the external validation coefficient (Q²_ext) was used to evaluate the model’s external prediction ability, and the Williams plot was drawn to assess the model’s generalization ability. The results demonstrated that the R², Q²_lOO, and Q²_ext of the training and test sets of the three models were all above 0.9, and the arm ratio of the majority of compounds (96.88%) in the Williams plot was within the warning value, indicating that all the three models were suitable for predicting the SLT of hydrocarbons and their derivatives. By comparing the performance parameters of the three models, the PSO-SVM model achieved the best performance across all parameters, suggesting that there exists a strong nonlinear relationship between the molecular structure of hydrocarbons and their derivatives and the SLT. The utilization of the QSPR method to predict the SLT of hydrocarbons and their derivatives can provide powerful theoretical support for the safe design and control of industrial operations.

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.

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.

Identifying risk factors and improving safety “immunity” is beneficial to chemical enterprises to reduce the occurrence of production accidents. In this paper, an immune system model has been built according to the bionics principle, and the corresponding antigen–antibody index system based on hidden dangers and treatment measures has been established, including 8 factor layers and 33 index layers. Then, the immune evaluation model of production safety was built using index weight, and an evaluation model of immune response ability for production safety was constructed based on grey system theory. Finally, aiming to identify immune deficiency through numerical analysis, a safety-management platform focused on people management, visual monitoring, and danger warning was constructed. The results verify the suitability of the immune correlation principle applied in chemical enterprises, which makes an instructive contribution to the construction of information management platforms for chemical enterprise safety production.