The research addresses the safety imperative of preventing flammable gas explosions in enclosed pipelines by establishing a predictive model for the critical quenching diameter within porous media flame arresters. A novel predictive framework was developed based on a comprehensive nine-dimensional feature space incorporating gas composition parameters (e.g., hydrogen equivalence ratio), pipeline geometry dimensions (length-to-diameter ratio), initial thermodynamic conditions (pressure), and porous medium structural characteristics (thickness, material thermal conductivity). A systematic investigation was conducted to identify the optimal hyperparameter configurations for both convolutional neural network (CNN) and Transformer architectures. Rigorous validation demonstrated that the Transformer model exhibits statistically significant superiority over the CNN model across all key performance metrics. Specifically, the model achieved a δMAE (mean absolute error, MAE) of 0.068, a δMSE (mean squared error, MSE) of 0.008, and an coefficient of determination R2 of 0.928. The performance notably surpassed the CNN results (δMAE=0.079, δMSE=0.012, R2=0.906). Beyond evaluation indicators, detailed error distribution analysis confirmed the Transformer's enhanced predictive accuracy and reduced susceptibility to outliers. The superior performance is attributed to the Transformer’s intrinsic self-attention mechanism, which excels at dynamically identifying and weighting critical interdependencies among the diverse input features governing the complex quenching process. The capability enables more precise capture of the nonlinear phenomena defining the quenching limit. Furthermore, robustness testing involving diverse data normalization schemes revealed that the Transformer model exhibits greater stability. This resilience stems from its inherent layer normalization mechanism, which effectively decouples feature dependencies and mitigates sensitivity to input scaling variations. Consequently, the Transformer architecture is established as the definitive optimal model for this critical safety prediction task. Its significant advantage lies in the minimal data preprocessing required before deployment, a feature that greatly enhances its practical utility. The model provides robust quantitative decision-making support essential for formulating effective gas explosion mitigation strategies and optimizing the safety design parameters of pipeline flame arresters. By accurately predicting the critical quenching diameter across various scenarios, this work delivers a valuable tool for enhancing inherent safety in industries handling combustible gases within confined pipeline systems.
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Coal continues to be a vital component of China's energy infrastructure, but the inherent explosion risk associated with coal dust significantly hampers industrial safety and sustainable economic growth. Given these challenges, conducting in-depth research on efficient explosion inhibition technology for coal dust and understanding its underlying mechanism are urgent and necessary. Researching efficient explosion inhibition technology and its mechanisms represents a scientific challenge and an essential step toward achieving sustainable economic development.
In this study, we adopted a thorough and systematic approach to investigate the thermal decomposition characteristics and product distribution of raw coal and phytic acid (PA)-containing coal samples at high temperatures. Our research method ology combined thermogravimetric experiments, mass spectrometry analysis, and other techniques. During the thermogravimetric experiment, we accurately recorded the mass change of the coal samples during the heating process. The data served as the foundation for subsequent mass spectrometry analysis. By monitoring the thermal decomposition process of the coal samples, we revealed in detail the thermal decomposition characteristics of the raw coal and PA-containing coal samples in the high-temperature environment. We paid particular attention to the dynamic changes in the gases and volatiles produced during the heating process of the coal samples. Following the thermogravimetric experiment, we conducted a mass spectrometry analysis using in-situ pyrolysis time-of-flight mass spectrometry (in-situ-Py-TOF-MS). With this technique, we monitored the gases and products generated during the pyrolysis of the coal samples in real time. This provided us with high-resolution data that supported our analysis of the relative abundance and the generation mechanisms of these gases and products.
The research results indicate that the inclusion of PA-containing inhibitors can significantly reduce the content of light combustible gases (mainly CH4 and CO) and organic volatiles (mainly alkenes, aromatic compounds, phenolic compounds, and dihydroxy aromatic hydrocarbons) during coal dust pyrolysis. The main chemical components generated by the pyrolysis of PA water mist include[(HO)2PO]2O, PO(OH)3, and HOPO2. These small phosphorus-containing molecules can effectively terminate the chain reaction of explosions by consuming key flame radicals such as H and OH. Additionally, the addition of PA water mist results in a 15.8 % decrease in the heat release of coal dust samples. Concurrently, the activation energy increases from 11.3 kJ/mol to 16.4 kJ/mol. The inhibition mechanism of PA-containing fine water mist on coal dust explosion is further analyzed by measuring the pyrolysis products of coal samples with and without detonation inhibitors.
PA pyrolysis yields[(HO)2PO]2O, PO(OH)3, HOPO2, and other small molecules of phosphorus-containing components. These phosphorus-containing explosion inhibition components can be achieved through the removal of flame radicals, bond breaking, and free radical extraction reactions involving oxygen or hydrogen atoms. Additionally, these components can initiate hydrogen atoms or hydroxyl addition reactions, all of which collectively work to inhibit the chain reaction of dust explosion. This, in turn, reduces the concentration of key flame radicals.
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