Kejiang Lei

Safe and resilient operation of liquefied petroleum gas (LPG) storage and distribution stations is important to the sustainability of fuel infrastructure, yet boiling liquid expanding vapor explosion (BLEVE) prevention requires time-dependent barrier analysis and a clear separation between prevention priorities and post-rupture exposure. This study develops a dynamic–spatial screening framework that integrates evidence-graded basic events, a priority-AND dynamic fault tree, a continuous-time Markov chain (CTMC), memory-efficient Monte Carlo trajectory simulation, modeled-area prevention prioritization, and tank-origin relative-exposure screening. A one-million-trajectory accelerated numerical baseline produced 112,693 BLEVE-conditioned trajectories. Weld cracking and fire-pump failure had the highest dynamic criticality because they act in later barrier-degradation stages, whereas high-wind spread had the greatest conditional involvement. Tank structure had the highest modeled-area prevention priority. Receiver rankings depended on the assumed layout: P5 ranked first under the baseline schematic coordinates, whereas P2 ranked first under the illustrative metric-coordinate scenario across the tested distance-attenuation kernels. Convergence, acceleration-factor, evidence-grade, weight, recovery, alternative-path, static-importance, coordinate, and kernel sensitivities were evaluated. Prevention-priority scores and normalized exposure indices are reported separately and are not interpreted as calibrated accident frequency, physical dose, expected loss, or quantitative risk. By supporting targeted inspection, maintenance, monitoring, and emergency-resource allocation, the framework can contribute to safer, more resilient, and more sustainable operation of LPG infrastructure.

Railway hazardous chemical transportation, a high-risk activity that endangers personnel, infrastructure, and ecosystems, directly undermines the sustainability of the transportation system and regional development. Traditional risk management algorithms, which rely on empirical rules, result in sluggish emergency responses (with an average response time of 4.8 h), further exacerbating the environmental and economic losses caused by accidents. The standalone DBSCAN algorithm only supports static spatial clustering (with unoptimized hyperparameters); it lacks probabilistic reasoning capabilities for dynamic scenarios and thus fails to support sustainable resource allocation. To address this gap, this study develops a DBSCAN–Bayesian network fusion model that identifies risk hotspots via static spatial clustering—with ε optimized by the K-distance method and MinPts determined through cross-validation—for targeted prevention; meanwhile, the Bayesian network quantifies the dynamic relationships among “hazardous chemical properties-accident scenarios-material requirements” and integrates real-time transportation and environmental data to form a “risk positioning-demand prediction-intelligent allocation” closed loop. Experimental results show that the fusion algorithm outperforms comparative methods in sustainability-linked dimensions: ① Emergency response time is shortened to 2.3 h (a 52.1% improvement), with a 92% compliance rate in high-risk areas (e.g., water sources), thereby reducing ecological damage. ② The material satisfaction rate reaches 92.3% (a 17.6% improvement), and the neutralizer matching accuracy for corrosive leaks is increased by 26 percentage points, which cuts down resource waste and lowers carbon footprints. ③ The coverage rate of high-risk areas reaches 95.6% (a 16.4% improvement over the standalone DBSCAN algorithm), with a 27.5% reduction in dispatch costs and a drop in resource waste from 38% to 11%. This model achieves a leap from static to dynamic decision-making, providing a data-driven paradigm for the sustainable emergency management of railway hazardous chemicals. Its “spatial clustering + probabilistic reasoning” path holds universal value for risk control in complex systems, further boosting the sustainability of infrastructure.

In order to develop precise hole placement technology for gas extraction, this paper combines industrial testing, the evolution law of coal rock damage permeability, and the coal double-pore double-permeability model; establishes a coupling model of the coal damage deformation field and matrix-fracture double diffusion seepage field; and determines precise hole placement spacing for a coal seam gas by using the COMSOL Multiphysics 5.6 numerical simulation software. This study shows that the effective radius of gas extraction is 4.8 m after 180 d of extraction, which is a power function of the extraction time. The permeability of the coal body is affected by matrix adsorption, and contraction and effective stress, and the influence range between the boreholes under multi-hole extraction increases with the increase in spacing; at the same time, we took into account the positive effect of the permeability change on the extraction effect and ultimately determined the optimal spacing of boreholes in Dashucun Mine to be 6 m, which was arranged in the square area. The extraction effect was good after the on-site test, and the results of this study can provide guidance for the optimal arrangement of the spacing of boreholes in the underground areas of coal mines.

Nowadays, underground coal mine accidents occur frequently, causing huge casualties and economic losses, most of which are gas explosion accidents caused by fires. In order to improve the emergency rescue capability of coal mine fires and reduce the losses caused by coal mine fires, this article is dedicated to the assessment of coal mine fire rescue capability. Taking the fire emergency rescue system of Lugou mine as an example, based on the introduction of gray system theory and gray evaluation method, an evaluation model was established to assess the risk of the fire emergency rescue index system of Lugou mine. Four primary and 19 secondary indicators were delineated, and a hierarchical structure model of the fire emergency rescue capability of the Lugou mine was established by combining expert opinions, and the weights of indicators at all levels were calculated by using hierarchical analysis. We then used the gray system evaluation method and expert scoring to judge the safety level of various indicator factors in the index system. The evaluation results show that the risk level of the emergency rescue system of the Lugou mine fire is higher than the fourth level. The main risk indicator factors are firefighting equipment, decision-making command, emergency education and training, and fire accident alarm. In response to this evaluation result, corresponding control measures were formulated in four aspects: rescue organization guarantee, personnel guarantee, material guarantee, and information guarantee, which optimally improved the emergency rescue capability of the Lugou mine fire and reduced the loss caused by fire.