Against the backdrop of the global energy transition, zero-emission hydrogen fuel cell vehicles are critical for achieving deep decarbonization in the transportation sector. However, the potential safety hazards associated with their onboard high-pressure hydrogen storage systems cannot be overlooked. This issue is particularly critical in confined spaces with poor ventilation, such as underground parking garages. Hydrogen leaks pose a significant safety hazard owing to the risk of combustion. Current hydrogen detection technologies for large enclosed spaces suffer from response delays, low positioning accuracy, and susceptibility to environmental noise, which makes it difficult to meet the stringent requirements for rapid and precise leak localization in emergency rescue operations. Therefore, improved hydrogen leak detection and early warning systems must be urgently developed to ensure the safety of hydrogen fuel cell vehicles in underground parking facilities.

This study developed a hydrogen leak localization and early warning platform for underground parking garages by integrating computational fluid dynamics numerical simulations, deep learning algorithms, and visualization technology. First, 240 typical scenarios covering different leakage rates and locations were simulated using Fluent, and a high-fidelity and high-volume dataset of original signals was constructed. Second, an innovative deep learning localization model integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural networks (CNNs), and bidirectional long short-term memory (BiLSTM) networks (hereafter known as "the CEEMDAN–CNN–BiLSTM deep learning model") was developed. In this architecture, the CEEMDAN algorithm performs multiscale decomposition and denoising on nonstationary concentration signals, effectively eliminating interference from environmental noise. CNNs are used to extract the spatial distribution features of the sensor array data, whereas BiLSTM networks are used to deeply mine the temporal sequence dependencies of gas diffusion. Finally, an early warning platform integrating data import, intelligent hierarchical alarming, and visual leakage source localization was developed using MATLAB App Designer.

The results indicated that the combustible hydrogen cloud primarily accumulated beneath the vehicle and around the side door areas, stabilizing at 25 s as the leakage and hydrogen diffusion rates reached dynamic equilibrium. The CEEMDAN–CNN–BiLSTM deep learning model demonstrated outstanding performance. Allowing for an error margin of one parking space, the row deep learning model achieved 100% accuracy, precision, recall, and F1 scores, whereas the column deep learning model attained an accuracy of 99%, with precision, recall, and F1 score reaching 99.5%. Using two sets of row and column deep learning models, preliminary localization was achieved 5 s after an alarm was triggered, meeting the strict timeliness requirements for emergency response. Furthermore, high-precision localization was accomplished after 20 s of data accumulation, ensuring accurate results. These findings provide technical support for balancing the efficiency and precision of hydrogen safety monitoring in underground parking garages. The proposed platform enables real-time data import, hierarchical alarming, and leak localization, provides an intuitive display of concentration changes at the sensors, and precisely locates the leak.

The simulation–data–model–platform workflow fosters the interdisciplinary competencies and safety risk awareness of students as well as their ability to solve real-world engineering problems. This initiative offers a practical, results-oriented approach to hydrogen safety and related professional education.

The oxy-coal combustion steam system of near-zero emissions efficiently uses coal resources and reduces CO₂ emissions. It uses demineralized coal as fuel that reacts with pure oxygen and steam within a high-pressure combustion chamber. Demineralization modifies the chemical structure of coal and changes its reactivity. However, the evolution of abundant short-lived free radicals during char combustion cannot be easily observed and detected via experiments.

The combustion mechanism of the Zhundong demineralized char (ZDDChar) was comprehensively analyzed herein. ¹³C solid-state nuclear magnetic resonance (¹³C-NMR) was used to chemically characterize the carbon molecular structure of ZDDChar and develop its macromolecular structure model. Combustion microreaction of ZDDChar was simulated under high steam concentration, oxygen concentration, temperature, and pressure via the molecular dynamics simulation of the reaction force field molecular dynamics (ReaxFF MD). As a result, the O₂/H₂O combustion mechanism of ZDDChar was elucidated at the molecular level, thereby addressing the experimental limitations.

Results revealed that ZDDChar has a molecular formula of C₈₀H₂₈O₁₆N. Its macromolecular structural model was characterized by the predominance of thick cyclic aromatic hydrocarbons, with oxygen-containing functional groups primarily in the form of hydroxyl, carboxyl, carbonyl, and pyran groups. Contrary to the conventional ZDDChar–air combustion, O₂/H₂O combustion was characterized by C+H₂O and C+O₂ reactions that competed with each other. High concentrations of H ions and OH radicals promoted the C+H₂O gasification reaction in a positive direction. The competition between O, H, and OH radicals for reactive sites within ZDDChar varied under different atmospheres. High pressures considerably promoted the C+O₂ reaction compared with the C+H₂O reaction. In addition, the combustion of char was enhanced at high pressures, and the combustion reaction rates increased at high temperatures. The depth of the char burning degree was directly proportional to the duration of combustion. The combustion reaction of ZDDChar under the aforementioned four conditions was initiated by the rupture of a side-branch chain and the decomposition of an unstable aliphatic ring, followed by the ring-opening and ring-breaking of thick ring aromatic hydrocarbons with larger bond energy into small molecular fragments. The small molecule fragments interacted with the surrounding reactive free radicals to form stable combustion products.

The ReaxFF MD simulation of O₂/H₂O combustion of ZDDChar offered unique insights into the combustion of ZDDChar at the microscopic level. In addition, successive bond-breaking and bond-forming processes during char combustion could be observed via this simulation, and the evolution of combustion intermediate products could be elucidated. It also provides a foundation for students to develop a comprehensive understanding of the coal combustion mechanism and offers a theoretical basis for the development and application of subsequent combustion technologies.

To meet the targets set in the “Carbon Peak” and “Carbon Neutrality” initiatives, it is imperative to enhance the energy-saving and environmental protection capabilities of the aluminum industry. In this industry, optimizing the combustion process in aluminum melting furnaces is vital for enhancing thermal efficiency and curtailing NOx emissions, thereby ensuring energy conservation and emission reduction.

In this study, the multifield coupled combustion process in an aluminum melting furnace is simulated to analyze the combustion characteristics of the furnace. The analysis focuses on the distribution of temperature fields, velocity fields, and component concentration fields in the furnace. The Box–Behnken response surface methodology is employed to design experiments for achieving the highest possible heat flow density (q) on the upper surface of aluminum along with the smallest possible equivalent NOx emissions (eq-NOx). The correlation between different influencing factors and the optimization objectives is analyzed, the effect of multiparameters on the optimization objectives is explored, and the optimum condition is proposed.

The results indicate that the magnitude of the correlation with q on the upper surface of aluminum follows the order of burner vertical inclination > oxygen concentration > burner horizontal inclination. Moreover, the magnitude of the correlation with the smallest possible eq-NOx follows the order of oxygen concentration > burner horizontal inclination>burner vertical inclination>excess air coefficient. A balanced consideration is given to the correlation of each factor with q and eq-NOx, leading to the proposed optimized conditions of an oxygen concentration of 18%)(volume), an excess air factor of 1.05, a burner vertical inclination of 85.5°, and a burner horizontal inclination of 83.5°. Under these conditions, q on the upper surface of aluminum is determined to be 93 958 W/m², while eq-NOx is calculated to be 0.002 22× 10^–6/(W·m^–2). A comparison of the optimized conditions with the actual operating conditions reveals that the maximum temperature of the furnace and the temperature inhomogeneity in the furnace are reduced under the optimized conditions. Furthermore, the flame stroke of methane combustion is extended, and the oxygen concentration in the combustion flame stroke is decreased, the q on the upper surface of aluminum increases by 1.76%, and the eq-NOx emissions decrease by 40%.

The employment of Box-Behnken response surface methodology for optimizing the combustion of the aluminum melting furnace facilitates the construction of a regression model, thus enabling the extrapolation of response values from limited test point data to non-test points. The optimization result of the response surface methodology is capable of attaining an arbitrary point that is aligned with the established constraints. Consequently, this optimization solution is rendered more accurate and can serve as a reliable guide to the structural design of aluminum melting furnaces, thereby providing a foundation for production optimization.