Process Safety and Environmental Protection最新文献

This study addresses one of the knowledge gaps in liquid tank safety, i.e., the assessment of the coupling hazards between physical processes and chemical reactions in liquid tanks under transient high temperatures. If the phase transition process of the liquid storage tank occurs simultaneously with a gaseous explosion, a significantly more intense energy release will be generated within the tank. However, due to the challenges of numerical calculations and the complexities of experimental design, current research has yet to explore the potential hazards associated with the explosion of vapor and air within liquid storage tanks. A novel numerical model has been established to simulate the coupled processes of phase transitions and chemical reactions in this research. The findings indicate that phase transition and chemical reactions commence at the intersection of the two-phase interfaces and the tank walls. After the cessation of transient high temperature, the upward trend in pressure and temperature within the tank will persist for a certain duration. As the radiation temperature rises and the duration extends, phase transition and chemical reactions within the liquid tank occur increasingly earlier. The duration of the chemical reactions decreases as the radiation temperature increases and the duration extends; however, the molar concentration of reactants consumed during the reaction does not exhibit a monotonic change. The intersection of the high-temperature hazard zone and the premixed hazard zone, where both ignition energy and concentration conditions are met, can lead to intense chemical reactions. As the radiation temperature rises, the ignition energy also increases; however, this leads to greater instability in the premixed hazard zone, thereby increasing the likelihood of secondary explosions.

Hydrothermal carbonization (HTC) was conducted to observe the effect of extracellular polymeric substances (EPS) structure on the nitrogen migration mechanism during the HTC process. The denitrification efficiency of activated sewage sludge (ASS) decreases with an increase in EPS stripping intensity. Loosely and tightly bound extracellular polymers diminish the denitrification effectiveness by inhibiting the hydrolysis of nitrogen-containing organic compounds and protecting the cell structure, respectively. Intracellular nucleic acids and proteins generate stable N-containing species, such as heterocyclic-N and quaternary-N, which are unfavorable for deep denitrification. In general, nitrogen removal is more effective outside the cell than inside. Deep destruction of EPS eliminated a considerable number of N-containing components, and the N/C value of hydrochar at 180 °C without cell membrane protection was 0.06, which dropped to half that of ASS (0.11). This study revealed the influence of EPS structure on nitrogen migration in hydrothermal environment and provided significant insights for the preparation of clean solid fuels.

Fast identification of flammable chemicals is essential for industrial production and laboratory safety. With the continuous advancement of sensor technology, data-driven methods have become a promising tool for gas identification. However, these methods face problems such as insufficient feature learning, unstable prediction of the single classifier, and overfitting caused by insufficient data. In this work, a kernel-based BLS (KBLS) method is proposed, in which the kernel matrix is used to calculate the sample distances and map the feature nodes to the kernel space to reduce the uncertainty. In addition, KBLS uses a pseudo-inverse method to solve the weights, which greatly avoids the risk of overfitting while improves computational efficiency. To avoid the errors caused by a single classifier for specific gas samples, KBLS is used as the weak learner and combined with the AdaBoost algorithm to form an Ada-KBLS classifier to achieve fast and accurate gas identification. In the Ada-KBLS model, the sample weights obtained by the previous weak learners are used to train the following weak learners. This method improves the classification performance by paying attention to difficult and misclassified samples and integrating the classification results of multiple weak learners. In addition, a dataset containing four flammable gases is used to verify the effectiveness of the Ada-KBLS model. The initial stage of all response data is divided into different time windows as the input of the model to test the fast gas identification ability of the method. The Ada-KBLS achieves an average classification accuracy of 98.4 % in the 4 s time window, the best among all models, and the training time is only 6.22 s. The result represents a 0.4 % improvement over the second-best model, KBLS, and a 4.5 % increase compared to the 93.9 % accuracy achieved by Random Forest (RF). In addition, the precision, recall, and F1-score of ethanol gas classification reach high values of 100 %. The experimental results demonstrate the robustness and effectiveness of the proposed method in handling the task of fast detection of flammable gases, thus promoting the application of BLS and ensemble learning in gas identification.

Electrochemical regeneration of ferric ions catches more and more attention since it could decrease iron sludge to avoid the sludge problem while it could simultaneously obtain similar robust removal efficiency to refractory organic contaminants in wastewater treatment as the normal Fenton agent method. However, the electrochemical regeneration of ferric ions in aqueous solution was very slow. In this paper, the mass transfer limit of ferric ions in electro-Fenton reaction was evaluated. Koutecky-Levich equation reveals an extremely low diffusion coefficient (D₀) value of ferric ions since its complex coordination of [Fe(HO)_x(H₂O)_6-x]^3-x. The D₀ value was only 2.70 × 10⁻⁶ cm²·s⁻¹. Flow-through reactor was therefore introduced in which the ferric ions was designed to penetrate through the 73.1 μm porous holes in the graphite fiber electrode. The μm-scaled confinement of ferric ions diffusion inside the holes was proved to successfully enhance the reduction current of ferric ions by more than 200 % since the diffusion distance of the ferric ions was significantly decreased in the flow-through reactor. However, besides the benefit of the flow-through reactor, the ferric ions reduction electro-Fenton (FeRR electro-Fenton) in flow-through still faces both the pH limit and H₂O₂ decomposition problems. Hydrogen evolution reaction (HER) could also cause the decrease of pH which exceeded the optimal pH window for Fenton and therefore destroyed the electro-Fenton reaction consequently although the electrochemical reaction of FeRR was 770 mV prior to HER reaction. The regeneration of Fe (II) process simultaneous destruction of H₂O₂ since H₂O₂ e-reduction was 120 mV prior to FeRR reaction, which resulted in only very low H₂O₂ concentration suitable for FeRR electro-Fenton. Even using stepwise addition of H₂O₂ in electro-Fenton, the decomposition of H₂O₂ still could not be avoided. Although the decomposition of H₂O₂ quite limited the application of FeRR electro-Fenton in real application, FeRR electro-Fenton still supports the enhancement removal efficiency of the refractory organic contaminants under low organic contaminants application.

In the wake of the 2011 Fukushima nuclear disaster, the presence of radioactive cesium (¹³⁷Cs) in nuclear wastewater has posed a critical and enduring health risk. Addressing this challenge, we have synthesized a gelatin aerogel, denoted as KCuFC/GA, immobilized with potassium cupric ferrocyanide (KCuFC) through an in situ approach, aiming for the efficient extraction of ¹³⁷Cs from aqueous environments. This novel aerogel's rich porous structure enhances the accessibility of adsorption sites, thereby significantly promoting the capture of Cs⁺. The adsorption of cesium onto KCuFC/GA was investigated under various experimental conditions, including initial solution pH, contact time, initial cesium concentration, and the presence of coexisting ions (K⁺, Na⁺, Ca²⁺, Mg²⁺, Sr²⁺). The results indicated that the adsorption performance was largely independent of pH, achieving a high cesium removal rate of 93.4 % within the first 5 minutes. The cesium adsorption data were well-described by the Langmuir adsorption model, indicating a maximum adsorption capacity of 222.22 mg·L⁻¹. Furthermore, in the presence of various competing ions, KCuFC/GA has demonstrated unparalleled selectivity for Cs⁺, with a partition coefficient (K_d) of 2.49 × 10⁵ mL·g⁻¹, and has sustained its adsorptive properties across five cycles of use. Through systematic investigation, including X-ray photoelectron spectroscopy (XPS) analysis, we have elucidated the adsorption mechanism, highlighting the pivotal role of ion exchange between lattice K⁺ and Cs⁺. The straightforward fabrication process and the aerogel's robust cesium removal capabilities from complex solutions indicate that KCuFC/GA is a promising candidate for real-world applications in the treatment of radioactive wastewater.

The development of efficient and cost-effective dye-sensitized solar cells is crucial for advancing third-generation solar technology. Despite their advantages, dye-sensitized solar cells face challenges due to the high cost of platinum-based counter electrodes, which impedes their commercialization. MXenes and MXene-based composites have emerged as promising alternatives, offering exceptional electrochemical properties, high catalytic activity, and large surface area. This review examines the potential of MXenes in enhancing dye-sensitized solar cells’ performance. The findings suggest that Ti₃C₂T_x MXene exhibits remarkable electron transfer efficiency, making it a viable substitute for platinum. Additionally, composites, such as Ti₃C₂T_x with graphene, demonstrate superior electrical conductivity and catalytic activity, outperforming both pure MXene and graphene electrodes. The key performance metrics include cathodic peak current density, fill factor, short-circuit current, and charge transfer resistance, indicating that MXenes can match or exceed traditional materials. The Ti₃C₂T_x/graphene composite is recommended for its enhanced properties and cost-effectiveness.

Bioinspired materials hold great potential for transforming energy storage devices due to escalating demand for high-performance energy storage. Beyond biomimicry, recent advances adopt nature-inspired design principles and use synthetic chemistry techniques to develop innovative hybrids that merge the strengths of biological and engineered materials. The multifaceted role of hierarchical structures, interfacial engineering, conjugated polymers, hybrid materials, and templating approaches is a powerful tool to translate bioinspired designs into high-energy, durable, and sustainable storage technologies by bridging fundamental biological motifs with rational materials engineering. Bioinspired hierarchical nanostructured electrodes provide accelerated ion and electron transport and electrolytes with enhanced safety by leveraging natural ion regulation mechanisms. However, significant challenges remain in reproducing the complex, dynamic interactions between material constituents and large-scale manufacturing. This review provides a comprehensive overview of bioinspired materials strategies that go beyond biomimicry to enable transformative advances in diverse storage applications spanning batteries, supercapacitors, fuel cells, and beyond. We critically analyze the structural design principles, synthetic approaches, characterization techniques, and theoretical aspects of bioinspired material innovations across multiple length scales. Perspectives, challenges, and opportunities are discussed in depth to provide critical insights into how bioinspiration can be harnessed to engineer unprecedented energy storage performances.

As one of the most representative hard-to-biodegrade industrial wastewater, coking wastewater is poor in biodegradability and has a high concentration of organic matter. Consequently, even after treatment using biochemical technology, the discharge standards cannot be reached. We used the self-developed Fe-loaded needle coke electrodes (Fe-NCPEs) as particle electrodes and established a three-dimensional electro-Fenton (3DEF) system for the treatment of bio-treated coking wastewater (BTCW) in this study. The 3DEF system's conditions for treating BTCW were optimized using Response Surface Method (RSM). When the initial pH was 4.69, the applied voltage was 11.1 V, the Fe-NCPE dosage was 11.63 g L⁻¹, and the COD was reduced from 387 to 57.3 mg L⁻¹ and the colour removal rate reached 99 % after 3 h of reaction, meeting local coking wastewater discharge standards. The power consumption is only 15 kWh per ton of BTCW, indicating that this system can treat the BTCW with high efficiency and low energy consumption. UV-Vis, FTIR, and GC-MS analysis illustrated that cyclic aromatic compounds, esters, and ethers can be efficiently degraded to alkanes and olefins by the 3DEF system. The ·OH quenching experiments showed that in the 3DEF system, the ·OH is the main reactive group, which can oxidize the large hard-to-degrade organic compounds in BTCW to small organic compounds, and even to CO₂ and H₂O. Dynamic experiments showed that the 3DEF system can successfully remove pollutants from BTCW under continuous flow conditions with an HRT of 3 h, allowing it to meet emission standards.

Accurate prediction of air quality is crucial for ensuring the scientific validity and effectiveness of air pollution control measures. This study proposes a combined deep learning (DL) model (XGBoost-GDA-TCN-IMRFO-GRU) for predicting hourly air quality index (AQI) data in four cities. The model integrates Extreme gradient boosting (XGBoost) for feature selection, Gaussian data augmentation (GDA), improved manta ray foraging optimization (IMRFO) algorithm, temporal convolutional network (TCN), and gated recurrent unit (GRU). XGBoost calculates the scores of pollutant gases affecting AQI, selecting the top four important pollutants (PM_2.5, PM₁₀, NO₂, O₃) based on their importance rankings. GDA enhances the robustness of the DL models and addresses the limitations of insufficient and overfitting training datasets. Additionally, the IMRFO algorithm, with two improved strategies, is applied to enhance the GRU model. TCN extracts spatiotemporal features of AQI, while GRU constructs a temporal model for efficient computations. Compared to eleven benchmark models, the proposed model demonstrates superior performance in terms of MAE, RMSE, MAPE, and NSE, achieving high accuracy and optimal prediction performance. Specifically, the XGBoost-GDA-TCN-IMRFO-GRU model reduces RMSE, MAE, and MAPE by 33–60 %, 39–68 %, and 39–66 %, respectively, compared to the TCN model. Therefore, the XGBoost-GDA-TCN-IMRFO-GRU model can provide reliable early warnings for air quality, which is of great significance for air pollution prevention and the sustainable development of society.