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CO2 pipeline transportation is a core link in the CCUS (Carbon Capture, Utilization, and Storage Technology) industry. Ensuring the flow safety of CO2 pipelines under transient conditions is currently a key and challenging issue in industry research. This paper focuses on the phase migration and safety control during the shutdown process of supercritical carbon dioxide pipelines. Taking a supercritical carbon dioxide transportation pipeline in Xinjiang Oilfield, China, as the research object, a hydro-thermal coupling model of the pipeline is established to simulate the pipeline and elucidate the coordinated variation patterns of temperature, pressure, density, and phase state. It was found that there were significant differences in the migration paths of the CO2 phase at different positions. The accuracy of the simulation results was verified through the self-built high-pressure visual reactor experimental system, and the influences of the initial temperature, initial pressure, and ambient temperature before pipeline shutdown on the slope of the phase migration path were explored. The phase migration line slope prediction model was established by using the least squares method and ridge regression method, the process boundary ranges and allowable shutdown time ranges for pipeline safety shutdowns in both summer and winter were further established. The research results show that when the pipeline operates under the low-pressure and high-temperature boundary, the CO2 in the pipeline vaporizes earlier from the starting point after the pipeline is shut down, and the safe shutdown time of the pipeline is shorter. There is a clear safety operation window in summer, while vaporization risks are widespread in winter. The phase migration path prediction formula and the safety zone division method proposed in this paper provide a theoretical basis and engineering guidance for the safe shutdown control of supercritical carbon dioxide pipelines, which can help reduce operational risks and lower maintenance costs.

Supercritical CO2 pipeline transportation is a crucial link in Carbon Capture, Utilization, and Storage (CCUS). Compared with traditional oil and gas pipelines, if a supercritical CO2 pipeline is shut down for an excessively long time, the phase state of CO2 may transform into a gas–liquid two-phase state. It is urgently necessary to conduct research on the phase change mechanism and safety control during the restart process of gas–liquid two-phase CO2 pipelines. Based on a certain planned supercritical carbon dioxide pipeline demonstration project, this paper proposes a new pipeline safety restart scheme that actively seeks the liquefaction of gaseous CO2 inside the pipeline by injecting liquid-phase CO2 at the initial station. Through numerical simulation and experimental methods, the co-variation laws of parameters such as temperature, pressure, density, and phase state during the pipeline restart process were revealed. It was found that the pipeline shutdown and restart process could be subdivided into four stages: shutdown stage, liquefaction stage, pressurization stage, and displacement stage. The phase transition line would form a closed curve that is approximately trapezoidal. It is suggested to optimize the restart scheme from aspects such as reducing the restart time, controlling the pressure rise rate, and saving CO2 consumption. It is proposed that the liquid holdup of CO2 fluid in the pipe at the initial moment of restart and the mass flow rate of CO2 injected at the initial station during the restart process are the main controlling factors affecting the evolution of the phase path of pipeline restart. For the demonstration project, the specific critical threshold values are given. The research results can provide a certain theoretical guidance and reference basis for the safe restart method of supercritical CO2 pipelines.

This study aims to clarify the influence of H2S concentration and temperature on the sulfide stress corrosion cracking (SSCC) susceptibility of 20# steel weld joints. 20# steel is a commonly selected pipe material for ground gas pipelines, and there is a risk of welds cracking after years of service. The selection of a corrosive environment is based on the working conditions of high-sulfur oil fields on site. Slow strain rate tensile (SSRT) tests were conducted under simulated high-sulfur gathering and transportation conditions across varying temperatures and H2S concentrations. The mechanical properties, SSCC susceptibility, fracture morphology, and elemental composition of fracture surfaces were systematically analyzed. As H2S concentration increased (5%, 7.5%, 10%) and temperature decreased (30–60 °C), the elongation after fracture and the reduction in area of 20# steel decreased, while the yield strength and SSCC susceptibility increased. The H2S concentration range of 0–5% represented a critical sensitivity interval for the material, where elongation after fracture decreased by up to 74%. Within the 5–10% H2S range, elongation decreased by only 2.11%, indicating a slowing trend of fracture toughness deterioration, though SSCC susceptibility still increased by 12%. Increasing the temperature from 30 °C to 60 °C reduced SSCC susceptibility by approximately 30%, confirming higher susceptibility at lower temperatures. Temperature exerts a lesser influence on material performance than H2S concentration. Also, 20# steel remains within the SSCC brittle fracture sensitivity zone in sulfur-containing environments, necessitating strict quality control to avoid defects and stress concentrations. In high-sulfur environments (H2S > 5%), further increases in H2S concentration have a diminished effect on cracking susceptibility.

In the CCUS industrial chain, the pipeline transportation of CO2 is a crucial link that connects the upstream and downstream. However, currently, there is still no reliable, stable, and efficient method for detecting pipeline leaks. Based on the time difference in arrival (TDOA) localization method within the acoustic emission technique, this study conducted preliminary experiments on air pipeline leak localization and experiments on gaseous CO2 pipeline leak localization, thereby establishing the applicability of acoustic emission technology for leak detection in gaseous CO2 pipelines. In the preliminary experiment on air pipeline leak location, the SNR (signal-to-noise ratio) of the CEEMDAN denoising algorithm is greater than that of the EEMD denoising algorithm. The larger the SNR, the smaller the signal interference, which proves the superiority of the CEEMDAN denoising algorithm. In the experiment on gaseous CO2 pipeline leak location, the CEEMDAN denoising algorithm was adopted. Five time-delay estimation methods, namely GCC, Roth weighting, PHAT weighting, ML weighting, and SCOT weighting, were used for location calculations. The positioning accuracies were 10.6%, 6.9%, 6.9%, 8.6%, and 8.6% respectively, all meeting the engineering accuracy requirements. Combining the results of the preliminary experiment on air pipeline leak location, the Roth weighting time-delay estimation method is recommended. The results show that: Acoustic emission technology can be used for the leak location of gaseous CO2 pipelines.

As the manufacturing industry continues to advance its digital transformation, intelligent sensing technology has become a key support for achieving precise, efficient and automated quality control. However, current production line monitoring systems predominantly rely on fixed and costly monitoring equipment and sensors, lacking flexible and interactive first-person perspective perception approaches centered on on-site operators. Meanwhile, factory process monitoring often depends solely on visual expression rather than balancing the capabilities of the simulation model and visual state detection, leading to delayed responses to abnormal systems and hindering the adjustment strategy feedback. To address these limitations, this study provides wearable sensing for key workers, enriching the state perception capabilities in industrial scenarios. Furthermore, to achieve dynamic model and real-time visual representation of production line operations, a multi-source information-enhanced Petri nets model is proposed in terms of engineering and user-friendliness. With the solid mathematical basics of the Petri nets and the enriched human–machine data from the product line, this method provides an intuitive, dynamic and accurate reflection of the production system’s real-time operational status, offering a scientific and reliable basis for operational decision-making. The proposed approach has been implemented in a real-world production system for reinforced concrete civil defense doors, and this engineering application can also be extended to many other scenarios.

Compared with other fossil energy sources, natural gas is characterized by compressibility, low energy density, high storage costs, and imbalanced usage. Natural gas pipeline supply systems possess unique attributes such as closed transportation and a highly integrated upstream, midstream, and downstream structure. Moreover, pipelines are almost the only economical means of onshore natural gas transportation. Given that the upstream of the pipeline features multi-entity and multi-channel supply including natural gas, coal-to-gas, and LNG vaporized gas, while the downstream presents a competitive landscape with multi-market and multi-user segments (e.g., urban residents, factories, power plants, and vehicles), there is an urgent social demand for non-discriminatory and fair opening of natural gas pipeline network infrastructure to third-party entities. However, after the fair opening of natural gas pipeline networks, the original “point-to-point” transaction model will be replaced by market-driven behaviors, making the verification and allocation of gas transmission capacity a key operational issue. Currently, neither pipeline operators nor government regulatory authorities have issued corresponding rules, regulations, or evaluation plans. To address this, this paper proposes a multi-dimensional quantitative evaluation model based on the Analytic Hierarchy Process (AHP), integrating both commercial and technical indicators. The model comprehensively considers six indicators: pipeline transportation fees, pipeline gas line pack, maximum gas storage capacity, pipeline pressure drop, energy consumption, and user satisfaction and constructs a quantitative evaluation system. Through the consistency check of the judgment matrix (CR = 0.06213 < 0.1), the weights of the respective indicators are determined as follows: 0.2584, 0.2054, 0.1419, 0.1166, 0.1419, and 0.1357. The specific score of each indicator is determined based on the deviation between each evaluation indicator and the theoretical optimal value under different gas volume allocation schemes. Combined with the weight proportion, the total score of each gas volume allocation scheme is finally calculated, thereby obtaining the recommended gas volume allocation scheme. The evaluation model was applied to a practical pipeline project. The evaluation results show that the AHP-based evaluation model can effectively quantify the advantages and disadvantages of different gas volume allocation schemes. Notably, the gas volume allocation scheme under normal operating conditions is not the optimal one; instead, it ranks last according to the scores, with a score 0.7 points lower than that of the optimal scheme. In addition, to facilitate rapid decision-making for gas volume allocation schemes, this paper designs a program using HTML and develops a gas volume allocation evaluation program with JavaScript based on the established model. This self-developed program has the function of automatically generating scheme scores once the proposed gas volume allocation for each station is input, providing a decision support tool for pipeline operators, shippers, and regulatory authorities. The evaluation model provides a theoretical and methodological basis for the dynamic optimization of natural gas pipeline gas volume allocation schemes under the fair opening model. It is expected to, on the one hand, provide a reference for transactions between pipeline network companies and shippers, and on the other hand, offer insights for regulatory authorities to further formulate detailed and fair gas transmission capacity transaction methods.

During spacecraft operations, structures experience extreme aerodynamic heating, necessitating thermal testing to gather data on the thermal response of surface materials. Given the nonrepeatable nature of these test articles, accurately predicting the temperature rise profile under thermal load inputs is essential before formal testing. Although theoretical analyses can develop precise internal heat transfer models for modules, limited test data hampers the modeling of inter-module heat transfer processes. Furthermore, variations in test article parameters across different tests restrict the generalizability of existing models. We present a hybrid modeling approach that integrates a physical model with a long short-term memory (LSTM) network to address these challenges. The LSTM model is trained on historical data to capture complex inter-module heat transfer dynamics. Additionally, varying parameters of the test articles are included as model inputs to enhance versatility and adaptability. Experiments demonstrate that the model achieves high prediction accuracy (MAE = 17.41 (K) R2 = 0.9988) even when test article parameters differ from historical data. Moreover, it shows strong adaptability to changes in the input power signal (MAE = 34.91 (K) R2 = 0.9990). This study successfully predicts temperature profiles during thermal testing using minimal test data, thereby improving computational efficiency and reducing testing costs, which facilitates the effective implementation of formal thermal testing.

Background: To overcome the gastrointestinal and hepatic toxicity of oral pirfenidone (PFD) in the treatment of idiopathic pulmonary fibrosis (IPF), this study systematically constructed a minimal-component, buffer-free pirfenidone aerosol inhalation solution (PFD-AIS), achieving lung-targeted delivery, reduced systemic exposure, and maintained antifibrotic efficacy. Methods: Analytical methods for PFD-AIS, covering content, related substances, aerodynamic particle size distribution (APSD), and delivered dose uniformity, were established. The prescription and preparation process of the formulation was optimized by evaluating its key quality attributes. Pharmacodynamic and pharmacokinetic evaluations of PFD-AIS were performed in a mouse lung-fibrosis model and SD rats. Results: The final specification of PFD-AIS was set to 40 mg:4 mL, containing 40 mg of PFD, 28 mg of sodium chloride, and 4 mL of injection water with a preparation process of 40 °C for 60 min and a pH range of 4–8. The PFD-AIS exhibited a fine particle fraction (FPF) of 56.1%, meeting the requirements for deep lung deposition. The delivered dose and delivery rate were 17.52 mg and 2.48 mg/min, respectively, both complying with inhalation formulation standards. In the bleomycin-induced IPF mouse model, the PFD-AIS markedly improved pulmonary fibrosis pathology, reduced the lung coefficient, and significantly lowered serum ALT/AST levels, indicating hepatic protection. In the SD rats, compared with oral dosing, PFD-AIS administration resulted in significantly lower AUC0−t (−63%) and AUC0–∞ (−67%) values, demonstrating a substantial reduction in systemic drug exposure. Conclusion: This work presents a complete, systematic chain—from formulation, process, and quality control to pharmacodynamics and pharmacokinetics—of a PFD-AIS. The PFD-AIS is effective and feasible, featuring a stable preparation process and controllable quality. Lung-directed drug delivery enhances PFD’s therapeutic efficacy, reduces systemic exposure and liver toxicity, and offers significant clinical advantages.

Gastric ulcers (GU) are a disease of the gastrointestinal tract that can be caused by excessive alcohol consumption and heavy use of nonsteroidal anti-inflammatory drugs. GU manifests predominantly as pathological damage, such as extensive inflammatory erosion and superficial bleeding of the gastric mucosa. Oxidative stress damage and the inflammatory response are now considered important predisposing factors for GU, suggesting that antioxidant and anti-inflammatory drugs could be treatments for GU. Nanoparticle drug carriers offer many advantages over conventional drugs, such as improved drug efficiency, increased drug stability, and increased half-life. We designed chitosan-bilirubin conjugate (CS-BR) nanoparticles and assessed the anti-inflammatory and antioxidant abilities of CS-BR in gastric epithelial cells. Then, we evaluated the intragastric retention time and the anti-ulcer effects of CS-BR in vivo. The in vitro data showed that CS-BR nanoparticles protect gastric epithelial cells against oxidative/inflammatory injury. The in vivo study demonstrated that CS-BR nanoparticles accumulate permanently in the stomach and exert powerful antioxidant and anti-inflammatory effects against GU. This study applied bilirubin to the treatment of GU and confirmed that CS-BR nanoparticles are effective at alleviating acute GU in an experimental model. The findings provide innovative ideas for prophylaxis against or treatment of GU.

Federated learning enables multiple devices to collaboratively train a high-performance model on the central server while keeping their data on the devices themselves. However, due to the significant variability in data distribution across devices, the aggregated global model’s optimization direction may differ from that of the local models, making the clients lose their personality. To address this challenge, we propose a Bidirectional Decoupled Distillation For Heterogeneous Federated Learning (BDD-HFL) approach, which incorporates an additional private model within each local client. This design enables mutual knowledge exchange between the private and local models in a bidirectional manner. Specifically, previous one-way federated distillation methods mainly focused on learning features from the target class, which limits their ability to distill features from non-target classes and hinders the convergence of local models. To solve this limitation, we decompose the network output into target and non-target class logits and distill them separately using a joint optimization of cross-entropy and decoupled relative-entropy loss. We evaluate the effectiveness of BDD-HFL through extensive experiments on three benchmarks under IID, Non-IID, and unbalanced data distribution scenarios. Our results show that BDD-HFL outperforms state-of-the-art federated distillation methods across five baselines, achieving at most 3% improvement in average classification accuracy on the CIFAR-10, CIFAR-100, and MNIST datasets. The experiments demonstrate the superiority and generalization capability of BDD-HFL in addressing personalization challenges in federated learning.