Erosion caused by solid particles in a pipeline is one of the main problems endangering the safety production of the oil and gas industry, which may lead the equipment to malfunction or even fail. However, most of the previous studies focused on the standard elbow, and the erosion law of right-angle elbow and blind tee is rarely reported in the literature. This work aims to investigate the erosion law of different pipeline structures including 90° elbow, right-angle pipe, and tee pipe based on the production characteristics and engineering parameters of the gas field. An integrated CFD-DPM method is established including a realizable k-ε turbulence model, discrete phase model, and erosion rate prediction model. The accuracy of the model is evaluated by a series of experimental data of flow conditions of our previous work. Further, the erosion rate, pressure distributions, and particle trajectories in 90° elbow, right-angle pipe, and tee pipe under different flow velocities, particle mass flow rate, pipe diameter are investigated by applying the presented model. The results show that the blind tee has the most obvious growth rate, and the most serious erosion is located in the blind end of the pipe wall. The maximum erosion rate of the 1.5D is greater than that of the 3D elbow as a whole, and the 1.5D elbow is more concentrated in the serious erosion area. Furthermore, the erosion rate of the bend weld is much greater than that of the straight pipe weld. This study can provide a basis for the selection of different structural pipe fittings, thereby reducing the pipeline erosion rate and improving the integrity of the management of gas pipelines.

With the assistance of surfactant, Fe nanoparticles are supported on g-C₃N₄ nanosheets by a simple one-step calcination strategy. Meanwhile, a layer of amorphous carbon is coated on the surface of Fe nanoparticles during calcination. Transmission electron microscopy (TEM), scanning electron microscope (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and inductively coupled plasma (ICP) were used to characterize the morphology, structure, and composition of the catalysts. By electrochemical evaluate methods, such as linear sweep voltammetry (LSV) and cyclic voltammetry (CV), it can be found that Fe₂₅-N-C-800 (calcinated in 800 °C, Fe loading content is 5.35 wt.%) exhibits excellent oxygen reduction reaction (ORR) activity and selectivity. In 0.1 M KOH (potassium hydroxide solution), compared with the 20 wt.% Pt/C, Fe₂₅-N-C-800 performs larger onset potential (0.925 V versus the reversible hydrogen electrode (RHE)) and half-wave potential (0.864 V vs. RHE) and limits current density (2.90 mA cm⁻², at 400 rpm). In 0.1 M HClO₄, it also exhibits comparable activity. Furthermore, the Fe₂₅-N-C-800 displays more excellent stability and methanol tolerance than Pt/C. Therefore, due to convenience synthesis strategy and excellent catalytic activity, the Fe₂₅-N-C-800 will adapt to a suitable candidate for non-noble metal ORR catalyst in fuel cells.

Natural gas plays an important role in the transition from fossil fuels to new energy sources. With the expansion of pipeline networks, there are also problems with the safety of pipeline network operations in the process of transportation. Among them, third-party damage is a key factor affecting the safety of pipelines. In this paper, the risk factors of third-party damage are analyzed, and an evaluation model of natural gas pipeline damage is established using the GeNIe Modeler. Through Bayesian network reverse reasoning and a maximum cause chain analysis from the four aspects of personnel, environment, management, and equipment, it was found that the top five factors that have significant influence on third-party damage, are safety investment, the completeness of equipment, safety inspection frequency, the management of residents along the pipeline, and safety performance, with the posteriori probability in the model of 97.3%, 95.4%, 95.2%, 95.1%, 95.1%, respectively. Consequently, it is necessary for pipeline operation companies to secure investment on safety, to make sure that the safety equipment (system) works and is in a good condition, to maintain the safety inspection frequency in an organization, to build a management system for residents along the pipeline, and to conduct routine safety performance assessments accordingly.

Biofortification could improve the bioremediation efficiency of microbes in the reparation of marine environmental damage caused by oil spills. In this paper, Chlorella vulgaris LH-1 was used as a fortifier to enhance the degradation of a marine oil spill by endogenous microorganisms. The addition of C. vulgaris LH-1 increased the degradation efficiency of crude oil by 11.09–42.41% and considerably accelerated oil degradation efficiency. Adding C. vulgaris LH-1 to a crude oil environment can improve the activity of endogenous seawater microorganisms. The results of high-throughput sequencing showed that the main bacterial genera were Oceanicola, Roseibacillus, and Rhodovulum when exotrophic C. vulgaris LH-1 and seawater endogenous microorganisms degraded low-concentration crude oil together. However, the addition of high-concentration nutrient salts changed the main bacterial genera in seawater to unclassified Microbacterium, Erythrobacter, and Phaeodactylibacter. The addition of C. vulgaris LH-1 increased the abundance of marine bacteria, Rhodococcus, and Methylophaga and decreased the abundance of Pseudomonas, Cladosporium, and Aspergillus. The functional prediction results of phylogenetic investigation of communities by reconstruction of unobserved states indicated that C. vulgaris LH-1 could improve the metabolic ability of seawater endogenous microorganisms to degrade endogenous bacteria and fungi in crude oil.

Considering that simultaneous detection of xanthine (XA) and hypoxanthine (HXA) has been proved to be a reliable and feasible method for assessing fish freshness, a novel electrochemical sensing platform based on the ZnIn₂S₄/UiO-66-NH₂ modified glassy carbon electrode (GCE) was constructed in this study for XA and HXA determination. X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and Fourier transform infrared spectroscopy (FT-IR) were performed to exhibit the morphology and structural characteristics of ZnIn₂S₄/UiO-66-NH₂. The Brunauer–Emmett–Teller (BET) displayed that the introduction of UiO-66-NH₂ can improve the specific surface area of the hybrid. Besides, the electrochemical sensing performance of ZnIn₂S₄/UiO-66-NH₂ was evaluated by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). For simultaneously detecting XA and HXA, the fabricated electrochemical sensor shows wide linear ranges (0.025–40 µM and 0.3–40 µM) with low detection limits (0.0083 µM and 0.1 µM). This sensor also has 96–103% recovery in detecting XA and HXA content in large yellow croaker meat samples, demonstrating a promising application in the marine food industry.

Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was “safer”. The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks.