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Total dissolved gas supersaturation (TDGS) occurs when air mixes with water under pressure, which can be caused by features such as hydroelectric dams and waterfalls. Total dissolved gas supersaturation can cause harmful bubbles to grow in the tissues of aquatic animals, a condition known as gas bubble trauma (GBT). As gills are the primary gas exchange surface for most fish, it is through the gills that elevated total dissolved gases enter the blood and tissues of a fish. We describe the role of the gills in admitting TDGS into the body and discuss potential effects of bubbles in the gills on blood oxygen and carbon dioxide diffusion, blood ion and pH homeostasis, and nitrogenous waste excretion, as well as downstream effects on aerobic swimming performance.

Dissolved gas analysis (DGA) is a vital method for the online detection of transformer operation state. The adsorption performance of a SnP3 monolayer modified by transition metal Cr regarding six characteristic gases (CO, C2H4, C2H2, CH4, H2, C2H6) dissolved in oil was studied. The study reveals the relevant adsorption and gas-sensing response mechanisms through calculations of the adsorption energy, density of states, differential charge density, energy gap, and recovery time. The results display a considerable increase in the adsorption effect of the Cr-SnP3 monolayer on six gases. The CO, C2H2, and C2H4 gases lead to chemical adsorption, and the CH4, H2, and C2H6 gases lead to physical adsorption. Combined with the recovery time, the Cr-SnP3 monolayer has a strong adsorption effect on CO and C2H2 gases at normal temperatures and even high temperatures, and the adsorption is stable. C2H4 gas can be rapidly desorbed from the Cr-SnP3 monolayer at 398 K. Therefore, the Cr-SnP3 monolayer can be expected to serve as a CO and C2H2 gas adsorbent and a resistive gas sensor for C2H4 gas. This research offers a theoretical foundation for the development of the Cr-SnP3 monolayer in gas-sensitive materials.

The online monitoring of transformer insulation is crucial for ensuring power system stability and safety. Dissolved gas analysis (DGA), employing highly sensitive gas sensors to detect dissolved gas in transformer oil, offers a promising means to assess equipment insulation performance. Based on density functional theory (DFT), platinum modification of a WTe2 monolayer was studied and the adsorption behavior of CO and C2H4 on the Pt-WTe2 monolayer was simulated. The results showed that the Pt atom could be firmly anchored to the W atoms in the WTe2 monolayer, with a binding energy of −3.12 eV. The Pt-WTe2 monolayer showed a trend toward chemical adsorption to CO and C2H4 with adsorption energies of −2.46 and −1.88 eV, respectively, highlighting a stronger ability of Pt-WTe2 to adsorb CO compared with C2H4. Analyses of the band structure (BS) and density of states (DOS) revealed altered electronic properties in the Pt-WTe2 monolayer after gas adsorption. The bandgap decreased to 1.082 eV in the CO system and 1.084 eV in the C2H4 system, indicating a stronger interaction of Pt-WTe2 with CO, corroborated by the analysis of DOS. Moreover, the observed change in work function (WF) was more significant in CO systems, suggesting the potential of Pt-WTe2 as a WF-based gas sensor for CO detection. This study unveils the gas-sensing potential of the Pt-WTe2 monolayer for transformer status evaluation, paving the way for the development of gas sensor preparation for DGA.

The prediction of dissolved gas concentrations in oil can provide crucial data for the assessment of power transformer conditions and early fault diagnosis. Current simulations mainly focus on the generation and accumulation of characteristic gases, lacking a global perspective on gas diffusion and dissolution. This study simulates the characteristic gases produced by typical faults at different flow rates. Using ANSYS 2022 R1 simulation software, a gas–liquid two-phase model is established to simulate the flow and diffusion of characteristic gases under fault conditions. Additionally, a fault-simulation gas production test platform was built based on a ±400 kV actual converter transformer. The experimental data show good consistency with the simulation trends. The results indicate that the diffusion of dissolved gases in oil is significantly affected by the oil flow velocity. At higher flow rates, the characteristic gases primarily move within the oil tank along with the oil circulation, leading to a faster rate of gas dissolution in oil and a shorter time to reach equilibrium within the tank. At lower flow rates, the diffusion of characteristic gases depends not only on oil flow circulation but also on self-diffusion driven by concentration gradients, resulting in a nonlinear change in gas concentration across various monitoring points.

Total dissolved gas (TDG) supersaturation downstream of hydropower plants may cause gas bubble disease (GBD) and harmful effects in fish. Little is known about tolerance levels of TDG supersaturation on Atlantic salmon (Salmo salar Linnaeus, 1758) in natural rivers. The present study investigated the effects of TDG supersaturation on the survival of Atlantic salmon smolts at two field sites in Norway. Here, we kept smolts in cages at increasing distances from hydropower plants known to cause TDG supersaturation and at control sites. We recorded fish mortality and examined for GBD using a stereo microscope. Mortality and symptoms of GBD commenced in fish exposed to an average of 108.3% TDG (maximum 111.0%, water depth 0.55 m) for 2 days. Significant differences in time before mortality at the control sites and test sites commenced at 110.2% TDG (maximum 111.8%) for 3 days. The study indicates that Atlantic salmon may be more vulnerable to TDG supersaturation than Pacific salmonids, which are considered at risk when the TDG is above 110%. In addition, the study provides important data to link effects caused by TDG in the laboratory and in the field.

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers’ four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers’ four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods.

The production of total dissolved gas (TDG) supersaturation resulting from dam discharges has been identified as a causative factor for gas bubble disease (GBD) or mass mortality in fish. In this study, the mitigation solution for fish refuge in supersaturated TDG water was explored by using microbubbles generated by aeration to enhance supersaturated TDG dissipation. The effects of various aeration factors (aeration intensity, water depth, and aerator size) on the dissipation processes of supersaturated TDG were quantitatively investigated through a series of tests conducted in a static aeration column. The results indicated that the dissipation rates of supersaturated TDG increased as a power function with the factors of aeration intensity and aerator size and decreased as a power function with increasing water depth. A universal prediction model for the dissipation rate of supersaturated TDG in the aeration system was developed based on the dimensional analysis of the comprehensive elements, and the parameters in the model were determined using experimental data. The outcomes of this study can furnish an important theoretical foundation and scientific guidance for the utilization of aeration as a measure to alleviate the adverse impacts of supersaturated TDG on fish.

This research focuses on problem identification due to faults in power transformers during operation by using dissolved gas analysis such as key gas, IEC ratio, Duval triangle techniques, and fuzzy logic approaches. Then, the condition of the power transformer is evaluated in terms of the percentage of failure index and internal fault determination. Fuzzy logic with the key gas approach was used to calculate the failure index and identify problems inside the power transformer. At the same time, the IEC three-gas ratio and Duval triangle are subsequently applied to confirm the problems in different failure types covering all possibilities inside the power transformer. After that, the fuzzy logic system was applied and validated with DGA results of 244 transformers as reference cases with satisfactory accuracy. Two transformers were evaluated and practically confirmed by the investigation results of an un-tanked power transformer. Finally, the DGA results of a total of 224 transformers were further evaluated by the fuzzy logic system. This fuzzy logic is a smart, accurate tool for automatically identifying faults occurring within transformers. Finally, the recommendation of maintenance strategy and time interval is proposed for effective planning to minimize the catastrophic damage, which could occur with the power transformer and its network.

Upflow anaerobic sludge blanket (UASB) reactors are considered to be a sustainable and well-established technology for sewage treatment in warm climate countries. However, gases dissolved in the effluent of these reactors, CH 4 and H 2 S in some instances, are a major drawback. These dissolved gases can be emitted into the atmosphere downstream of the anaerobic reactors, resulting in odour nuisance and, in the case of H 2 S, corrosion, while in the case of CH 4 , increasing greenhouse gas emissions with a significant loss of potentially recoverable energy. In this sense, this study aims to provide a critical review of the recent efforts to control CH 4 and H 2 S dissolved in UASB reactor effluents, with a focus on the different available techniques. Different desorption techniques have been tested for the removal/recovery of dissolved CH 4 and H 2 S: diffused aeration, simplified desorption chamber, packed desorption chamber, closed downflow hanging sponge reactor, membrane contactor, and vacuum desorption chamber. Other recent publications addressing the oxidation of these compounds in biological posttreatments with simultaneous nitrification/denitrification of ammonia were also discussed. Additionally, the rationale of CH 4 recovery was determined by energy balance and carbon footprint approaches, and the H 2 S removal was examined by modelling its emission and atmospheric dispersion.

Penta-graphene, a novel two-dimensional carbon allotrope entirely composed of pentagonal carbon rings, has attracted increasing attention due to its unique geometric structure, mechanical robustness, and intrinsic semiconducting nature. In this study, we systematically investigate the adsorption behavior of three typical dissolved gases in transformer oil (CO, C2H2, and C2H4) on penta-graphene using first-principles calculations based on density functional theory. The optimized adsorption configuration, adsorption energy, charge transfer, adsorption distance, band structure, density of states, charge density difference, and desorption time are analyzed to evaluate the sensing capability of penta-graphene. Results reveal that penta-graphene exhibits moderate chemical interactions with CO and C2H2, accompanied by noticeable charge transfer and band structure changes, whereas C2H4 shows weaker physisorption characteristics. The projected density of states analysis further confirms the orbital hybridization between gas molecules and the substrate. Additionally, the desorption time calculations suggest that penta-graphene possesses good sensing and recovery potential, especially under elevated temperatures. These findings indicate that penta-graphene is a promising candidate for use in gas sensing applications related to the monitoring of dissolved gases in transformer oils.