Gas Concentration Research Articles

Classification of Composite Hazards of Gas and Coal Spontaneous Combustion in Gob

ABSTRACT To prevent the occurrence of gas and coal spontaneous combustion composite disasters in gob areas during coal mining, this study considers the gob area of the Longfeng Coal Mine as the experimental background. By analyzing the pressure values of the hydraulic props in the isolation wall, the composition of gases in the gob area, and the low-temperature oxidation characteristics of coal, it was found that the pressure values of the hydraulic props were correlated with the concentration of gases in the gob area, temperature, and the g-factor and free radical concentration of coal. The absolute values of the correlation coefficients were ranked as T > O2 > CH4 > CO2 > g-values > Ng > N2, and all correlation coefficients were greater than 0.85. When the isolation wall was located in the stress concentration area, the wall deformed and cracked, leading to frequent gas exchange between the gob-side entry retaining (GSER) and the gob area, which increased the risk of gas and coal spontaneous combustion composite disasters in the gob area. According to the safety risk level, the areas adjacent to the gob area were divided into high-risk areas (0 ~ 100 m, 300 ~ 600 m, 700 ~ 900 m, 1050 ~ 1200 m), moderate risk areas (200 ~ 300 m, 900 ~ 1050 m), general risk areas (600 ~ 700 m), and low-risk areas (100 ~ 200 m), the positions at 500 ~ 550 m and 700 ~ 750 m were located in the gas and coal spontaneous combustion composite disaster area. The study suggests that for high-risk areas, the injection amount of fire extinguishing materials should be increased to strengthen safety assurance, whereas for low-risk areas, the injection can be moderately adjusted and reduced to achieve the optimal balance between economic benefits and safety benefits.

Implementing Internet of Things for Real-Time Monitoring and Regulation of Off-Season Grafting and Post-Harvest Storage in Citrus Cultivation: A Case Study from the Hilly Regions of Nepal

Citrus fruit cultivation, especially mandarin oranges, is crucial to the economy of Nepal’s hilly regions due to their ideal geoclimatic conditions. Despite its economic importance, the sector faces several challenges, such as inadequate grafting techniques, low-quality saplings, and ineffective post-harvest storage. This paper explores these issues and proposes innovative solutions through the use of Internet of Things (IoT) technology. To address these challenges, we identified key areas for improvement. First, we focused on extending grafting practices during the off-season to ensure a higher success rate and better-quality saplings. Second, we examined different post-harvest storage methods to determine their effectiveness in terms of shelf life, decay loss, and quality of fruit. In addition to exploring post-harvest strategies, this paper provides preharvest recommendations for farmers, emphasizing methods to enhance fruit quality and longevity through effective pre-storage practices. Our IoT-based approach introduces off-season grafting in polyhouses and advanced monitoring for post-harvest storage. The results are promising: We achieved grafting success rates of 91% for acid lime and 92% for local mandarin orange varieties. Additionally, our research compared different post-harvest storage methods for mandarin oranges, including room, cellar, and cold chamber. We assessed these methods based on shelf life, physiological weight loss, and the total soluble solids (TSS) to titratable acidity (TA) ratio. The cold chamber proved to be the most effective method, offering superior conditions for storing mandarin oranges. The IoT-based monitoring system played a crucial role in maintaining optimal temperature, humidity, and gas content within the cold chamber, resulting in reduced post-harvest losses and extended shelf life. These findings highlight the transformative potential of IoT technology in mandarin orange cultivation and post-harvest storage.

Implementing Internet of Things for Real-Time Monitoring and Regulation of Off-Season Grafting and Post-Harvest Storage in Citrus Cultivation: A Case Study from the Hilly Regions of Nepal

Citrus fruit cultivation, especially mandarin oranges, is crucial to the economy of Nepal’s hilly regions due to their ideal geoclimatic conditions. Despite its economic importance, the sector faces several challenges, such as inadequate grafting techniques, low-quality saplings, and ineffective post-harvest storage. This paper explores these issues and proposes innovative solutions through the use of Internet of Things (IoT) technology. To address these challenges, we identified key areas for improvement. First, we focused on extending grafting practices during the off-season to ensure a higher success rate and better-quality saplings. Second, we examined different post-harvest storage methods to determine their effectiveness in terms of shelf life, decay loss, and quality of fruit. In addition to exploring post-harvest strategies, this paper provides preharvest recommendations for farmers, emphasizing methods to enhance fruit quality and longevity through effective pre-storage practices. Our IoT-based approach introduces off-season grafting in polyhouses and advanced monitoring for post-harvest storage. The results are promising: We achieved grafting success rates of 91% for acid lime and 92% for local mandarin orange varieties. Additionally, our research compared different post-harvest storage methods for mandarin oranges, including room, cellar, and cold chamber. We assessed these methods based on shelf life, physiological weight loss, and the total soluble solids (TSS) to titratable acidity (TA) ratio. The cold chamber proved to be the most effective method, offering superior conditions for storing mandarin oranges. The IoT-based monitoring system played a crucial role in maintaining optimal temperature, humidity, and gas content within the cold chamber, resulting in reduced post-harvest losses and extended shelf life. These findings highlight the transformative potential of IoT technology in mandarin orange cultivation and post-harvest storage.

Simultaneous measurement of solid concentration and solid moisture in gas–solid two-phase flow using a dual-modality electrical sensor

Abstract In a gas-solid two-phase flow system, simultaneous measurement of solid concentration and solid moisture is essential for some applications. However, during the measurement process, solid concentration and solid moisture are coupled in the measurement signals when the measurement signal is sensitive to both solid concentration and moisture. This makes it challenging to measure them simultaneously in the same measurement field. To solve this issue, a novel measurement method is proposed in this study, which uses a dual-modality electrical sensor to achieve simultaneous measurement of solid concentration and solid moisture in gas-solid two-phase flow. The dual-modality electrical sensor features a simple structure and captures measurement signals of capacitance and angle of dielectric loss within the same measurement section. With the above signals which are both sensitive to solid concentration and moisture, an iterative correction calculation method is adopted to achieve the simultaneous measurement of solid concentration and solid moisture. During the calculation process, the measured parameters are decoupled and continuously corrected until the related error values meet the threshold requirement. Numerical simulation experiments under three typical distributions and physical experiments under roping flow are carried out to verify the proposed measurement method. The results prove the effectiveness of the proposed measurement method in the simultaneous measurement of solid concentration and solid moisture for gas-solid two-phase flow.

Post-modified porous aromatic frameworks for carbon dioxide capture

A new method was developed for post-modification of porous aromatic framework-1 (PAF-1) with chloride and then amine groups confirmed by different characterizations such as nuclear magnetic resonance (NMR), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FTIR). Compared to PAF-1, the amine-functionalized PAF-1 (PAF-1-NH2) exhibits a 50% improvement in carbon dioxide (CO2) adsorption capacity reaching 42 cm3·g-1 at room temperature and 1 bar, and the uptakes under CO2 concentration in air (4 kPa) and flue gas (150 kPa) also greatly increase. The column breakthrough experiments showed that PAF-1-NH2 can separate CO2 from a simulated flue gas of CO2/N2 (15:85, v/v) indicating its potential applications in post-combustion systems.

Research on CO concentration detection based on deep learning and TDLAS technology

In order to improve the accuracy of detecting CO and other gases in the industrial field, a CO concentration detection scheme based on tunable diode laser absorption spectroscopy (TDLAS) and the CO absorption spectral line at 1566 nm was proposed. At the outset, after removing the outliers from the original data, the harmonic signal is filtered using a moving average filtering method to enhance the signal-to-noise ratio. Subsequently, a one-dimensional convolutional neural network (1D-CNN) is employed to extract the peak and valley values, as well as the mean values of each segment of harmonic data. In the end, the Long short term memory network (LSTM) model establishes a nonlinear fitting relationship between gas characteristics and concentration, and the attention layer is added to further optimize the network weight parameters. The experimental results indicate that the R2 for the test set is 0.9884, and the RMSE is 156.869. Through Allan deviation analysis, the detection limit reaches 1.9605 ppm at an integration time of 60 s. Compared to the traditional regression model, the accuracy and stability are significantly improved, making it highly suitable for gas detection in the industrial circle.

Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review.

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11-17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments.

Investigation of the flood event under global climate change with different analysis methods for both historical and future periods

ABSTRACT Global climate change is a phenomenon resulting from the complex interaction of human influences and natural factors. These changes lead to imbalances in climate systems as human activities such as greenhouse gas emissions increase atmospheric gas concentrations. This situation affects the frequency and intensity of climate events worldwide, with floods being one of them. Floods manifest as water inundations due to factors such as changes in rainfall patterns, rising temperatures, erosion, and sea level rise. These floods cause significant damage to sensitive areas such as residential areas, agricultural lands, river valleys, and coastal regions, adversely impacting people's lives, economies, and environments. Therefore, flood risk has been investigated in decision-making processes in the Diyarbakır province using the Analytical Hierarchy Process (AHP) method and future disintegration of global climate model data. HadGEM-ES, GFDL-ESM2M, and MPI-ESM-MR models with RCP4.5 and RCP8.5 scenarios were used. Model data were disintegrated using the equivalent quantile matching method. The study reveals flood risk findings in the HadGEM-ES model while no flood risk was found in the GFDL-ESM2M and MPI-ESM-MR models. In the AHP method, flood risk analysis was conducted based on historical data across Diyarbakır and interpreted alongside future rainfall data.

Flexible Resistive Gas Sensor Based on Molybdenum Disulfide-Modified Polypyrrole for Trace NO2 Detection.

High sensitivity and selectivity and short response and recovery times are important for practical conductive polymer gas sensors. However, poor stability, poor selectivity, and long response times significantly limit the applicability of single-phase conducting polymers, such as polypyrrole (PPy). In this study, PPy/MoS2 composite films were prepared via chemical polymerization and mechanical blending, and flexible thin-film resistive NO2 sensors consisting of copper heating, fluorene polyester insulating, and PPy/MoS2 sensing layers with a silver fork finger electrode were fabricated on a flexible polyimide substrate using a flexible electronic printer. The PPy/MoS2 composite films were characterized using X-ray diffraction, Fourier-transform infrared spectroscopy, and field-emission scanning electron microscopy. A home-built gas sensing test platform was built to determine the resistance changes in the composite thin-film sensor with temperature and gas concentration. The PPy/MoS2 sensor exhibited better sensitivity, selectivity, and stability than a pure PPy sensor. Its response to 50 ppm NO2 was 38% at 150 °C, i.e., 26% higher than that of the pure PPy sensor, and its selectivity and stability were also higher. The greater sensitivity was attributed to p-n heterojunction formation after MoS2 doping and more gas adsorption sites. Thus, PPy/MoS2 composite film sensors have good application prospects.

Probabilistic analysis of geological and structural Prospects for Oil and gas Potential in Western Yakutia Using three-Dimensional Empirical Mode Decomposition of Potential Fields

Further development of modern formal approaches to extracting geo-structural information from gravimetric and magnetometric data is herein discussed. A practical example of using 3D GEMD (three-dimensional controlled empirical mode decomposition) algorithm to calculate potential fields transforms associated with the impact of deep and medium (of different ranks) geological structures on the adjacent territories of Western Yakutia was considered. Two types of reference groups were determined based on analysis of the known hydrocarbon fields in the Lena-Vilyuy and Lena-Tunguska oil and gas provinces. Statistical efficiency of the observed fields, fields’ area transforms (modified functions of empirical decomposition), and also spatially-linked combinations of the said attributes, aiming to solve the forecasting tasks, was determined. As a result of integration within the testing area, layout diagrams of deep geological and structural prerequisites for oil and gas potential, similar to group standards, were drawn up. A geological interpretation of forecasting and geophysical constructions is presented. It is shown that a number of identified areas predicted with high probabilities correlate with depressions of rift zones. Combined with thick volcanogenic-sedimentary formations, the latter forms most prospective oil and gas content pre-requisites.

LAS-on-edge: A real-time laser absorption spectroscopic water vapor sensor on edge computing platforms

Real-time and in situ water vapor (H2O) sensors are increasingly demanded to indicate thermochemical parameters of energy systems. As an accurate, fast and non-intrusive sensing technology, laser absorption spectroscopy (LAS) enables simultaneous measurement of gas concentration and temperature. Due to the complex spectral computation, real-time and rapid LAS signal processing are still challenging. Most state-of-the-art LAS sensors utilize high-level computing units to post-process laser measurements in real time, hindering their portable deployment in harsh industrial environments. To address the above challenge, we developed an online LAS gas sensor on edging computing platforms, named as LAS-on-Edge. The developed sensor achieves an online monitoring rate up to 62.5 Hz by deploying a spectral-informed neural network on an embedded device. Simultaneously, it reserves the raw kilo-Hz measurements offline for underlying dynamics understanding. The LAS-on-Edge sensor’s compactness also enables its flexible and in situ integration. The sensor is firstly used to monitor a slow mist diffusion and accumulation process, exhibiting a high measurement accuracy with low spectra recovery residuals below 0.01. It is further applied to online monitor the H2O concentration and temperature variation during a lab-scale propane combustion process. It demonstrates a minimal temperature deviation of 1.86 % compared to the thermocouple reference in the flame. The sensor also successfully detects the fundamental flame pulsation at 5.65 Hz as well as high-frequency pulsations exceeding 200 Hz, thereby effectively reveals underlying flame dynamic behaviors.

The effect of UV365/Fenton process on the removal of gaseous ethylbenzene in a bubble column reactor

ABSTRACT As volatile organic compounds (VOCs), gaseous ethylbenzene has adverse effects on human health and ecology. Therefore, an effective degradation process is highly desirable. The Fenton process under UV 365 nm was selected as the first option to remove gaseous ethylbenzene in a bubble column reactor. The main parameters for the batch experiments were systematically studied, including H2O2 concentration, [H2O2]/[Fe2+], pH, UV wavelength, UV intensity, gaseous ethylbenzene concentration, gas flow rate, and process stability towards removal efficiency. The optimum conditions were found to be H2O2 concentration of 100 mmol·L−1, [H2O2]/[Fe2+] of 4, pH of 3.0, UV wavelength of 365 nm, UV power of 5 W, gas flow rate of 900 mL·min−1, and gaseous ethylbenzene concentration of 30 ppm, resulting in a removal efficiency of 76.3%. The study found that the Fenton process, when coupled with UV 365 nm, was highly effective in removing gaseous ethylbenzene. The degradation mechanism of gaseous ethylbenzene was proposed in the UV365/Fenton process based on EPR, radical quenching experiments, iron analysis, carbon balance, and GC-MS analysis. The results indicated that •OH played a crucial role in the process.

Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements

Abstract. The increase in greenhouse gas concentrations, particularly CO2, has significant implications for global climate patterns and various aspects of human life. Spaceborne remote sensing satellites play a crucial role in high-resolution monitoring of atmospheric CO2. However, the next generation of greenhouse gas monitoring satellites is expected to face challenges, particularly in terms of computational efficiency in atmospheric CO2 retrieval and analysis. To address these challenges, this study focuses on improving the speed of retrieving the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using spectral data from the Orbiting Carbon Observatory-2 (OCO-2) satellite while still maintaining retrieval accuracy. A novel approach based on neural network (NN) models is proposed to tackle the nonlinear inversion problems associated with XCO2 retrievals. The study employs a data-driven supervised learning method and explores two distinct training strategies. Firstly, training is conducted using experimental data obtained from the inversion of the operational optimization model, which is released as the OCO-2 satellite products. Secondly, training is performed using a simulated dataset generated by an accurate forward calculation model. The inversion performance and prediction performance of the machine learning model for XCO2 are compared, analyzed, and discussed for the observed region over east Asia. The results demonstrate that the model trained on simulated data accurately predicts XCO2 in the target area. Furthermore, when compared to OCO-2 satellite product data, the developed XCO2 retrieval model not only achieves rapid predictions (<1 ms) with good accuracy (1.8 ppm or approximately 0.45 %) but also effectively captures sudden increases in XCO2 plumes near industrial emission sources. The accuracy of the machine learning model retrieval results is validated against reliable data from Total Carbon Column Observing Network (TCCON) sites, demonstrating its ability to effectively capture CO2 seasonal variations and annual growth trends.

TECHNOLOGICAL COMPLICATIONS (FORMATION DAMAGE) DURING CO2 INJECTION FOR ENHANCED OIL RECOVERY. Part 1. GEOCHEMICAL PROCESSES INVOLVING ROCK MINERALS, CHANGE OF PETROPHYSICAL AND GEOMECHANICAL ROCK PROPERTIES

Decrease in permeability, porosity, change in wettability of oil-bearing formations during field development are defined by a general term - formation damage, which occur at various stages of field development, including drilling, production, application of enhanced oil recovery methods, well stimulation technologies with acid compositions, hydraulic fracturing and well workover. Reservoir damage leads to a reduction in the oil and gas content of reservoirs and in the economic efficiency of operation. In this regard, it is important to understand and prevent formation damage based on experimental and analytical methods, the applicationof which will minimize damage to the oil and gas bearing reservoir. The review considers anthropogenic processes that cause damage to oil and gas reservoir when CO2 is injected by various methods to enhance oil recovery. The processes of interaction of CO2 with formation rock are considered in detail. The main geochemical transformations of rock minerals are given, which lead to changes in the structure of pore space, permeability and porosity, decrease of geomechanical properties. Secondary sedimentation processes and risks of rock particles removal into the borehole are discussed.

System for continuous metabolic monitoring of mechanically ventilated patients.

In clinical settings, due largely to the cost, size and calibration complexity of existing indirect calorimetry systems, there is seldom instrumentation available to provide reliable, continuous tracking of a mechanically ventilated patient's metabolic output in support of proper nutrition. The atypical metabolisms associated with critically ill patients are difficult to predict and both underfeeding and overfeeding lead to negative impacts on both mortality and the recovery and healing processes. With these issues in mind, a novel ventilator-agnostic indirect calorimetry sensor design, prototype development, and validation were undertaken with the goal of enabling 24/7 metabolic monitoring of mechanically ventilated patients by means of a passive, rate-proportional side-stream sampling scheme and miniature mixing chamber. The miniature mixing chamber enables the use of small, low-cost gas concentration and flow sensing components to ensure the affordability of commercial design-for-manufacture implementations of the prototype sensor. In addition to continuous measurement of patient metabolism, the prototype sensor also enables autonomous monitoring and detection of calibration drift in the gas measurement sensors without disrupting the patient ventilation.

Impacts of Climate change on soil microbial diversity, distribution and abundance

Climate change, driven by anthropogenic activities, has far-reaching consequences for our planet. Among its many impacts, changes in temperature, elevated carbon dioxide levels, and shifts in greenhouse gas concentrations significantly affect soil ecosystems. In particular, soil microbial communities play a pivotal role in nutrient cycling, organic matter decomposition, and overall soil health. Soil microbial communities respond differently to the effects of climate change, like elevated warming and precipitation. The change in climatic conditions is reported to be adversely affecting soil biological activity directly through either drying or wetting of soil or affecting their associated plants. This review delves into the intricate relationship between climate change and soil microbial abundance, diversity, and distribution. The paper also discusses climatic change pressure on soil enzymatic activity and microbial biomasses, as well as soil faunal activity, as they are key indicators of soil health in a changing climate. Soil microbial communities cope with climate change by changing their diversity and physiological characteristics and by changing their symbiotic plants, which indicates the role of soil microbes in withstanding the negative impact of climate change.

The effect of natural waste filter on dimethyl ether production from low calorific coal gasification

In Indonesia, rapid population growth has increased energy demand, especially in the transportation and power generation sectors. The Indonesian government has been active in promoting the use of renewable energy, but the transition from fossil energy is still faced with various challenges, including limited technology and adequate infrastructure. Apart from that, Indonesia's large potential in coal is the main factor slowing down this energy transition. One promising alternative is to convert coal into Dimethyl Ether (DME) through the gasification process, which is a thermochemical process for converting solid fuel into gas, producing syngas containing H2, CO, and CH4. Synthesis purification is essential to produce high-purity DME. This process involves the use of filter materials such as zeolite and rice husk charcoal to remove contaminants such as sulfur. Zeolite, with its chemical absorption properties and regular pore structure, is effective in absorbing H2S from syngas. Meanwhile, rice husk charcoal, which is an agricultural waste rich in silica and has a porous structure, has shown potential as an absorbent material to reduce H2S content in gas. In research on the use of various filter material compositions in the up-draft type coal gasification process with CuO–ZnO–Al2O3 and ZSM-5 catalysts, it was found that a 3:7 ratio for zeolite and rice husk charcoal provided optimal performance. This ratio not only increases CH4 and CO levels in syngas, but is also effective in reducing H2S content. The research results showed that the highest DME production reached 90.10% at a ratio of 3:7, while at a ratio of 2:8 and 5:5, production was 81.61% and 73.64% respectively. These findings emphasize the importance of using diverse filter materials in improving syngas quality and DME synthesis efficiency, strengthening steps to accelerate the energy transition towards more sustainable solutions in Indonesia.

Progressive prediction algorithm by multi-interval data sampling in multi-task learning for real-time gas identification

Effective surveillance of harmful gases is paramount in the safeguard of human health and the protection of environmental air quality, thereby emphasizing the imperative for robust detection strategies, such as electronic noses. While the integration of intelligent algorithms has significantly improved the detection capabilities of electronic noses, most existing models focus on enhancing accuracy but usually ignore the crucial need for detection speed. To address this issue, the progressive prediction algorithm (PPA) is proposed to accomplish real-time gas recognition. The implementation of PPA incorporates time correction and multi-interval data sampling for gas analysis. Leveraging multi-task networks, this method proposes a novel network architecture that predicts gas type and concentration. The architecture synergizes the flexible receptive field size of time convolutional networks (TCN) with the long-term temporal dependency capture of gated recurrent units (GRU) to optimize overall model performance. The PPA achieves a classification accuracy of 99.3 % and a regression R2 score of 0.927 within half of the response time. For practical applications, this work enables the prediction of gas type and concentration during the early stages of sensor response, facilitating rapid detection of hazardous gases.

Performance Improvement of NiO/YSZ Sensitive Electrode for High Temperature Electrochemical NOx Gas Sensors

In the mixed-potential gas sensor, the electrode type, particle size, microstructural morphology, as well as the interface state between the oxide-sensitive electrode and the solid electrolyte, are the key factors for determining the magnitude of the mixed potential. NiO-based material with the addition of Y2O3-stabilized ZrO2 (YSZ) particles is considered as one of the promising sensitive electrode materials for preparing the mixed-potential NOx sensors due to its excellent gas sensitivity. In this paper, the preparation technology of NiO-sensitive electrodes is developed and optimized for improving NOx (NO and NO2) gas-sensitivity properties of the NOx sensors. The experimental results show that the addition of YSZ can effectively improve the agglomeration morphology of NiO particles in the NOx-YSZ-based electrode material during high temperature sintering (i.e. 1200 °C) and obtain improved bonding strength with the solid electrolyte. The sensor which was prepared by using the developed NOx-YSZ-based electrode material with the YSZ addition of 20 wt.% has the most sensitive response characteristic to NO gas, and its response potential value increases with NO gas concentration. Elevating electrode sintering temperature can increase the size of NiO particles and shorten the length of the three-phase boundary (TPB) in the sensitive electrode. The average length of TPB is the longest in the sensitive electrode sintered at 1000 °C which corresponds to the best sensitive response to NO gas. The sensitive electrode’s thickness also affects the NO response sensitivity, and the sensor with an electrode thickness of 14 μm has the most NO response sensitivity. In addition, the NO/NO2 gas mixture test results indicate that the current developed sensor is more sensitive to NO2 gas than that of NO gas.

Transformer oil electrical–thermal characteristics analysis and evaluation

Transformer oil plays the role of heat dissipation and insulation in power transformers, and its electrical and thermal characteristics are directly related to the stable operation of the transformer. At the same time, it is important to master the electric–thermal characteristics of transformer oil for transformer state evaluation, latent fault prediction, and maintenance planning. Based on this, the gas production rule and electrical characteristics of transformer oil under accelerated aging are studied in this paper. First, the experimental platform for power transformer aging is established to accelerate the aging of power transformers. Second, the accelerated aging transformer oil is taken as the research object, and the power frequency, DC, lightning, and gas chromatography are measured and analyzed. Finally, a transformer oil aging evaluation model is established, which takes into account four factors: carbon and oxygen gas factors in transformers, life loss factors calculated by hotspot temperature, hydrocarbon gas factors dissolved in transformer oil, and oil quality factors, and conducts aging evaluation. The conclusion shows that the T parameter proposed in this paper can effectively evaluate the quality of transformer oil. When T is less than or equal to 0.6, there is no aging of the oil. When T is greater than 0.6 and less than 1, the transformer oil has been aging, but it is not manifested. Those are parameters whose chromatography and breakdown voltage are not far beyond the IEEE standard. When T = 1, the aging of oil with significant changes in gas content and breakdown voltage can be observed. When T is greater than 1, the oil has undergone serious aging.