Search Results (29)

This research investigates the use of a fiber optic sensor for detecting hydrogen gas during a thermal runaway of lithium-ion batteries (LIBs). Timely detection of thermal runaway in LIBs, particularly in storage and logistics, is crucial for effective safety management and preventing the escalation of incidents to adjacent cells. The sensors employed in this study utilize fiber Bragg grating (FBG) technology. The FBG sensors are coated with palladium nanoparticles, enabling the detection of hydrogen concentrations up to 5%. In abuse tests, the sensors successfully identified hydrogen emissions. Cross-sensitivity effects were observed during a secondary test and were thoroughly investigated. These interferences were found to be primarily caused by carbon monoxide (CO), a common byproduct of battery venting. While the presence of CO can interfere with hydrogen detection, both signals remain independently valuable as indicators of cell malfunction. This dual-response behavior enhances the robustness of fault detection under real-world battery failure scenarios. Full article

The VIIRS Nightfire (VNF) data product is particularly useful for monitoring of global natural gas flaring and estimation of flared gas volumes. Advantages of VIIRS include the collection of nightly global coverage with the inclusion of four daytime channels in the near and shortwave infrared that cover the wavelengths of peak radiant emissions from flares. VNF calculates flare temperatures, source areas, and radiant heat using physical laws. For more than a decade, the Earth Observation Group has estimated flared gas volumes based on radiant heat with a calibration based on reported annual flared and vented natural gas volumes from Cedigaz. The calibration was tuned with an exponent of 0.7 placed on the VNF source areas to achieve the highest regression correlation coefficient. The Cedigaz calibration has wide error bars attributed to unresolvable reporting errors in the Cedigaz data. In this paper we report on the development of an empirical calibration for estimating flared gas volumes based on VIIRS observations of flares running at low, medium, and high flared gas volumes. Tests were run with both single and double flares, with and without atmospheric correction. The new calibrations were applied to VIIRS detection profiles for metered flares located in the North Sea, Arabian Peninsula, and Gulf of Mexico. The results indicate the following: (1) the exponent is unnecessary and causes flared gas volumes to be overestimated for small flares and underestimated for large flares, (2) the calibration can be applied to sites having either single or multiple flares, and (3) flared gas volume estimates can be improved by applying an atmospheric correction to account for regional difference in band-specific transmissivity levels. The new calibration has a prediction interval (error bars) seventy times smaller than the Cedigaz calibration. Full article

Efficient oil transportation in field-deployed mobile pipelines is critical, but mixed-oil zones at interfaces reduce quality and increase waste, necessitating effective interface detection and cutting. Existing online densitometers, such as vibrating tube or high-accuracy magnetic suspension types, typically require external power, limiting their use in remote or emergency/temporary field operations. A self-powered device is presented that leverages gravitational force variations acting on a float to detect density changes and trigger automatic cutting. Validated with gasoline, diesel, kerosene, and water, it achieves a 10 kg/${\mathrm{m}}^3$ resolution, deemed sufficient for functional batch separation in its target application, with switching times of 61–395 s for density differences (760–835 kg/${\mathrm{m}}^3$). It supports 20–90% blending ratios, with a vent mitigating gas effects. The modular, robust, self-powered design suits emergency operations, offering a practical alternative to powered systems. Future work targets improved resolution and environmental testing. Full article

Lithium-ion batteries have been increasingly demonstrated in reuse applications for environmental and economic reasons, and stationary energy storage systems (ESS) and mobile ESS are emerging as reuse applications for electric vehicle batteries. Most mobile ESS deployments are at large scales, necessitating experimental data on thermal runaway (TR) to ensure comprehensive safety. In this study, TR induction and suppression experiments were conducted using fully charged NCM-based batteries at the cell (750 Wh), module (7.5 kWh), and pack (74 kWh) levels. The stepwise TR experiments measured changes in temperature, voltage, heat release rate, volatile organic compound concentrations, and vent gas composition. The suppression experiments assessed the effective water injection rate, timing, and volume required to mitigate TR propagation. The results demonstrate that in the case of TR caused by thermal abuse, early detection of battery abnormalities is possible through monitoring pre-TR indicators, such as temperature and vent gas concentration. It was also confirmed that CO₂ injections can effectively cool the battery without causing damage. Furthermore, it is proposed that rapid water injection, directly contacting the battery immediately after the onset of TR, can successfully prevent TR propagation. Full article

In coal mining operations, methane explosions constitute a severe safety risk, endangering miners’ lives and causing substantial economic losses, which, in turn, weaken the production efficiency and economic benefits of the mining industry and hinder the sustainable development of the industry. To address this challenge, this article explores the application of decoupling network-based methods in methane explosion simulation, aiming to optimize underground mine ventilation system design through scientific means and enhance safety protection for miners. We used the one-dimensional finite difference method (FDM) software Flowmaster to simulate the propagation process of shock waves from a gas explosion source in complex underground tunnel networks, covering a wide range of scenarios from laboratory-scale parallel network samples to full-scale experimental mine settings. During the simulation, we traced the pressure loss in the propagation of the shock wave in detail, taking into account the effects of pipeline friction, shock losses caused by bends and obstacles, T-joint branching connections, and cross-sectional changes. The results of these two case studies were presented, leading to the following insights: (1) geometric variations within airway networks exert a relatively minor influence on overpressure; (2) the positioning of the vent positively contributes to attenuation effects; (3) rarefaction waves propagate over greater distances than compression waves; and (4) oscillatory phenomena were detected in the conduits connecting to the surface. This research introduces a computationally efficient method for predicting methane explosions in complex underground ventilation networks, offering reasonable engineering accuracy. These research results provide valuable references for the safe design of underground mine ventilation systems, which can help to create a safer and more efficient mining environment and effectively protect the lives of miners. Full article

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. With the continuous improvement of information technology and the rapid advancement of artificial intelligence (AI), the research on leakage detection technology based on AI methods has attracted more and more attention. This paper discusses the application scenarios of an AI agent based on the recently emerged large language model (LLM) technology in oil and gas production leakage detection: (1) Compared with the traditional leakage detection methods, this paper innovatively employs a combination of AI-based diagnostics and infrared temperature measurement technologies to develop a specialized small model for oil and gas leakage detection, which has been proven to significantly improve the accuracy of detecting industrial venting events in natural gas valve chambers; (2) By employing retrieval-augmented generation (RAG) technology, a knowledge vector library is constructed, utilizing a series of leakage-related documents, assisting the LLM to carry out knowledge questioning and inference. Compared with the traditional SimBERT, the accuracy can be improved by about 15% in the Q&A search ability test. The correct rate is about 70% higher than the SimBERT in the Chinese complex reasoning quiz. Also, it can still remain stable under high load conditions, with the interruption rate of retrieval closing to zero. (3) By combining the specialized small model and the knowledge Q&A tool, the natural gas valve chambers’ leakage detection AI agent based on the open-source LLM model was designed and developed, which preliminarily achieved the leakage detection based on the specialized small model, and the automatic processing of the retrieval reasoning process based on the knowledge Q&A tool and the intelligent generation of corresponding leakage disposal scheme. The effectiveness of the method has been verified by actual project data. This article conducts preliminary explorations into the in-depth applications of AI agents based on LLMs in the oil and gas energy industry, demonstrating certain positive outcomes. Full article

This study proposes a probabilistic quantification technique that applies an expert inference method to warn of the risk of a fire developing into a thermal runaway when a lithium-ion battery fire occurs. Existing methods have the shortcomings of low prediction accuracy and delayed responses because they determine a fire only by detecting the temperature rise and smoke in a lithium-ion battery to initiate extinguishing activities. To overcome such shortcomings, this study proposes a method to probabilistically calculate the risk of thermal runaway in advance by detecting the amount of off-gases generated in the venting stage before thermal runaway begins. This method has the advantage of quantifying the probability of a fire in advance by applying an expert inference method based on a combination of off-gas amounts, while maintaining high reliability even when the sensor fails. To verify the validity of the risk probability design, problems with the temperature and off-gas increase/decrease data were derived under four SOC conditions in actual lithium-ion batteries. Through the foregoing, it was confirmed that the risk probability can be accurately presented even in situations where the detection sensor malfunctions by applying an expert inference method to calculate the risk probability complexly. Additionally, it was confirmed that the proposed method is a method that can lead to quicker responses to thermal runaway fires. Full article

High-definition bathymetry mapping, combined with the measurement of dissolved benthic fluxes and water column biogeochemical properties, allows for a description of new biogeochemical processes around the Panarea Volcanic island. Investigations focused on the CO₂ releases from the bottom sea on the east of the Panarea volcanic complex provided insights into the geological setup of the marine area east and south of the Panarea Island. Between the Panarea Island and the Basiluzzo Islet lies a SW-NE-stretching graben structure where a central depression, the Smoking Land Valley, is bounded by extensional faults. Abundant acidic fluids rich in dissolved inorganic Carbon are released on the edges of the graben, along the extensional faults, either diffusely from the seafloor, from hydrothermal chimneys, or at the center of craters of different sizes. The precipitation of iron dissolved in the acidic fluids forms Fe-oxyhydroxides bottom sea crusts that act as a plug, thus preventing the release of the underlying gases until their mounting pressure generates a bursting release. This process is cyclic and results in intermittent gas release from the bottom, leaving extinct craters and quiescent chimneys. The measurement of dissolved benthic fluxes allowed us to estimate the volcanic DIC venting at 15 Mt of CO₂ over the past 10,000 years. The fluxes are not distributed homogeneously but rather concentrate along fractures and fault planes, which facilitate their rise to the seafloor. The acidic fluids released affect the chemical properties and structure of the water column through the formation of layers with a lower pH under the pycnocline, which can limit volcanic CO₂ release to the atmosphere. Further and continuous monitoring and investigation of the area are needed in order to complete a thorough picture of the variations in fluid releases through time and space. The importance of such monitoring lies in the development of a new method for detecting and quantifying the diffusive dissolved benthic fluxes on a volcanic sea bottom affected by hydrothermal seeps. Full article

The key area of the Arctic Ocean for atmospheric venting of CH₄ is the East Siberian Arctic Shelf (ESAS). Leakage of methane through shallow ESAS waters needs to be considered in interactions between the biogeosphere and a warming Arctic climate. The development of remote sensing techniques for gas seepage detection and mapping is crucially needed for further applications in the ESAS and other areas of interest. Given the extent of the seepage areas and the magnitude of current and potential future emissions, new approaches are required to effectively, rapidly, and quantitatively survey the large seepage areas. Here, we consider the main features of gas seep detection on the sea surface in the characteristics of wind waves and radar signals. The kinematics of wave packets based on the kinetic equation for the spectral density of the wave action of surface waves is described. The results of a full-scale experiment on the remote radar observation of a model gas seep to the sea surface in the radar equipment signals are considered. The characteristic radar signatures of the gas seep in a wide range of hydrometeorological conditions, the parameters of which were recorded synchronously with the radar mapping, were determined. The results of the first radar observations of natural methane seeps on the ESAS are presented, and their radar contrasts are evaluated. The theoretical conclusions are in good qualitative agreement with the results of the model experiment and field studies and can be used for further research in aquatic areas with potential gas seepage, both of natural or anthropogenic origin, such as bubbling release from broken underwater gas pipelines. Full article

Carbon dioxide (CO₂) is a known greenhouse gas and one of the largest contributors to global warming in the Earth’s atmosphere. The remote detection and measurement of CO₂ from industrial emissions are not routinely carried out and are typically calculated from the fuel combusted or measured directly within ducted vents. However, these methods are not applicable for the quantification of fugitive emissions of CO₂. This work presents the results of remote measurement of CO₂ emissions using the differential absorption lidar (DIAL) technique at a wavelength of ~2 µm. The results from the DIAL measurements compare well with simultaneous in-stack measurements, these datasets were plotted against each other and can be described by a linear regression of y (t/h) = 1.04 x − 0.02, suggesting any bias in the DIAL data is likely small. Moreover, using the definition outlined in EN 15267-3 a lower detection limit of 0.12 t/h was estimated for the 2 µm wavelength DIAL data, this is three orders of magnitude lower than the corresponding CO₂ detection limit measured by NPL in the 1.5 µm wavelength region. Thus, this paper demonstrates the feasibility of high-resolution, ground-based DIAL measurements for quantifying industrial CO₂ emissions. Full article

This paper proposes to apply a newly developed Non-Dispersive Infrared Spectroscopy (NDIR) gas sensing system composed of pyroelectric infrared detectors to monitor the thermal runaway (TR) process of lithium-ion batteries in real time and achieve an early warning system for the battery TR process. The new Electrical Vehicle Safety—Global Technical Regulation (EVS-GTR) requires that a warning be provided to passengers at least five minutes before a serious incident. The experimental results indicate that carbon dioxide and methane gas were detected during the overcharge test of the automotive battery, and the target gas was detected 25 s in advance before the battery TR when the battery vent was closed. In order to further explore the battery TR mechanism, an experiment was carried out using the battery sample with the battery vent opened. The target gas was detected about 580 s before the battery temperature reached the common alarm temperature (60 °C) of the battery management system (BMS). In this study, the beneficial effects of NDIR gas sensors in the field of thermal runaway warnings for automotive batteries were demonstrated and showed great application prospects and commercial value. Full article

Dry dust collectors are a typical dust and gas coexistence space. Dust explosion risk assessments should be performed for effective prevention and control of dust explosion accidents. In this paper, a dust explosion risk assessment index system for dust removal systems was constructed following the dust characteristics and the actual operation of a dry dust collector. The proposed system consisted of three first-level indexes (dust explosion characteristic parameters, environmental parameters in the dust collector box, use state of explosion prevention and control device) and seven second-level indexes (dust explosion sensitivity, dust explosion severity, temperature in the dust collector box, pressure difference between inlet and outlet, operating state of spark detection, operating the explosion venting disc, and operating state of the lock gas ash discharge valve). The analytic hierarchy process was adapted to calculate the weight of each index. Additionally, a dust explosion risk assessment model for the dust removal system was constructed using the fuzzy comprehensive evaluation method to form a set of dust explosion risk assessment methods suitable for dry dust collectors. The risk of explosion was assessed at level II through the use of paper powder with a particle size of 75 $\mathsf{\mu }$m, which means this method is reliable. Full article

Accurately quantifying unsteady methane venting from key oil and gas sector sources such as storage tanks and well casing vents is a critical challenge. Recently, we presented an optical sensor to meet this need that combines volume fraction and Doppler shift measurements using wavelength modulation spectroscopy with 2f harmonic detection to quantify mass flux of methane through a vent line. This paper extends the previous effort through a methodical component-by-component investigation of potential sources of thermally-induced measurement drift to guide the design of an updated sensor. Test data were analyzed using an innovative signal processing technique that permitted quantification of background wavelength modulation spectroscopy signal drift linked to specific components, and the results were successfully used to design a drift-resistant sensor. In the updated sensor, background signal strength was reduced, and stability improved, such that the empirical methane-fraction dependent velocity correction necessary in the original sensor was no longer required. The revised sensor improves previously reported measurement uncertainties on flow velocity from 0.15 to 0.10 m/s, while markedly reducing thermally-induced velocity drift from 0.44 m/s/K to 0.015 m/s/K. In the most general and challenging application, where both flow velocity and methane fraction are independently varying, the updated design reduces the methane mass flow rate uncertainty by more than a factor of six, from ±2.55 kg/h to ±0.40 kg/h. This new design also maintains the intrinsic safety of the original sensor and is ideally suited for unsteady methane vent measurements within hazardous locations typical of oil and gas facilities. Full article

An optical sensor employing tunable diode laser absorption spectroscopy with wavelength modulation and 2f harmonic detection was designed, prototyped, and tested for applications in quantifying methane emissions from vent sources in the oil and gas sector. The methane absorption line at 6026.23 cm^–1 (1659.41 nm) was used to measure both flow velocity and methane volume fraction, enabling direct measurement of the methane emission rate. Two configurations of the sensor were designed, tested, and compared; the first used a fully fiber-coupled cell with multimode fibers to re-collimate the laser beams, while the second used directly irradiated photodetectors protected by Zener barriers. Importantly, both configurations were designed to enable measurements within regulated Class I / Zone 0 hazardous locations, in which explosive gases are expected during normal operations. Controlled flows with methane volume fractions of 0 to 100% and a velocity range of 0 to 4 m/s were used to characterize sensor performance at a 1 Hz sampling rate. The measurement error in the methane volume fraction was less than 10,000 ppm (1%) across the studied range for both configurations. The short-term velocity measurement error with pure methane was <0.3 m/s with a standard deviation of 0.14 m/s for the fiber-coupled configuration and <0.15 m/s with a standard deviation of 0.07 m/s for the directly irradiated detector configuration. However, modal noise in the multimode fibers of the first configuration contributed to an unstable performance that was highly sensitive to mechanical disturbances. The second configuration showed good potential for an industrial sensor, successfully quantifying methane flow rates up to 11 kg/h within ±2.1 kg/h at 95% confidence over a range of methane fractions from 25–100%, and as low as ±0.85 kg/h in scenarios where the source methane fraction is initially unknown within this range and otherwise invariant. Full article

This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA. Full article