Gas Leakage Research Articles

Density function theory investigation of bimetallic phthalocyanine as a potential sensor and scavenger for nitrogen-containing toxic gases

Urbanization and industrialization processes inevitably release a large amount of nitrogen-containing toxic gases (NTGs) such as NO, NO2, NH3 and HCN, posing a serious threat to the ecological environment. Therefore, it is necessary to develop new gas sensors to promptly detect the emission and leakage of these toxic gases. Herein, four bimetallic phthalocyanines (BMPc = FePdPc, FePtPc, MnPdPc and MnPtPc) were designed by introducing pairs of precious metal (Pd/Pt) and inexpensive metal (Fe/Mn) atoms into the phthalocyanine (Pc) monolayer. Subsequently, the adsorption behavior and sensing properties of these BMPc towards NTGs were investigated using density functional theory (DFT) method. The studied results show that the four BMPc exhibit excellent stability and display chemical adsorption for these target gases due to the synergistic effect of bimetallic active centers, with adsorption energy greater than −0.70 eV. The adsorption strength follows the order NO > NO2 > NH3 > HCN, which is further confirmed by electronic properties such as charge density difference and density of states. Additionally, the analyses of work function, bandgap, and sensing response demonstrate the superior sensitivity of BMPc towards NO, NO2, NH3, and HCN. Meanwhile, the MnPtPc monolayer presents suitable adsorption strength for HCN at 298 K, resulting in a short recovery time of only 0.68 s. When the temperature is increased to 348 K, FePdPc and MnPdPc exhibit high reversibility for HCN, while MnPtPc displays favorable performance for NH3. Therefore, MnPtPc is identified as a recyclable HCN gas-sensing material at room temperature, while FePdPc, FePtPc, and MnPdPc are regarded as reusable gas-sensing materials for HCN at high temperature, with their sensing properties remaining unaffected in a humid environment. By contrast, the prolonged recovery times of NOx (x=1, 2) adsorption systems make these four BMPc more suitable as efficient scavengers for NOx. This study provides a new strategy for designing and developing phthalocyanine-based gas sensors and scavengers for the NTGs and other harmful substances.

Decreased Smoke Detector Performance Functionon MV. Sarah S

Ships are vessels used for transporting passengers and goods across bodies of water, such as rivers and seas, using canoes or smaller boats. Fires on ships can occur due to various reasons, including interference with electrical and electronic equipment, as well as flammable liquids like gasoline, grease, and gas leaks. This study adopts a descriptive qualitative approach, utilizing data collection methods such as direct field observation, in-depth interviews with involved parties, and documentation/library research. The research findings highlight two main points: 1) The factors leading to decreased functionality of fire alarm sensors are the lack of maintenance and repair of the system; and 2) A decreased fire alarm system may fail to detect fire or smoke in specific areas, potentially leading to accidents or severe damage to the ship.

Enhancing coal seam gas pressure measurement accuracy and preventing leakage through dual chamber pressure balancing in borehole

Accurate measurement of coal seam gas pressure (CSGP) is critical to the evaluation of coal mine gas hazards and the potential of coal seam gas extraction. In this study, we investigate gas leakage from chambers and its influences on CSGP measurement results (MR) through numerical simulations. Furthermore, we propose, analyze and validate a novel method that balances gas pressure between two chambers, i.e. the measurement and regulation chambers, in a borehole. The results demonstrate that: (1) Traditional CSGP measurement method utilizing boreholes with a single chamber exhibit a gradual expansion of the gas pressure dropping region around the borehole, accompanied by an initial increase in chamber gas pressure followed by stabilization. The gas leakage process within the chamber undergoes three typical stages. (2) The increase of gas leakage lowers the MR value and raises the CSGP measurement error ratio (R). The relationship between MR, R and the stable value of gas leakage is nearly linear. (3) The proposed novel CSGP measurement method involves continuous gas injection into the regulation chamber to establish a dynamic gas pressure balance region between the two chambers. This approach minimizes gas leakage in the measurement chamber and accelerates gas pressure recovery. Maintaining dynamic pressure equality between the dual chambers is essential for minimizing R. (4) An automatic regulation unit is developed to achieve dynamic gas pressure balancing between the two chambers. With the novel method, R values remain below 3%, yielding higher CSGP measurement accuracy compared to alternative approaches.

The failure mechanism of field polyethylene gas pipeline with gas leakage at electrofusion joint

PurposeA section of in-service PE gas pipeline in Guocun, Beijing, was found to appear gas leaking at the electrofusion (EF) joint. This study is dedicated to reveal the material cause of EF joint failure to help with a more accurate prediction of service life of PE gas pipe and further normalize the construction of PE gas pipeline.Design/methodology/approachDefect detection was carried out on the leaking EF joint using ultrasonic phased array. The mechanical degradation and structural aging behavior was studied by tension test, FTIR technology, TG test and DSC test. The organic components in the soil surrounding the PE gas pipe failure area were qualitatively identified.FindingsThe results showed that the organic surfactants in the soil environment could accelerate the aging behavior of PE material, leading to a deterioration of mechanical properties and a serious reduction in the ability of the PE pipe and EF joint, especially at the welding defect, to resist external force.Originality/valueA novel study was conducted to investigate the failure cause of the EF joint of in-service PE gas pipe, incorporating the analysis of environmental factors and structural deterioration.

Rekayasa SmartHome System Berbasis Internet of Things

In the current digital era, the Internet of Things (IoT) has emerged as a concept that presents new opportunities to expand sustainable internet connectivity. In the context of Smarthome, IoT enables the control of electronic devices through smartphone applications using internet connectivity, bringing various benefits, including increased comfort, enhanced security, better electricity savings, and introducing innovative solutions that not only allow real-time monitoring of home environments but also provide accessibility by considering the needs and preferences of various users, including families, busy individuals, and people with disabilities. The implementation of an IoT-based Smarthome system utilizes NodeMCU 8266 as a microcontroller, MIT App Inventor as a control tool, Thingspeak as a data processing platform, ESP32-CAM as a camera, and Telegram bot as a notification sender. This system includes the control of lights, fan, environmental monitoring, motion detection, gas leak detection, and fire detection. Various sensors are used, including Flame sensors for fire detection, MQ5 sensors for gas leak detection, and light sensors for automatically turning on lights. Relays are also applied to connect electronic devices to the system. Testing and system analysis show that equipment control operates according to the given commands, provided that the internet connection remains stable and continuous. This underscores the importance of a reliable network infrastructure in supporting the smooth operation of IoT-based Smarthome systems.

Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management.

The burden of natural gas leaks on public sector emergency response in the United States

Fire departments play a critical role in responding to potential hazards generated by natural gas and propane leaks, yet the costs of providing these services is unknown. Here, we analyzed 15 years of gas leak-coded incidents from the National Fire Incident Reporting System (NFIRS) and estimated associated costs using fire department operating budgets. From 2003 to 2018, a total of 2.4 million gas leak-coded incidents required fire department responses, accounting for ∼0.8% of all nationwide incidents. Reported gas leak incidents have steadily increased nationwide, nearly quadrupling from 2003 to 2018. In 2018, New York City (NYC) experienced 22,090 gas leak incidents—more than the next 25 cities combined—conservatively costing NYC $70 million annually. With a median national response cost of $2609 (S.D. $1734), uncombusted gas leaks conservatively cost U.S. fire departments $564 million in 2018—at least 10x greater than for gas leak-caused fires. City-level natural gas systems entail an underappreciated and increasing public cost burden on emergency response services. Findings herein raise the salience of gas leak-associated emergency response costs and support improved gas leak response coordination between emergency services and gas utility providers. Decision-makers are encouraged to incorporate these costs into cost-benefit analyses and consider the intended and unintended effects on emergency response systems in managing energy transitions.

A real-time early warning classification method for natural gas leakage based on random forest

Serious natural gas leakage explosion accidents have brought seriously threatening to people's lives and properties. Efficient warning classifications is of great significance to make rapid response, thus reducing the losses caused by accidents. This paper describes a novel early warning classification method for natural gas leakage based on a multi-classification random forest (RF) model, which allows evaluating the level of early warning of gas accidents timely and accurately, assisting monitoring department and gas company in timely rapid decision and scientific disposal. Fully considering the laws of natural gas leakage and the change of comprehensive risks in underground spaces adjacent to natural gas pipeline, an early warning classification index system was established, and multiple warning factors features were extracted from recorded warning events of natural gas leakage. Then the early-warning level labels of the warning events was gained by K-mean clustering and experts scoring methods. The extracted warning features and the associated early-warning level labels were used to train and validate the proposed model. The effectiveness and feasibility of this model is further verified by comparing with other popular approaches. Furthermore, the verified model is loaded into real time module, which can achieve the real time warning classification. The research results demonstrated that the proposed method can timely and accurately classify the levels of the early warning events. The prediction accuracy of the natural gas leakage early warning classification model based on the RF algorithm is 88.02 %. For real time warning events, rapid decision can be made according to the characteristics of early-warning grades, and the emergency disposal can be guided more effectively based on the warning classification results.

STUDENT MANUAL : TESTING A HYDROCARBON REFRIGERANT LEAK DETECTOR WITH ARDUINO NANO AND MQ-2 SENSOR

This research aims to address public concerns regarding the use of hydrocarbon refrigerants, which are prone to leakage and have the potential to cause explosions and fires. Therefore, a hydrocarbon refrigerant gas leak detector was developed using the MQ-2 sensor for accurate detection. This device utilizes an Arduino Nano microcontroller to detect the gas. The manufacturing method includes both hardware and software development. The results show that this device is effective in detecting leaks by measuring the PPM (parts per million) values from five sensors, producing 70 buzzer sounds in 20 minutes when 105 grams of hydrocarbon refrigerant is released, and the device tsemperature reaches 30°C. The visual indicator provides "safe" and "leak" statuses at a pressure of 130 Psi, and the light indicator remains on during testing. In conclusion, this device improves the maintenance procedures of hydrocarbon refrigerants in refrigeration systems, giving users peace of mind because leaks can be detected with sound and visual indicators every 17 seconds.

Digital twin-based modeling of natural gas leakage and dispersion in urban utility tunnels

Background Unexpected leakage accidents of the natural gas pipeline inside urban utility tunnels can pose great threats to public safety, property, and the environment. It highlights the modeling of natural gas leakage and dispersion dynamics, especially from a digital twin implementation perspective facilitating effective emergency response in a data-driven way. Methods In this study, a digital twin-based emergency response framework for gas leakage accidents in urban utility tunnels is proposed. Within this framework, the data-calibrated gas concentration prediction (DC-GCP) model is developed by integrating the Lattice Boltzmann Method (LBM) with data assimilation (DA) techniques. This combination enables accurate spatiotemporal predictions of gas concentrations, even with a prior or inaccurate gas leakage source term. Specifically, we develop a high-performance LBM-based gas concentration prediction model using the parallel programming language Taichi Lang. Based on this model, real-time integration of gas sensor data from utility tunnels is achieved through the DA algorithm. Therefore, the predicted results can be calibrated by the continuous data in the absence of complete source term information. Furthermore, a widely used twin experiment and statistical performance measures (SPMs) are used to evaluate and validate the effectiveness of the proposed approach. Results The results show that all SPMs progressively converge towards their ideal values as calibration progresses. And both the gas concentration predictions and the source term estimations can be calibrated effectively by the proposed approach, achieving a relative error of less than 5%. Conclusions This study helps for dynamic risk assessment and emergency response of natural gas leakage accidents, as well as facilitating the implementation of predictive digital twin in utility tunnels.

High-precision regression prediction of HCHO concentration based on gas sensors and FPNet

The leakage of HCHO gas has direct and irreversible damage on both human health and the environment. However, the current commercial electronic nose (E-nose) has low accuracy in predicting the concentration of HCHO. In this work, a sensor array consisting of 6 gas sensors was fabricated and combined with advanced deep learning algorithms to achieve accurate prediction of HCHO concentration. High-performance SnO2 sensing material with high specific surface area was synthesized by applying the Metal-Organic Frameworks (MOFs) sacrificial template, and the response of the SnO2 gas sensor was as high as 15.098, which is much higher than that of commonly used commercial sensors. In addition, the sensing performance of the SnO2 sensor, including response, selectivity and repeatability etc., were investigated. Afterward, a Formaldehyde Prediction Network (FPNet) model was proposed for processing the HCHO gas dataset and cosine annealing was added to gradually reduce the learning rate during training to help the model converge to the optimal solution. Furthermore, the residual structures with different number of layers were compared, and the 3-layer residual structure expressed the most accurate results of concentration prediction of the HCHO gas, with an MSE of 9.003, MAPE of 0.015, and R2 of 0.998. This study provides a new idea from sensor preparation to pattern recognition algorithm research, effectively solving the problems of the low response of commercial sensors to HCHO and the low accuracy of pattern recognition algorithms for E-nose datasets.

Gas Free Dissipation Characteristics Analysis and Safety Repair Time Determination of Buried Pipeline Leakage Based on CFD

Buried pipelines, as the most common method of natural gas transportation, are prone to pipeline leakage accidents and are difficult to detect due to their harsh and concealed environment. This paper focused on the problem regarding the free dissipation of residual gas in buried gas pipelines and soil after closing the gas supply end valve after a period of leakage by numerical simulation. A multiple non-linear regression model was established based on the least squares method and multiple regression theory, and MATLAB 2016b mathematical calculation software was used to solve the problem. The research results indicated that compared to the gas leakage diffusion stage, the pressure and velocity distribution during the free dissipation stage were significantly reduced. The increase in leakage time, pipeline pressure, leakage size, and pipeline burial depth led to a large accumulation of natural gas, which increased the concentration and distribution range of gas on the same free dissipation stage monitoring line. A prediction model for natural gas concentration in the free dissipation stage was established with an average error of 7.88%. A calculation model for the safety repair time of buried gas pipeline leakage accidents was further derived to determine the safety repair time of leakage accidents.

Under-expanded jet and diffusion characteristics for small-hole leakage of hydrogen-blended natural gas in high-pressure pipelines

Hydrogen blending in natural gas pipelines is an economical method of hydrogen transportation, but it can increase explosion risks from leakages. Current leakage models struggle to capture under-expanded jet structures near leakage holes due to limited computational resources and cannot accurately describe explosion hazardous distances of subsequent diffusion. A two-stage model for simulating the leakage of hydrogen-blended natural gas (HBNG) pipelines is introduced, comprising jet and diffusion models. The source strength determined in the jet model serves as the inlet boundary condition for the subsequent diffusion model. The results show that considering the under-expanded jet structure can more accurately reflect the pressure fluctuation changes near the leakage hole, particularly at the location of the Mach disc, xm. The pressure at the 10xm section of the jet region stabilizes at atmospheric pressure and utilizing this value as the inlet boundary for the diffusion model prevents supersonic and oscillatory regions. Subsequently, the impact of pipeline operating pressure, leakage hole diameter, and hydrogen blending ratio (HBR) on the far-field diffusion characteristics is analyzed. Notably, the leakage hole diameter has a more pronounced effect on the explosion hazardous distance compared to other factors. This research provides insights for the prevention and management of HBNG leakage accidents.

Justification of the Locations of Differential Pressure Sensors for Coarse and Fine Filter Elements Installed one Inside the Other in a Single Gas Filter Housing

Statement of the problem. The relevant task of ensuring the uninterrupted operation of modern gas pressure reduction systems is to develop and comprehensively substantiate the possibility of using two-stage filters for natural gas purification in gas reduction points and theoretically substantiate the method for calculating the pressure drop in cylindrical filter elements (CFE). Results. It is proved that when gas flows in the upper cross section of the filter, the pressure drop will be less than in lower cross sections, including the pressure drop in the lowest cross section. This leads to a larger amount of leaking gas, to a larger mass of impurities settling in the lower part of the filter elements of cylindrical coarse and fine cleaning and to a greater degree of their clogging. Therefore, it is recommended to place gas pressure sensors in the lower part of the internal volume of the coarse and fine-cleaned CFE, so that the flow direction of the coarse and finely purified gas washing the sensors at the location of the hole for measuring statistical pressure is tangential to the side surface of the sensor tube. Then the flow direction of the roughly and finely purified gas entering the holes of the sensors is carried out at an angle of 90° and the pressure gauges measure the static pressure. Conclusions. The locations of pressure drop sensors on coarse and fine filter elements installed one inside the other in a single gas filter housing are substantiated. The proposed arrangement of differential pressure sensors will significantly increase the possibility of receiving an earlier signal about the degree of clogging of the gas filter at the control room, as well as have a significant amount of time for timely removal of blockages formed in the coarse cleaning CFE and replacement of fine cleaning CFE.

Generation and emission mechanism of polycyclic aromatic hydrocarbon (PAHs) during the coking process in Shanxi, China

Although coking process is the important source of polycyclic aromatic hydrocarbons (PAHs) in the environment, the generation and emission of PAHs during this process is unclear. It is crucial to clarify the formation mechanism of PAHs in coal pyrolysis during the coking process for effectively identifying and controlling the emission of these organic pollutants. In this study, the combination of laboratory simulation and field sampling was used to analyze the mechanism of PAHs formation and emission in coking process. The release of PAHs from the pyrolysis process of coal blends used in coking plants was 1778.20 ± 111.95 μg · g−1, which was much higher than the content of free PAHs in raw coal (76.50 ± 12.46 μg · g−1). 3-ring PAHs were the most abundant components of free PAHs and pyrolysis-generated PAHs. PAH formation during pyrolysis of coal blends was primarily attributed to the cracking of the macromolecular structure of coal, with minimal influence of free PAHs in blended coal. The emission of PAHs from coal-charging was higher (62.93 ± 17.75 μg · m−3) than that from pushing of coke (11.79 ± 1.91 μg · m−3·, PC) and combustion of coke oven gas (5.53 ± 1.20 μg · m−3, CG), and was mainly related to free PAHs in coal. In contrast, the characteristics of PAHs in the flue gas of PC and CG were similar to those from blended coal pyrolysis. PAHs in fugitive emission from coke oven were primarily affected by flue gas leakage and were mainly related to coal pyrolysis and free PAHs in blended coal.

Study on the Diffusion Law of Heavy Gas Leakage in Complex Scenarios Based on Scaled-Down Experiments.

In order to study the effects of temperature, wind speed, and leakage volume on the diffusion of heavy gas leakage, this paper establishes a scaling model for the experimental study of gas leakage and diffusion by using the similarity theory with a certain factory as the target. And carbon dioxide gas is selected to replace the toxic and harmful heavy gas to carry out experiments under different temperatures (0-40 °C), wind speeds (0-2 m/s), and leakage velocities (2.5-12.5 L/min), respectively. The results showed that the diffusion rate of heavy gas expanded with increasing temperature under the conditions of wind speed of 0.25 m/s and leakage velocity of 1.5 L/min. When the temperature was increased from 0 to 40 °C, the concentration increase at each location was 125-290% at 600 s. Under the condition of temperature of 20 °C and leakage velocity of 5 L/min, the concentration at each location increased linearly with diffusion time when there was wind, while the linear relationship was not obvious when there was no wind. The effect on the concentration was larger when the wind speed was less than 1 m/s and smaller when the wind speed was greater than 1 m/s. At 20 °C and a wind speed of 0.5 m/s, the concentration of carbon dioxide at each location was increasing as the leakage increased. As the leakage velocity increases from 2.5 to 12.5 L/min, the carbon dioxide concentration at 600 s spreads 2-14 times. The research in this paper provides some decision support for the rescue work, which is important for improving the emergency rescue capability of the leakage accident.

Digital Twin Technology in the Gas Industry: A Comparative Simulation Study

Continuous innovation is essential in the urban gas industry to achieve the stability of energy supply and sustainability. The continuous increase in the global demand for energy indicates that the urban gas industry plays a crucial role in terms of stability, the economy, and the environmental friendliness of the energy supply. However, price volatility, supply chain complexity, and strengthened environmental regulations are certain challenges faced by this industry. In this study, we intend to overcome these challenges by elucidating the application of digital twin technology and by improving the performance of the prediction models in the gas industry. The real-time data and simulation-based predictions of pressure fluctuations were integrated in terms of pressure control equipment. We determined the contribution of this data integration to enhancing the operational efficiency, safety, and sustainable development in the gas industry. The summary of the results highlights the superior predictive performance of the autoregressive integrated moving average (ARIMA) model. It exhibited the best performance across all evaluation indices—mean absolute percentage error (MAPE), root mean square error (RMSE), and the coefficient of determination (R2)—when compared with the raw data. Specifically, the ARIMA model demonstrated the lowest RMSE value of 0.01575, the lowest MAPE value of 0.00609, and the highest R2 value of 0.94993 among the models evaluated. This indicates that the ARIMA model outperformed the other models in accurately predicting the outcomes. These findings validate that the integration of digital twin technology and prediction models can innovatively improve the maintenance strategy, operational efficiency, and risk prediction in the gas industry. Predictive maintenance models can help prevent significant industrial risks, such as gas leak accidents. Moreover, the integration of digital twin technology and predictive maintenance models can significantly enhance the safety and sustainability in the gas industry. The proposed innovative method of implementing digital twin technology and improved prediction models lays a theoretical foundation for sustainable development that can be applied to other industries with high energy consumption.

Enhanced Pressure Response of Edge-Deposited Graphene Nanomechanical Resonators.

Nanomechanical resonators made of suspended graphene exhibit high sensitivity to pressure changes. Nevertheless, the graphene resonator pressure performance is affected owing to the gas permeation problem between the graphene film and the substrate. Therefore, we prepared edge-deposited graphene resonators by focused ion beam (FIB) deposition of SiO2, and their gas leakage velocities and pressure-sensing ability were demonstrated. In this paper, we characterize the pressure-sensing response and gas leakage velocities of graphene membranes using an all-optical actuation system. The gas leakage velocities of graphene resonators with diameters of 10, 20, and 40 μm are reduced by 5.0 × 106, 2.0 × 107, and 8.1 × 107 atoms/s, respectively, which demonstrates that the edge deposition structure can reduce the gas leakage of the resonator. Furthermore, the pressure-sensing performance of three graphene resonators with different diameters was evaluated, and their average pressure sensitivities were calculated to be 3.4, 2.4, and 1.9 kHz/kPa, with the largest full-range hysteresis errors of 0.6, 0.7, and 1.0%, respectively. The temperature stabilities of the three sizes of resonators in the temperature range of 300-400 K are 0.016, 0.015, and 0.016%/K, and the maximum resonance frequency drift over 1 h is 0.0058, 0.0048, and 0.0112%, respectively. This work has great significance for the improvement of gas leakage velocity characterization of graphene membrane and graphene resonant pressure sensor performance optimization.

Estimation of the Hazardous Chemical Leakage Scale Inside Buildings Using CFD

Increased industrialization and aging infrastructure have resulted in leaks of hazardous chemicals, such as CO. Leak modeling is crucial to developing emergency response strategies. Therefore, we simulated the time to criticality (TTC), which is the time to reach the threshold limit for occupational exposure, of a CO leak. The basis of the study is a fire dynamics simulator, a computational fluid dynamics model that was used to investigate the movement of CO in various scenarios, including using different building layouts and areas, temperatures, and leak diameters. Multiple regression analysis was performed to obtain regression equations for the TTC as a function of the independent variables. Ultimately, we found that the type of dispersion varies with respect to the temperature-dependent density of CO, and, among the independent variables, the leak diameter had the strongest effect on the TTC. The regression equations with logarithmic conversion were validated and found to have higher accuracy than those without logarithmic conversion. The findings provide useful information for developing emergency response plans regarding leak size in the case of hazardous chemical leakage. However, empirical studies of different gas types and leakage scenarios are required.

Experimental Study to Visualize a Methane Leak of 0.25 mL/min by Direct Absorption Spectroscopy and Mid-Infrared Imaging

Tunable laser spectroscopy (TLS) with infrared (IR) imaging is a powerful tool for gas leak detection. This study focuses on direct absorption spectroscopy (DAS) that utilizes wavelength modulation to extract gas information. A tunable interband cascade laser (ICL) with an optical power of 5 mW is periodically modulated by a sawtooth injection current at 10 Hz across the methane absorption around 3271 nm. A fast and sensitive thermal imaging camera for the mid-infrared range between 3 and 5.7 µm is operated at a frame rate of 470 Hz. Offline processing of image stacks is performed using different algorithms (DAS-F, DAS-f and DAS-2f) based on the Lambert–Beer law and the HITRAN database. These algorithms analyze various features of gas absorption, such as area (F), peak (f) and second derivative (2f) of the absorbance. The methane concentration in ppm*m is determined on a pixel-by-pixel analysis without calibration. Leak localization for methane leak rates as low as 0.25 mL/min is accurately displayed in a single concentration image with pixelwise sensitivities of approximately 1 ppm*m in a laboratory environment. Concentration image sequences represent the spatiotemporal dynamics of a gas plume with high contrast. The DAS-2f concept demonstrates promising characteristics, including accuracy, precision, 1/f noise rejection, simplicity and computational efficiency, expanding the applications of DAS.