Gas Leak Detection Research Articles

A Wireless Safety System for Detecting Gas Leakage in Air Conditioner

— In this paper, we’ll keep an eye on the air conditioner’s gas leaking. Frequently, a minor gas leak can cause a gas concentration to grow up explosively over time. A small amount of explosive gas combined with fire can cause significant damage to our homes and loved ones’ lives if this gas leak is not discovered in time. By the usage of sensor helps to keeps an eye on gas leaks in the air conditioner. Gas presence in a space can be determined with a gas sensor. The microcontroller for this paper, an Arduino UNO, is utilized to process the sensor input. When a gas leak is discovered, the system alerts the user by sending an SMS through the GSM module and playing a buzzer sound.

Research on Deep learning based Ultrasonic Image Learning to Develop a Gas Leak Detection Model

If a gas leak occurs in an industrial area, identifying the location of the gas leak and predicting the scale of the accident are challenging owing to the invisible nature of the gas. In this study, we developed a deep learning-based gas leak detection model that can obtain not only the gas leak status, but also the gas leak location and flow rate information, by using technology to visualize the ultrasonic waves generated during gas leaks. Research methods are broadly categorized into data collection and model learning methods. First, data was collected using an ultrasonic camera to capture ultrasonic images at different measurement distances (1 and 3 m) and gas leak flow rates (0-8 L/min). YOLO (You Only Look Once) was used for image learning, and the model was trained after setting the class according to the gas-leak flow range. The clarity of the collected ultrasonic images decreased as the measurement distance increased. In addition, there was little difference between the images for each leakage flow rate, posing challenges in distinguishing them with the naked eye. However, the model learning results showed high accuracy, with a precision of 0.960, recall of 0.967, and mAP (IoU (Intersection over Union) 50%) of 0.987. Applying this model as a gas safety management technology at industrial sites, enables the accurate determination of gas leak status, gas leak location, and gas leakage flow. This information is expected to guide appropriate accident responses for workers.

Comparative Analysis of Ultrasonic and Traditional Gas-Leak Detection Systems in the Process Industries: A Monte Carlo Approach

Gas leaks can cause disasters at process sites, including fires and explosions, and thus, effective gas-leak detection systems are required. This study investigated the limitations of conventional detectors and introduced an innovative ultrasonic sensor-based approach for continuous monitoring. A new configuration for a stationary remote ultrasonic gas-leak monitoring system is proposed. The selected material was 1-Butene. The detection probability was assessed through a simulation based on a gas-leak scenario, detailing the selection criteria for leak sites and simulation conditions. Computational fluid-dynamics (CFD) simulations were used to evaluate the detection capability of the existing system, whereas Monte Carlo simulations were used to compare it with the proposed ultrasonic system. The CFD simulation was performed by setting the lower detection limit of the concentration-measurement-type gas detector to 600 ppm, and the leak-detection time was approximately 8.895 s. A Monte Carlo simulation accounting for wind direction showed that the existing system’s average detection probability was 3.41%. By integrating the existing and new systems, the detection probability increased by 96.58% and 3.92% compared with that using only the concentration-measurement and ultrasonic measurement methods, respectively. These rates provide valuable insights for future applications in oil refining and petrochemical industries.

Perancangan Alat Sensor Pendeteksi Kebocoran Gas LPG dan Api dengan Menggunakan MQ-2 Dan Flame Sensor

LPG gas leaks can become a serious threat to human safety and health, especially if they are not detected quickly and accurately in the main control system for this LPG gas leak detection system and even if the worst possibilities occur, such as a spark which could result in a light fire. This research uses the Prototype method. Another goal is to minimize the risk of accidents when using LPG gas stoves, and reduce the risk of damage and material losses arising from gas leaks and fires caused by LPG gas stoves. Design and manufacture of hardware and software by developing the MQ-2 sensor detection algorithm which is used to detect gas leaks and Flame sensors for fire detection. Testing is carried out by simulating various test scenarios from the use of gas sensors and fire sensors. The test results show that this system is capable of detecting detected gas and fire. Tests are carried out alternately between gas sensors and fire sensors. The output on the system is in the form of SMS notifications, Bazzer and LED lights. SMS notification using SIM800L. When a fire is detected there will be an SMS notification "API DETECTED!!!", the bazzer will sound and the red LED will light up. When gas is detected there will be an SMS notification "GAS DETECTED!!!!" and the yellow LED will light up, the maximum limit if gas is detected is 10cm and the maximum limit if fire is detected is 30 cm. In conclusion, the tool is made using the Arduino Nano microcontroller, by making a cable design on the PCB board, after which the soldering process or connecting the modules is carried out.

A lightweight network based on local–global feature fusion for real-time industrial invisible gas detection with infrared thermography

The detection of industrial invisible gas plays a vital role in preventing environmental pollution and fire accidents. Optical gas imaging (OGI) with infrared thermography is widely used in the field of gas leak monitoring and treatment by visualizing gases to quickly and accurately detect and locate the gas leak sources. However, this method still relies on manual visual inspection. Existing automatic visual gas detection methods suffer from insufficient gas feature extraction and high computational cost due to the indistinct gas features in thermal images. To address these problems, we propose a new lightweight network specialized for thermal gas feature extraction, namely GasViT, sufficiently extracting gas features at very low computational cost by local–global feature fusion. Specifically, two new feature extraction modules Multi-scale Fusion Feature Attention (MsFFA) and Multi-head Linear Self-attention (MhLSa) are proposed for GasViT. MsFFA enhances the gas local feature extraction ability by constructing multi-scale channel and spatial feature fusion maps, enabling the network to focus on more valid local information. MhLSa complements the gas global features with very low computational cost by efficiently encoding the global information of the image in terms of the innovative linear self-attention mechanism. Our experimental results on the self-made Industrial Invisible Gas (IIG) Dataset show GasViT achieves 82.7% mAP50, significantly outperforming the state-of-the-art lightweight networks. Moreover, GasViT achieves 33 FPS real-time detection with a running memory footprint of only 47.2 MB on edge computing devices, making it extremely suitable for portable and embedded detection devices than existing methods to cover gas leakage detection in complex and hazardous industrial scenarios.

METHODS OF INVESTIGATING GAS LEAKS IN PIPELINE BELTS

The use of combustible gases in household and industry began in the past centuries. By processing coal, methane and other gas mixtures contained in it were obtained. For the first time, this method was used in England in the 19th century. A large number of plants have started operating, which realized the purchase. This processed gas was used to burn large furnaces and relatively heat houses. Over the years, people began to use natural gas as a fuel and its use became more and more widespread. Already in the middle of the 20th century, along with other combustible gases, natural gas began to be used in industry and household. Historical sources show that the most use of natural gas was in the United States. The reason for this is the discovery of large gas fields in the country. The history of the beginning of the gas industry in the United States The year 1831 is considered. Later, the wide consumption of natural gas in the pipe-laying industry and in households began here. Wide use of natural gas in Azerbaijan began in the middle of the 19th century. Natural gas was used as a fuel for the first time in 1859 in a factory operating in Surakhani. Gas energy is the main source of energy used in many home and industrial applications. However, gas leaks can pose serious hazards. Timely detection and repair of gas leaks play an important role in preventing dangerous situations such as fire, explosion and poisoning. In this article, we will focus on the methods used to investigate gas leaks. The article examines and analyzes the types of gas leak detection. The main goal of selecting automatic systems in gas pipelines and their analysis is to detect gas leaks and accurately control gas consumption. The article analyzes the following methods of monitoring gas leaks in a gas pipeline: Volume balance method, gas movement time-varying pressure drop method, acoustic emission method, ultrasound method and pressure profile method. Each of these methods has advantages and disadvantages. The results of the application of all the considered methods vary depending on the parameters of the external environment. The parameters of the external environment can be given as an example of temperature, density and pressure. The main task of the proposed methods is the accuracy of the measurement results in the changing parameters of the external environment. Let's consider modern methods of detecting gas leaks in pipelines. Two main methods have been developed for locating gas leaks: visual and instrumental methods. Special attention is also paid to one of the most common automatic gas analyzers - gas sensors. Keywords: Gas leak detection, automatic gas analyzers, pipelines, gas leak monitoring, gas sensors.

Colorimetric detection of ammonia using an adhesive, stretchable hydrogel patch

Currently, because of the harmful effects of ammonia on human health, ecosystems, and overall air quality, the development of easily usable, portable, cost-effective, and field-deployable ammonia sensors is urgently necessary. This study focused on the development of an ammonia-sensing patch composed of a fibrous sensing component and hydrogel-like support substrate. This sensor was capable of colorimetric ammonia sensing with a limit of detection of 0.2454 ppm. The fibrous sensing region was prepared by electrospinning a mixture of poly(acrylic acid) (PAA) and poly(vinyl alcohol) (PVA), followed by the partial crosslinking and adsorption of transition metal ions such as copper. A support substrate was prepared by combining PVA and borate compounds, resulting in a stretchable, adhesive, and self-healing hydrogel. The ammonia-sensing patch demonstrated enough adhesion to various substrates and could be used as a sticky plaster-type sensor that also function as a sealant. The patch exhibited a distinct color change from blue–green to blue upon exposure to ammonia gas, which was attributed to the formation of a complex between the copper ions and ammonia. The color change was analyzed by monitoring the average RGB intensities using a charge-coupled device (CCD) camera and a MATLAB program. Additionally, we assessed the patch's capability of detecting and sealing ammonia leakage through experiments conducted in a simulated gas leakage setup. Overall, the developed ammonia-sensing patch shows promise for the real-time colorimetric detection of ammonia gas and has potential applications in gas leakage detection and prevention.

Arduino-Based Safety System for Gas Flame Monitoring, Leakage Detection, and Utensil Presence Using Sensor Integration and SMS Alerts

Abstract: In this research project, we present an innovative Arduino-based safety system designed to enhance household gas safety by monitoring gas flame, detecting gas leakage, and ensuring the presence of utensils in the cooking area. By integrating various sensors such as a flame sensor, gas leakage sensor, and ultrasonic sensor, coupled with servo motor control and GSM technology, our system provides real-time monitoring and proactive response to potential hazards. This project showcases the integration of hardware components and intelligent control algorithms, enabling an efficient and reliable safety system. The use of Arduino as the central control unit provides a flexible and cost-effective solution for gas safety applications. Experimental results demonstrate the effectiveness of our system in detecting potential hazards, triggering appropriate actions, and notifying users via SMS alerts.

Alat Pendeteksi Kebocoran Gas menggunakan Sistem Mikrokontroler Arduino dengan Notifikasi SMS

The use of gas can have both good and bad effects, the good effect is increasing the effectiveness of cleaner heat production and reducing the concentration of pollutants in the air. Gas cylinders that explode as a result of human error or errors in the production process of LPG gas cylinders or gas hoses should receive immediate treatment so that they do not become more dangerous and result in more victims. Efforts can be made to reduce this incident by detecting the smell of gas from leaks in gas cylinders by a gas sensor implemented by a microcontroller. How this microcontroller-based gas leak detector works in a home can be seen in the MQ-2 Sensor. If the sensor detects a gas leak (MQ-2) in the room, the buzzer will light up as an alarm that a gas leak has occurred and the Arduino will give a command to the SIM800L GSM module to send an SMS notification to the home owner's cellphone. This tool works by reading gas levels which are tested using a portable stove with a gas source via a portable gas cylinder which contains the same butane compound as the compound in LPG gas. If the gas level is detected by the mq-2 sensor, an alarm will automatically come on indicating an occurrence. gas leak in the room. then the Arduino nano orders the SIML800 GSM module to send an SMS to the home owner that there has been a gas leak in the house so that the home owner can find out and replace the hose or regulator on the gas cylinder.

Flow rate quantification of small methane leaks using laser spectroscopy and deep learning

To ensure safety in the transport and loading processes involving hazardous gasses, the detection of gas leaks in technical installations and the subsequent quantification of the released mass flows is of great public interest. One common approach to obtain this information is the usage of gas cameras, but they typically provide only inaccurate concentration data and are not sensitive enough to detect small gas leaks. In this work, we use a new gas camera approach that is based on laser absorption spectroscopy combined with mid-infrared imaging to solve these challenges. We gain videos of artificial gas leaks that containing more accurate concentration length information in ppm*m while visualizing small flow rates between 5 and 60 ml/min. For the ease of experimentation, we utilize methane while the camera can be adapted to other possibly hazardous gasses. On the dataset obtained, we train a deep-learning model aiming to continuously predict the flowrate. Additionally, we evaluate it on an eight-class classification task to compare accuracy metrics. We demonstrate an average difference of 1.6 ml/min from the mean predicted values to the true flow rates as well as an accuracy of (54 ± 2) % in the classification task.

One-step synthesis of Pt-CNTs/rGO electrocatalyst for wearable methanol sensing: Effect of supports on gas-sensitive property

Methanol is widely used in the chemical industry to develop downstream products. However, given the toxicity of methanol, it is of huge engineering significance to establish a wearable methanol sensor with a real-time monitoring function. Herein, we have constructed Pt-CNTs/rGO electrocatalysts based wearable sensor with good methanol-sensitive response, including a wide linear detection range, good repeatability, stability, selectivity as well as detectability in real samples. Noteworthy, in this work, CNTs and rGO acted as composite support for loading Pt nanoparticles, electrochemical measurement revealed that the sensing performance of Pt-CNTs/rGO based sensor was better than the sensor used Pt-CNTs due to the synergistic effect of composites support. Density functional theory (DFT) calculations have also demonstrated that rGO was more favorable for the adsorption of methanol molecules than CNTs. When the ratio of CNTs and rGO was 1:2, the methanol sensor exhibited the highest sensitivity and response/recovery speed (less than 12 s for detection of methanol gasoline) compared to that of other proportions. This work provides convincing support for developing wearable electrochemical gas sensors from the point of view of designing electrocatalysts, showing great prospects in the safety detection application, such as early trace gas leak detection and daily monitoring of workers on the shop floor.

Detection of Volatile Organic Compounds (VOCs) in Indoor Environments Using Nano Quadcopter

The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor gas concentrations in environments where these substances are manipulated. Gas sensor technology has evolved rapidly in recent years, offering increasingly precise and reliable solutions. However, there are still challenges to be overcome, especially when sensors are deployed on unmanned aerial vehicles (UAVs). This article discusses the use of UAVs to locate gas sources and presents real test results using the SGP40 metal oxide semiconductor gas sensor onboard the Crazyflie 2.1 nano quadcopter. The solution proposed in this article uses an odor source identification strategy, employing a gas distribution mapping approach in a three-dimensional environment. The aim of the study was to investigate the feasibility and effectiveness of this approach for detecting gases in areas that are difficult to access or dangerous for humans. The results obtained show that the use of drones equipped with gas sensors is a promising alternative for the detection and monitoring of gas leaks in closed environments.

Precision Gas Leakage Detection System with Integrated Sensors

Gas leaks pose significant safety risks in both residential and industrial settings, making gas leakage detectors essential safety devices for many years. These detectors serve the crucial function of identifying the presence of hazardous gases, such as carbon monoxide, and activating an alarm system to promptly alert occupants of the building. This innovative gas leak detection system leverages mobile technology, integrating phone calls, SMS, WhatsApp, and GPS location services. Additionally, it features a unique capability to shut off the power supply in the affected area, providing a real-time and effective means of gas leak detection and containment. This gas leakage detection system prototype has been successfully developed and tested with gases such as BUTANE and PROPENE. The experimental results demonstrate that the system can detect gas leaks in less than a minute. The primary objective of this gas detection project is to implement a security system for identifying gas leaks in closed environments. It utilizes MQ-2 sensors designed to function effectively within enclosed spaces. Furthermore, this system can be seamlessly integrated with other security and safety systems for enhanced overall protection.

Identificating vegetation stress under natural gas micro-leakage based on leaf scale temporal hyperspectrum

ABSTRACT Timely detection of underground natural gas leakage is significant for the safe production of gas storage and the protection of ecological environment. Direct detection makes it difficult to identify natural gas micro-leakage accurately, while hyperspectral remote sensing can identify micro-leakage based on spectral changes of vegetation near the leakage area. However, the spectral morphological characteristics of vegetation leaves under natural gas micro-leakage will undergo complex changes over time, making it difficult to identify vegetation stress. Temporal spectral characteristics improve the capability of capturing spectral differences of stressed vegetation at different times, allowing for indirect evaluation of leaked information. This study simulates the stress of underground natural gas storage micro leaks on bean growth through field experiments. Based on the changes in spectral reflectance and chlorophyll content of leaves, temporal features were constructed based on multi-temporal reflectance data, and feature selection methods were used to obtain bands sensitive to natural gas stress. This paper proposed a leaf scale natural gas micro-leakage identification index based on temporal spectral features (TNII) to identify bean stress under natural gas leakage. The results are summarized as follows: (1) With the leakage of underground natural gas, the spectral reflectance of leaves increased in the visible and near-infrared range, and the chlorophyll content decreased. (2) The TNII ( R 676 − R 526 ) Tn − ( R 676 − R 526 ) T 1 ( R 676 − R 526 ) Tn + ( R 676 − R 526 ) T 1 constructed based on sensitive wavebands (676 and 526 nm) can be employed to identify bean stress under natural gas after 17 days of leakage. The JM (Jefferies Matusita) distance test indicates that TNII could timely and stably identify bean stress under natural gas micro-leakage (JM > 1.8). This study provides the theoretical basis and technology support for micro-leakage detection of underground gas storage or pipelines.

Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data

Deep learning has been widely applied to automated leakage detection and location of natural gas pipe networks. Prevalent deep learning approaches do not consider the spatial dependency of sensors, which limits leakage detection performance. Graph deep learning is a promising alternative to prevailing approaches as it can model spatial dependency. However, the challenge of collecting real-world anomaly data for training limits the accuracy and robustness of currently used graph deep learning approaches. This study proposes a deep probabilistic graph neural network in which attention-based graph neural network is built to model spatial sensor dependency. Variational Bayesian inference is integrated to model the posterior distribution of sensor dependency so that the leakage can be localized. An urban natural gas pipe network experiment is employed to construct the benchmark dataset, in which normal time-series data is applied to develop our proposed model while anomaly leakage data is used for performance comparison between our model and other state-of-the-art models. The results demonstrate that our model exhibits competitive detection accuracy (AUC)=0.9484, while the additional uncertainty interval provides more comprehensive leakage detection information compared to state-of-the-art deep learning models. In addition, our model’s posterior distribution enhances the leakage localization with the accuracy of positioning (PAc)= 0.8, which is higher than that of other state-of-the-art graph deep learning models. This study provides a comprehensive and robust alternative for subsequent decision-making to mitigate natural gas leakage from pipe networks.

Automatic LPG Leakage Detector using Arduino (UNO)

Gas leakage is a major problem in industries, residential premises and gas powered vehicles. The leakage if not detected may lead to explosion and cause severe damages to life and environment. The conventional leakage detection system uses on-site alarms for warning. The "LPG Gas Leakage Detector Using Arduino" is an innovative safety system designed to detect and respond to the presence of Liquefied Petroleum Gas (LPG) leaks in various environments, including homes, kitchens, and industrial settings. This device leverages Arduino microcontroller technology and gas sensors to provide real-time monitoring and alerts. The system offers an additional layer of protection against the potential hazards and dangers associated with gas leaks.

Design Of An LPG Leak Detection System Using Iot Based MQ-2 Sensor

The IoT-based LPG gas leak detection system with MQ-2 and automatic regulator aperture is designed to protect the environment from the dangers of gas leaks and optimize gas use. The MQ-2 sensor is used to detect LPG gas accurately and sensitively. This system is connected to IoT which allows remote monitoring via smart devices. When the sensor detects that the LPG gas concentration exceeds a safe threshold, the system will send an alert with notification and automatically activate the regulator to cut off the gas supply. This helps prevent the accumulation of harmful gases. This design combines reliable gas detection with automatic operation to improve environmental safety and gas efficiency. LPG gas is very commonly used by the community because it has many advantages, but there are also many risks associated with using LPG gas, such as poisoning, shortness of breath and even fire. It is therefore important to have a leak detection system to prevent accidents that may occur, by integrating the programmable MQ-2 sensor and NoteMCU ESP8266.

Smart Home Remote Control System Prototype Using Internet of Things (IoT) Based ESP8266 Microcontroller

Technology is developing very rapidly in the current era, there will be several impacts that will result from the development of technology. Electronic equipment control can be done remotely in real time using the IoT system. The problem faced is the concern when leaving home. Because when leaving the house, homeowners sometimes often forget about electronic devices whether they are turned off or still on. IoT can be utilized at home as a smart home system by connecting an electronic device to an android application. This IoT-based smart home system uses an ESP8266 microcontroller which is used to connect between hardware and software that is controlled through the Blynk application. Blynk application is used as an interface that functions to provide input and produce output. This system consists of four sensors, namely the MQ2 sensor to detect gas leaks in the kitchen, the DHT11 sensor to determine the level of humidity and temperature in the bedroom, the PIR sensor to detect human movement at the front entrance, the ULTRASONIC sensor as a detector of the water level in the water reservoir and using three relays as a connector for lights and other electronic components. And using the Blynk application as a control system on the ESP8266 microcontroller.

Room-Temperature Monitoring of CH4 and CO2 Using a Metal-Organic Framework-Based QCM Sensor Showing Inherent Analyte Discrimination.

The detection of methane and carbon dioxide is of growing importance due to their negative impact on global warming. This is true for both environmental monitoring and leak detection in industrial processes. Although solid-state sensors are technologically mature, they have limitations that prohibit their use in certain situations, e.g., explosive atmospheres. Thus, there is a need to develop new types of sensor materials. Herein, we demonstrate a simple, low-cost, metal-organic framework (MOF)-based gas leak detection sensor. The system is based on gravimetric sensing by using a quartz crystal microbalance. The quartz crystal is functionalized by layer-by-layer growth of a thin metal-organic framework film. This film shows selective uptake of methane or carbon dioxide under atmospheric conditions. The hardware has low cost, simple operation, and theoretically high sensitivity. Overall, the sensor is characterized by simplicity and high robustness. Furthermore, by exploiting the different adsorption kinetics as measured by multiple harmonic analyses, it is possible to discriminate whether the response is due to methane or carbon dioxide. In summary, we demonstrate data relevant toward new applications of metal-organic frameworks and microporous hybrid materials in sensing.

Mixed Potential Electrochemical Sensors for Natural Gas Leak Detection – Field Testing of Portable Sensor Package

According to the EPA, methane (CH4) emissions from oil and gas infrastructure accounted for 211 million metric tons of CO2 equivalent in 2020 [1]. Actual emissions may exceed this by a factor of three [2]. Current natural gas leak detection technologies largely consist of optical sensors such as IR spectrometers [3]. Optical sensors have high sensitivity, but the high cost and fragility of these sensors limit practical applications and continuous monitoring in the field. Mixed potential electrochemical sensors (MPES) are low cost, robust, selective and sensitive, making them a viable option for continuous natural gas leak detection [4]. While we have previously reported on the development of these sensors for natural gas detection in the laboratory, it is necessary to evaluate how these sensors perform in relevant environments.The MPES device consists of La0.87Sr0.13CrO3 (LSC), indium tin oxide (ITO, In2O3 90 wt%, SnO2 10 wt%), and Au sensing electrodes with a Pt pseudo-reference electrode, bridged by 3 mol% YSZ solid electrolyte. A low ionic conductivity magnesia stabilized zirconia (MSZ) substrate is used to enhance sensitivity with a demonstrated limit of detection (LOD) of < 40 ppm. The sensor is integrated with an internet of things (IoT) data collection and transmission package developed by SensorComm Technologies.Field testing was performed at Colorado State University’s Methane Emission Technology Evaluation Center (METEC). The sensors’ capability of detecting buried pipeline leaks was investigated by varying the leak rate from 7.2 SLPM to 37 SLPM, lateral sensor distance from 0 meters to 3 meters, and vertical distance from 0 meters to 0.28 meters (Figure 1). Machine learning methods were applied to a training dataset collected in the laboratory to quantify the CH4 concentration. These results serve as a first demonstration that a low-cost mixed potential electrochemical sensor system can successfully detect underground pipeline emissions and quantify CH4 concentrations that are in agreement with previously published results [6] collected using more complex and costly methods.