Leak Detection System Research Articles

SMART GAS MONITORING AND AUTOMATED ALERT SYSTEMS USING HYBRID MACHINE LEARNING MODELS

This study proposes a real-time gas leakage detection and prevention system leveraging machine learning models to ensure safety and efficiency in industrial settings. The system utilizes sensor data from various sources, including pressure and gas sensors. The algorithms that were adopted to have a real-time result include, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Time Memory (LSTM) and Support Vector Machines (SVM. The algorithm is used to enhanced the detection of anomalies in sensor readings, identify potential gas leaks, predict leakage probability and trigger alerts and automated responses. The proposed system integrates real-time data processing edge computing in a cloud based analytics to enhance fast detection and response which will reduce false alert and improve accuracy in real-time processing which will enhanced safety both in residential houses and industrial setting. Gas leakage detection and prevention are critical for ensuring safety in various industrial and residential environments. Traditional methods often fall short in terms of real-time response and accuracy. This paper explores the application of machine learning (ML) models in developing advanced real-time gas leakage detection and prevention systems. We discuss various ML approaches, including supervised and unsupervised learning techniques, and evaluate their performance based on real-time data handling, accuracy, and efficiency. Through empirical results and case studies, we demonstrate how ML can significantly enhance the effectiveness of gas leakage systems.

Development of a Tool for Verifying Leakage Detection in Microfluidic Systems

While submissions of microfluidic-based medical devices to the Food and Drug Administration (FDA) have increased in recent years, leakage remains a common but difficult failure mode to detect in microfluidic systems. Here, we have developed a sensitive tool to measure and verify leakages ranging from 0.1% to 10% in leakage detection systems, which can then be used to detect leak in microfluidic devices. Our methodology includes an analytical model that applies hydrodynamic resistance using different fluid-contacting elements (e.g., tubing, junctions, and connectors) to tune the leakage rate based on the application-specific acceptance criteria. We then used three polymer-based microfluidic systems to target leakage rates of approximately 0.1, 1.0, and 10%. The experimental uncertainties in Polyether Ether Ketone (PEEK) tubing were 23.08%, 13.64%, and 1.16%, respectively, while the PEEK-Coated Fused Silica (PEEKsil) tubing system had errors of 0.00%, 0.72%, and 1.59%, respectively, relative to the theoretical values for the same target leak rates. The commonly used commercial grade Cyclic Olefin Copolymer (COC) microfluidic chips produced errors of 7.69% and 5.05%, respectively, for target leakage rates of 0.24% and 1.88%. We anticipate that the proposed bench test method can be useful for device developers as a verification tool for leakage detection systems before assessing flow-mediated leakage failure modes in microfluidic medical devices.

Design and Development of LPG Monitoring and Leakage Detection System

This study focuses the design and developed of an LPG Monitoring and Leakage Detection System, with the goal of enhancing safety in areas where LPG is utilized by identifying gas leaks and monitoring gas levels in real time. The project employs an Arduino-based sensor system to automate the detection of gas leaks, emphasizing functionality, durability, and safety. The proposed system is assessed for its effectiveness in providing early warnings to avert potential accidents, fires, or health hazards linked to LPG leaks. The system was tested by 30 participants who regularly used LPG for cooking and possessed basic electronics knowledge, enabling them to evaluate the project's materials, functionality, and operation. The results indicated that the system is highly effective in detecting LPG and other combustible gases, which can pose significant safety risks. The device includes a display that indicates the amount of gas detected and the concentration percentage, offering real-time data on gas levels. In the event of a gas leak, the system activates a buzzer to alert users immediately, thereby minimizing the risk of accidents

Engineering an Ultrafast Ambient NO2 Gas Sensor Using Cotton-Modified LaFeO3/MXene Composites.

This work presents a room-temperature (RT) NO2 gas sensor based on cotton-modified LaFeO3 (CLFO) combined with MXene. LaFeO3 (LFO), CLFO, and CLFO/MXene composites were synthesized via a hydrothermal method. The fabricated sensor, utilizing MXene/CLFO, exhibits a p-type behavior and fully recoverable sensing capabilities for low concentrations of NO2, achieving a higher response of 14.2 times at 5 ppm. The sensor demonstrates excellent performance with a response time of 2.7 s and a recovery time of 6.2 s, along with notable stability. The sensor's sensitivity is attributed to gas interactions on the material's surface, adsorption energy, and charge-transfer mechanisms. Techniques such as in situ FTIR (Fourier transform infrared) spectroscopy, GC-MS (gas chromatography-mass spectroscopy), and near-ambient pressure X-ray photoelectron spectroscopy were employed to verify gas interactions and their byproducts. Additionally, finite-difference time-domain simulations were used to model the electromagnetic field distribution and provide insight into the interaction between NO2 molecules and the sensor surface at the nanoscale. A prototype wireless IoT (Internet of Things)-based NO2 gas leakage detection system was also developed, showcasing the sensor's practical application. This study offers valuable insight into the development of room-temperature NO2 sensors with a low detection limit.

Multi-pass Cavity-Enhanced Raman Spectroscopy of Complex Natural Gas Components

BackgroundThe concentration of natural gas components significantly impacts the transportation, storage, and utilization of natural gas. Consequently, implementing online monitoring and leak detection systems is vital to guarantee the efficient use of natural gas and to uphold its safe and stable operation. Raman spectroscopy offers distinctive benefits, including high selectivity, superior precision, and the capability to detect multiple gas components simultaneously using a single-wavelength laser. Nevertheless, the inherent weakness of the Raman effect in gases results in limited detection sensitivity for Raman spectroscopy, which may not suffice for specific practical applications. ResultsThis paper presents a study investigating the detection of natural gas's complex components using a high-sensitivity multi-cavity enhanced Raman spectroscopy technique. An enhanced folded Z-shaped multi-pass cavity has been constructed to amplify the interaction length between the laser and the gas, thereby significantly boosting the Raman signal intensity by 1000 times. The detection limits for CH4, C2H6, C3H8, n-C4H10, i-C4H10, n-C5H12, i-C5H12, and n-C6H14 gases reached 0.12, 0.53, 0.55, 0.67, 0.28, 0.46, 0.34, and 0.71 ppm, respectively. The least squares method was utilized to establish quantitative relationships between the characteristic peak heights and the concentrations of gases, encompassing both single-component and mixed multi-component systems. Additionally, the natural gas samples were configured and subsequently detected and analyzed. SignificanceAs proposed in this paper, the results indicate that the MPC-CERS system boasts several advantages, including a low detection limit, high quantitative accuracy, excellent detection repeatability, and robust system stability. Furthermore, its capability for real-time monitoring is well-suited to meet the gas detection requirements in practical applications. Consequently, the research presented in this paper offers innovative insights for the online tracking and leak detection of distributed energy natural gas systems.

(Invited) Development of Fiber-Optic Hydrogen Sensor Using Platinum-Supported Tungsten Trioxide-Silica Composite Film

Hydrogen has been attracting attention in recent years as a clean energy carrier that can be easily obtained by converting water into hydrogen by using various renewable natural energy resources and enable to generate energy through chemical reaction without emitting pollutants. While it has these advantages, hydrogen gas is characterized by two dangerous properties: low ignition energy and wide combustion range. Furthermore, leakage of hydrogen is difficult to detect because of its properties such as colorless, transparent, and odorless. Therefore, a leakage detection system is indispensable for the safe handling of large amounts of hydrogen energy carrier. So far, various high-performance sensors have been proposed and practically utilized. However, developments of sensor devices which can be operated at room temperature are still challenge. Furthermore, there are significant needs for distributed measurements for leakage detection of large-scale hydrogen infrastructure. In this study, fiber-optic techniques that can easily and inexpensively capture hydrogen leakage in a wide spatial area are focused to realize a line-type hydrogen sensor. In order to develop such kind of fiber optic hydrogen sensor, platinum-supported tungsten oxide film and tungsten-silica composite film were utilized as hydrogen-sensitive materials whose optical properties are significantly change through the reaction with hydrogen. A plastic-clad quartz core fiber with a core diameter of 200 μm and a cladding diameter of 230 μm (H-PCF: manufactured by Sumitomo Electric Industries) was used. In order to immobilize the hydrogen-sensitive cladding, the cladding was removed using an organic solvent. Then, the hydrogen-sensitive materials were directly coated on fiber core and immobilized by calcination in air at different temperatures. The sensing mechanism is based on the evanescent field interaction in the hydrogen sensitive clad region. As a first step, transmission spectra of sensing film itself under air and hydrogen atmosphere were evaluated for optimizing wavelength of the light source used in the sensor system. The transmittance of the sensitive film decreased in the range of wavelengths above about 500 nm after exposure to pure hydrogen, and the decrease at 1300 nm was greater than that at 850 nm, suggesting that the 1300 nm wavelength is more likely to extract changes in optical properties caused by the response (Fig. 1). Then, fiber optic sensor device was fabricated, and the amount of propagating light was measured using LED light sources at wavelengths of 850 nm and 1300 nm. In the case of using the light source with a wavelength of 1300 nm, the light attenuation rate with respect to the hydrogen response was nearly twice and the response speed was faster, compared with those at 850 nm. Therefore, it was concluded that a light source with a wavelength of 1300 nm for measurement of propagating light intensity is suitable for monitoring hydrogen leakage. Figure 1

Optimizing acoustic signal processing for localization of precise pipeline leakage using acoustic signal decomposition and wavelet analysis

Pipelines play a crucial role in today's infrastructure. However, they are easily affected by environmental conditions. Therefore, regular inspections are essential to ensure the integrity of pipelines. This study introduces a new approach that combines variation mode decomposition (VMD) and empirical wavelet transform (EWT) with continuous wavelet transform (CWT) for acoustic data to improve the signal-to-noise ratio (SNR) and accurately detect pipeline leakage location in the time domain.The acoustic data was gathered in three distinct scenarios inside an open environment, where external noise considerably influenced the original signal.VMD and EWT are employed to enhance the signal-to-noise ratio (SNR). VMD efficiently distinguishes high-frequency noise and assures distinct mode separation, whilst EWT delivers precise temporal localization for non-stationary signals. Based on the results of VMD and EWT, certain IMFs were selected for further study and SNR is computed among the respective chosen IMFs and original signals to measure the improvement. Subsequently, the CWT was utilized on the chosen IMF for feature extraction, effectively finding leakage locations in the time domain.This study illustrates the feasibility of successfully mitigating noise and extracting signals in open and noisy environments, thus enhancing the accuracy of leak detection. The results establish a robust basis for subsequent investigations in pipeline leak detection and demonstrate the applicability of advanced signal processing approach to real-time leak detection systems.

Water Leakage Detection and Management System

In this era of globalization, water leakage significantly contributes to water waste and can adversely affect the environment if left unchecked. Much water is wasted due to unsupervised and poorly managed actions. The primary objective of leak detection is to locate leaks and measure their dimensions in secured products and systems. The process begins with hardware development, which is crucial for obtaining raw data by measuring water flow velocity at specific crack locations. Software-based methods use computer systems to monitor pressure and flow rate data to detect leaks continuously. This involves converting the velocity data into audio frequency signals. The gathered signals are then processed in MATLAB, a high-performance technical computing software, through four phases: segmentation, pre-processing, feature extraction, and classification, with Internet of Things (IoT) features integrated. Finally, the data is analyzed using the k Nearest Neighbour (kNN) classifier for classification.

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption.

A global re-analysis of regionally resolved emissions and atmospheric mole fractions of SF6 for the period 2005–2021

Abstract. We determine the global emission distribution of the potent greenhouse gas sulfur hexafluoride (SF6) for the period 2005–2021 using inverse modelling. The inversion is based on 50 d backward simulations with the Lagrangian particle dispersion model (LPDM) FLEXPART and on a comprehensive observation data set of SF6 mole fractions in which we combine continuous with flask measurements sampled at fixed surface locations and observations from aircraft and ship campaigns. We use a global-distribution-based (GDB) approach to determine baseline mole fractions directly from global SF6 mole fraction fields at the termination points of the backward trajectories. We compute these fields by performing an atmospheric SF6 re-analysis, assimilating global SF6 observations into modelled global three-dimensional mole fraction fields. Our inversion results are in excellent agreement with several regional inversion studies in the USA, Europe, and China. We find that (1) annual US SF6 emissions strongly decreased from 1.25 Gg in 2005 to 0.48 Gg in 2021; however, they were on average twice as high as the reported emissions to the United Nations. (2) SF6 emissions from EU countries show an average decreasing trend of −0.006 Gg yr−1 during the period 2005 to 2021, including a substantial drop in 2018. This drop is likely a direct result of the EU's F-gas regulation 517/2014, which bans the use of SF6 for recycling magnesium die-casting alloys as of 2018 and requires leak detection systems for electrical switch gear. (3) Chinese annual emissions grew from 1.28 Gg in 2005 to 5.16 Gg in 2021, with a trend of 0.21 Gg yr−1, which is even higher than the average global total emission trend of 0.20 Gg yr−1. (4) National reports for the USA, Europe, and China all underestimated their SF6 emissions. (5) Our results indicate increasing emissions in poorly monitored areas (e.g. India, Africa, and South America); however, these results are uncertain due to weak observational constraints, highlighting the need for enhanced monitoring in these areas. (6) Global total SF6 emissions are comparable to estimates in previous studies but are sensitive to a priori estimates due to the low network sensitivity in poorly monitored regions. (7) Monthly inversions indicate that SF6 emissions in the Northern Hemisphere were on average higher in summer than in winter throughout the study period.

Tetraphenylethene-Containing Nanofilm via Interfacial Confinement Self-Assembly for Fluorescent Monitoring of SOCl2 Leakage in a Li-SOCl2 Battery Electrolyte.

Lithium thionyl chloride (Li-SOCl2) batteries are widely used due to their high energy density and long shelf life. However, the corrosive nature of SOCl2 poses a safety hazard, necessitating effective leak detection methods. We report an approach for real-time fluorescent detection of SOCl2 leakage in Li-SOCl2 batteries using a tetraphenylethene-based nanofilm. The nanofilms were prepared through the interfacial confined self-assembly method and exhibit excellent flexibility, homogeneity, tunable size and thickness, and adaptability to various substrates, enabling easy integration into sensor devices. They possess high photochemical stability and a photoluminescence quantum yield exceeding 25%, demonstrating their potential as high-performance fluorescent sensing material. The nanofilms also exhibit high sensitivity, good reproducibility, and selectivity toward SOCl2 detection. Upon exposure to SOCl2 vapor, the nanofilm shows a red-shifted and fluorescence quenching response, attributed to the protonation of the acylhydrazone bond in the nanofilm by the hydrolysis product of SOCl2, which disrupts the electronic structure of the nanofilm and leads to a decrease in the fluorescence intensity and a shift in the emission wavelength. A detection limit of ∼1.31 ppt and excellent repeatability over 300 cycles are demonstrated, highlighting their high sensitivity and reliability. This work paves the way for a highly sensitive and reliable leak detection system for Li-SOCl2 batteries, enhancing their safety and reliability in various applications.

Hybrid TDR-MI Based Wireless Sensor Network for Underground Water Pipeline Leakage Detection and Localization Using Pressure Residuals and Classifiers

The pipeline leakage detection and leak localization trouble is a highly demanding and dangerous issue. Underground pipelines are critical for transporting enormous fluid volumes (e.g., water) across extended distances. Not only would solving this issue save the nation a great deal of money and resources, but it will also save the environment. However, because of the harsh climatic conditions below earth, current leak detection systems are not enough for monitoring subterranean pipelines. To address these issues, this study suggests a hybrid wireless sensor network for monitoring subterranean pipelines that is based on magnetic induction and time domain reflectometry (TDR). TDR is installed in this instance below a wireless sensor network that is based on MI. TDR significantly reduces the time needed for inspection while accurately locating the leak. Based on MI technology, we provide a wireless sensor network for inexpensive, real-time leak detection in subterranean pipelines. Through the integration of data from several sensor types located within and around subterranean pipes, MISE-PIPE detects leaks. Ad-hoc WSNs are employed in pressure measurement. (WDNs) is a popular subject that has drawn attention from scholars lately. Since leak localisation has a significant influence on the human population and the economy, time and accuracy are essential components. A broad leak localisation technique is proposed using statistical classifiers operating in the residual space. Classifiers are trained using leak data from every node in the network, accounting for demand uncertainty, noise from sensor preservatives, and leak size. After localising and identifying leaks, all monitoring data is sent to the CH using the K-means clustering technique, which performs two vital tasks: optimum clustering, extending the Network Lifetime, and maintaining Quality of Service. The K-Means technique is used to optimise the clustering process. The K-means clustering technique is used to transfer all monitoring data to the CH for the purpose of pipeline leak identification and localisation. Unlike the current underground pipeline monitoring system, our proposed Hybrid TDR-MI-based wireless sensor network allows precise real-time leak identification and localisation.

IoT Leak Detection System for Onshore Oil Pipeline Based on Thermography.

The vast expanses of remote onshore areas in oil-producing countries are home to a network of flow and collection pipelines that are susceptible to leaks. Most of these areas lack the infrastructure to enable the use of remote monitoring systems equipped with sensors and real-time data analysis to provide early detection of anomalies. This paper proposes a proof of concept for a monitoring system based on the Internet of Things (IoT) for real-time detection of pipeline leaks in onshore oil production fields. The proposed system, based on a thermal imaging leak detection method, informs the operator of the system's operating status via a web page. The leak detection system communicates via a Zigbee network between the IoT devices and a 4G mobile network. The results of the tests carried out show that a visual and automatic IoT-based leak detection system is possible and plausible. The proposed leak detection system enables supervisors at remote stations and field workers to monitor the operating status of pipelines via computers, tablets, or smartphones, regardless of where they are.

Artificial intelligence based leak detection in blended hydrogen and natural gas pipelines

In the transition towards a hydrogen-based energy system, the strategic use of pipelines is crucial for efficient hydrogen distribution. Leveraging existing natural gas pipelines to carry a mix of hydrogen and natural gas offers a cost-effective alternative to building new infrastructure. This study explores the development of a leak detection system compatible with existing pipelines, specifically tailored for the blended hydrogen and natural gas mix. Given the scarcity of leak data for blended hydrogen-natural gas pipelines, the study introduces a Real-Time Transient Model (RTTM) for blended gases, simulating leak dynamics to generate necessary data. Additionally, a leak detection system (LDS) is developed using a fusion of Convolutional Neural Network (CNN) and Explainable Artificial Intelligence (XAI) through Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This LDS framework overcomes the “black box” issue common in AI-driven systems, enabling reliable detection. The integration of Explainable and traditional AI techniques holds promising implications for blended hydrogen pipelines by enhancing the safety and efficiency of hydrogen transportation, thereby mitigating economic and environmental impacts, and addressing public concerns.

Evaluation of Electrochemical Properties and Life Prediction of Sensor Wire in Leak Detection Systems of Underground Heating Pipelines

In this study, we investigated the electrochemical properties and lifespan of the NiCr (NiCr 8020) sensor wire of a resistance leaking detection (LD) system to detect pipe corrosion and leakage in an actual district heating (DH) system. The temperature and applied stress of the sensor wire during the actual operation of the resistance LD system of the DH system were derived through simulations and calculations. The anodic dissolution of the sensor wire was accelerated with the increased temperature and the applied current. The corrosion type changed from localized corrosion, such as pitting, to uniform corrosion. The applied stress caused ductile fracture of the thinned sensor wire by anodic dissolution. In conclusion, we confirmed that in the resistance LD system of a DH system, where current and stress are applied at high temperatures, the sensor wire becomes thin due to the anodic dissolution and subsequent ductile fracture. In addition, the lifespan of the sensor wire was derived according to the resistance level measured in the resistance LD system of the DH system. Our findings contribute to preventing failure and improving the reliability of the resistance LD systems of DH systems.

AUTOMATED LEAK AND WATER QUALITY DETECTION SYSTEM FOR PIPED WATER SUPPLY

The volume of water loss because of leakage in the conveyance pipe has been alarming. Old and poorly constructed pipelines, inadequate corrosion protection, poorly maintained valves, and mechanical damage contribute to leakage. Water-carrying pipes were buried underground, so tracing leak points manually could be tasking, if not impossible. This work was to report on the effectiveness of a developed Automated Leak and Water Quality Detection (ALWQD) system. This device can detect leaks in the piped water system automatically and can also report any deterioration in the quality of water that flows through affected pipes. The ALWQD consisted of several drainpipe connections, pipe accessories, electronic components, and sensors to monitor water quality impairment. The control signal was the solenoid valves that interfaced with the ESP-32 microcontroller boards placed on the pipe manifold at intervals, along with water quality monitoring sensors of turbidity, Total Dissolved Solids (TDS), and pH. The fabrication and testing of the device followed standard procedures. Testing of ALWQD was done at 0, 5, and 10 minutes under load and no-load conditions, with average variation in reading recorded after three trials. The findings indicated that the efficiency of ALWQD was between 70% and 80%, which could be improved upon. The trend in the results of the monitored parameters was not different from that of similar previous work. Leaks caused pressure drops and disallowed the full flow of water found at pipe joints, which could be a pathway for the intrusion of contaminants into the water conveyance system.

LPG Gas Leak Detection System and LPG Fire Classification Based on Internet of Things and Artificial Intelligence with Telegram Bot as a Monitoring Tool

LPG gas leaks pose a serious threat in industrial kitchens as they can cause costly fires, both in terms of material and safety. To improve safety, an accurate detection system is required. This research focuses on developing an LPG gas leak detection system and LPG fire classification with Internet of Things and Artificial Intelligence technology. Supported by Telegram Bot as an emergency notification monitoring tool, this system uses MQ-2 sensors to detect LPG gas leaks and ESP32-Cam to classify LPG fires along with Pretrained-model technology such as Cascade Fire Detection on OpenCV Cloud Server. As the output of this system, the use of PWM control and automation oversees regulating the Exhaust Fan according to the detected leakage. FreeRTOS is also used for system task efficiency, and Port Forwarding with Ngrok Local Server allows public access to the ESP32-Cam. System testing was conducted by Black-Box testing, then evaluating the performance of the MQ-2 sensor against 400 ppm and 1500 ppm thresholds for LPG testing distances in open kitchens and closed kitchens, as well as analyzing system response and delay via HTTP protocol. The results demonstrated the system's success in detecting gas leaks, classifying LPG fires and facilitating emergency communication.

Development of Model and Graphical User Interface for Leak Localization in Crude Oil Pipeline

Pipelines provide the most efficient and cost-effective means for fluid transport but the challenge posed by leaks has substantially increased the risks and hazards in pipeline fluid transportation. The development of efficient leak detection system pays off in quicker leak detection and localization, leading to quicker responses and remediation works by the pipeline emergency response team. This would ultimately bring about less severity of the pipeline leak in terms of financial losses, human and environmental consequences. Steady state modeling of crude oil pipeline flow with attempt to determine and localize leak has been achieved in this study. Mathematical models have been developed to detect and localize leaks in crude oil pipeline during leak occurrence. Leak detection was modeled using the conservation of mass equation while leak localization was modeled by modifying the Darcy-Weisbach pipeline liquid flow equations and utilizing the Swamee-Jain friction factor correlation. Equations for flowrate and pressure drop along the pipeline were developed for two cases: a case where there is no leak in the pipeline and a case where there is leak in the pipeline. Leak localization equation was determined by equating the fluid flow equation when there is no leak and when there is leak. The model was structured and simulated in Matlab software with the model tested for four field cases. The results from the simulation revealed swift leak detection, accurate localization of the leak, and determination of the pressure at the point of leak. Experimentally determined results from actual field measurements of leak incidences were used to validate the results. The result determined from the model developed proved accurate with only an average error of 0.216 miles.

Developing an internet of things system for hydrogen leak detection at hydrogen refueling stations integrating LoRa and global positioning system

Developing a hydrogen leak detection system that can quickly detect even the smallest amount of hydrogen is extremely crucial in the hydrogen industry. Currently, hydrogen refueling stations use a fixed hydrogen leak detection system. This study develops a mobile hydrogen gas leak detection system for hydrogen refueling stations using s, global positioning system, and Internet of Things (IoT) equipment, including LoRa and Wi-Fi. A digital twin is implemented by linking hydrogen gas-sensing data at a location that is quickly required or suspected using Google Maps. A time-series database is used to store the hydrogen gas-sensing data, and the transfer delay of hydrogen gas-sensing data between the hydrogen equipment was measured. Therefore, we developed a prototype system that transmits, stores, and analyzes hydrogen gas-sensing data in real time using IoT equipment for hydrogen refueling stations.

Gas leakage detection system for domestic and industrial applications

Liquefied Petroleum Gas (LPG) is a highly inflammable gas used as sources of energy in automobiles, industries and for domestic cooking. Disaster occurs when LPG leaks and is in contact with the source of fire which may claim lives and destroy properties. Hence, its early detection is essential for preventing disaster. This paper presented smart detection of LPG leakage in domestic homes and industries. The system consisted of MQ-2 gas sensor which sensed the presence of the gas in the air and sent signal to the Arduino micro controller. The micro-controller informed the embedded Global System for Mobile Communication (GSM) module to send message in form of Short Message Service (SMS) to the users to swing into action in a bid to prevent outbreak of fire. Performance tests carried out to evaluate the system showed the capability of the system to concurrently send SMS to different subscribers and produce audible alarm. It was also observed that SMS was sent to users in a timely and efficient manner. The system could be useful in residential homes and industrial premises for preventing fire hazards.