Detection of Natural Gas Leakages Using a Laser-Based Methane Sensor and UAV

Simultaneous Reconstruction of Gas Concentration and Temperature Using Acoustic Tomography

Acoustic tomography utilizes sensor arrays to collect sound wave signals, enabling non-contact measurement of physical parameters within an area of interest. Compared to optical technologies, acoustic tomography offers the advantages of low cost, low maintenance, and easy installation. Current research in acoustic tomography mainly focuses on reconstruction algorithms for temperature fields, while monitoring the composition and concentration of gases is significant for ensuring safety and improving efficiency, such as in scenarios like boiler furnaces and aviation engine nozzles. In excitable gases, the speed of sound exhibits an S-shaped curve that changes with frequency, a characteristic that could be potentially useful for acoustic tomography. Therefore, this study primarily discusses the quantitative calculation of gas concentration and temperature based on the dispersion of the speed of sound. By employing graphic processing and pattern matching methods, a coupled relationship of the dispersion of the speed of sound with gas concentration and temperature is established. The projection intersection method is used to calculate the concentration and temperature of binary and ternary gas mixtures. Combined with the inversion method, a joint reconstruction method for gas concentration fields and temperature fields based on the dispersion of the speed of sound is developed. The feasibility of the proposed simultaneous reconstruction method for temperature and concentration fields is validated using numerical simulations. Additionally, an acoustic tomography experimental system was set up to conduct reconstruction experiments for binary gas concentration fields and temperature fields, confirming the effectiveness of the proposed method.

Near-infrared dual-gas sensor for simultaneous detection of CO and CH4 using a double spot-ring plane-concave multipass cell and a digital laser frequency stabilization system

A novel double spot-ring plane-concave multipass cell (DSPC-MPC) gas sensor was proposed for simultaneous detection of trace gases, which has lower cost and higher mirror utilization than the traditional multipass cell with 129 m, 107 m, 85 m, 63 m and 40 m effective optical path lengths adjustable. The performance of the DSPC-MPC gas sensor was evaluated by measuring CO and CH4 using two narrow linewidth distributed feedback lasers with center wavelengths of 1567 nm and 1653 nm, respectively. An adjustable digital PID laser frequency stabilization system based on LabVIEW platform was developed to continuously stabilize the laser frequency within ∼±30.3 MHz. The Allan deviation results showed that the minimum detection limits for CO and CH4 were 0.07 ppmv and 0.008 ppmv at integration times of 711 s and 245 s, respectively. The proposed concept of DSPC-MPC provides more ideas for the realization of gas detection under different absorption path lengths and the development of multi-component gas sensing systems.

Quantifying non-steady state natural gas leakage from the pipelines using an innovative sensor network and model for subsurface emissions - InSENSE

Detecting and quantifying subsurface leaks remains a challenge due to the complex nature and extent of belowground leak scenarios. To address these scenarios, monitoring and evaluating changes in gas leakage behavior over space and time are crucial for ensuring safe and efficient responses to known or potential gas leaks. This study demonstrates the capability of linking environmental and gas concentration data obtained using a low-cost, near real-time methane (CH4) detector network and an inverse gas migration model to capture and quantify non-steady state belowground natural gas (NG) leaks. The Estimating Surface Concentration Above Pipeline Emission (ESCAPE) model was modified to incorporate the impact of soil properties on gas migration. Field-scale controlled NG experiments with leakage rates ranging from 37 to 121 g/h indicate that elevated belowground near-surface (BNS) gas concentrations persist long before elevated surface concentrations are observed. On average, BNS CH4 concentrations were 20%-486% higher than surface CH4 concentrations within the monitoring radius of 4 m from the leak location. An increase in the BNS CH4 concentration was observed within 3 h as the leak rate increased from 37 to 89 g/h. However, due to the atmospheric fluctuations, any changes in surface CH4 concentrations could not be confirmed within this period. The plume area of the BNS CH4 extended approximately two times farther than that of the surface CH4 as the gas leak rate increased from 37 to 121 g/h. The estimated NG leak rates by the modified ESCAPE model agreed well with the experimental NG leak rates (m = 0.99 and R2 = 0.77), demonstrating that including soil characteristics and BNS CH4 measurements can advance estimations of non-steady NG leak rates in low and moderate NG leak rate scenarios. The CH4 detector network and model show potential as an innovative tool to improve operators' risk assessment and NG leakage response.

Miniaturized and Portable Laser Gas Sensor for Standoff Methane Detection With Non-Cooperative Targets

A standoff methane (CH4) sensor with actual hard topographic targets (usually called non-cooperative targets) is essential for natural gas pipeline leakage inspection and many other practical applications. To address this requirement, a miniaturized and low-power-consumption gas sensor was developed based on tunable diode laser absorption spectroscopy for standoff CH4 detection with a non-cooperative target. Wavelength modulation spectroscopy with a 1f normalized 2f detection method was employed for calibration-free CH4 measurement. A Kalman filter algorithm was used to improve the precision of the detection. The performance of the standoff CH4 sensor was evaluated comprehensively under various conditions, including different incident angles, different hard topographic targets, and different standoff distances. The results show that the measurement precision is 0.107% and the sensitivity is 4.08 parts per million per meter (ppm·m) with a time resolution of 1 s and a standoff distance of 40 m. The detection limit can achieve 1.24 ppm·m at an optimal integration time of 70 s. This sensor can be easily integrated into mobile platforms, which lays the foundation for intelligent leak inspection.

End-to-end methane gas detection algorithm based on transformer and multi-layer perceptron

In this paper, an end-to-end methane gas detection algorithm based on transformer and multi-layer perceptron (MLP) for tunable diode laser absorption spectroscopy (TDLAS) is presented. It consists of a Transformer-based U-shaped Neural Network (TUNN) filtering algorithm and a concentration prediction network (CPN) based on MLP. This algorithm employs an end-to-end architectural design to extract information from noisy transmission spectra of methane and derive the CH4 concentrations from denoised spectra, without intermediate steps. The results demonstrate the superiority of the proposed TUNN filtering algorithm over other typically employed digital filters. For concentration prediction, the determination coefficient (R2) reached 99.7%. Even at low concentrations, R2 remained notably high, reaching up to 89%. The proposed algorithm results in a more efficient, convenient, and accurate spectral data processing for TDLAS-based gas sensors.

PAS-based analysis of natural gas samples

Photoacoustic spectroscopy (PAS) is well known for the detection of short-chain hydrocarbons, such as methane, ethane and propane, in the ppm (parts per million) or ppb (parts per billion) range. However, in the production process of natural gas and its combustion in gas-fired devices the composition, especially the concentrations of the main alkanes, plays a decisive role. Gas chromatography (GC) is considered the gold standard for natural gas analysis. We present a method to analyze natural gas samples by PAS. Furthermore, we describe a method to prepare storage gas samples, which are usually under atmospheric pressure, for PAS analysis. All measurements are validated by means of GC. The investigation allows conclusions to be drawn to what extent PAS is suitable for the investigation of natural gas samples.

Identification of ground spilled oil of buried pipeline based on laser absorption spectroscopy: Numerical investigation and experimental verification

The ground diffusion characteristics of buried oil pipeline after leakage and the laser optical transmission mechanism of surface oil film are the basis of spectral detection technology. Based on the computational fluid dynamics to solve the diffusion equation of multiphase flow in porous media, the leakage law of oil under different soil porosity is analyzed in two dimensions: surface diffusion diameter and oil film thickness. TracePro optical simulation is used to study the absorption and reflection patterns of laser at the oil-gas interface, and validation experiments are carried out based on the tunable diode laser absorption spectroscopy method. The results show that oil is easily accumulated on the ground surface with larger soil porosity and in the depressions of the ground surface. When the oil film thickness is greater than 2 mm, the laser cannot transmit the oil layer and the received light intensity is only provided by the mirror reflection at the oil-gas interface. The mechanism of laser detection of oil leaks is the spectral absorption of volatile alkane gases in the upper layer of the oil film by a laser of a specific wavelength.

Optical Methods of Methane Detection

Methane is the most frequently analyzed gas with different concentrations ranging from single ppm or ppb to 100%. There are a wide range of applications for gas sensors including urban uses, industrial uses, rural measurements, and environment monitoring. The most important applications include the measurement of anthropogenic greenhouse gases in the atmosphere and methane leak detection. In this review, we discuss common optical methods used for detecting methane such as non-dispersive infrared (NIR) technology, direct tunable diode spectroscopy (TDLS), cavity ring-down spectroscopy (CRDS), cavity-enhanced absorption spectroscopy (CEAS), lidar techniques, and laser photoacoustic spectroscopy. We also present our own designs of laser methane analyzers for various applications (DIAL, TDLS, NIR).

Adaptively Optimized Gas Analysis Model with Deep Learning for Near-Infrared Methane Sensors

Noise significantly limits the accuracy and stability of retrieving gas concentration with the traditional direct absorption spectroscopy (DAS). Here, we developed an adaptively optimized gas analysis model (AOGAM) composed of a neural sequence filter (NSF) and a neural concentration retriever (NCR) based on deep learning algorithms for extraction of methane absorption information from the noisy transmission spectra and obtaining the corresponding concentrations from the denoised spectra. The model was trained on two data sets, including a computationally generated one and the experimental one. We have applied this model for retrieving methane concentration from its transmission spectra in the near-infrared (NIR) region. The NSF was implemented through an encoder-decoder structure enhanced by the attention mechanism, improving robustness under noisy conditions. Further, the NCR was employed based on a combination of a principal component analysis (PCA) layer, which focuses the algorithm on the most significant spectral components, and a fully connected layer for solving the nonlinear inversion problem of the determination of methane concentration from the denoised spectra without manual computation. Evaluation results show that the proposed NSF outperforms widely used digital filters as well as the state-of-the-art filtering algorithms, improving the signal-to-noise ratio by 7.3 dB, and the concentrations determined with the NCR are more accurate than those determined with the traditional DAS method. With the AOGAM enhancement, the optimized methane sensor features precision and stability in real-time measurements and achieves the minimum detectable column density of 1.40 ppm·m (1σ). The promising results of the present study demonstrate that the combination of deep learning and absorption spectroscopy provides a more effective, accurate, and stable solution for a gas monitoring system.

UAV and Its Approach in Oil and Gas Pipeline Leakage Detection

The synergy of vibration and gas sensors with unmanned aerial vehicles for a low-response-time Leakage Detection System (LDS) is explored in this work. Several pipeline accidents have occurred, most of which were triggered by untimely detection of pipe leakages in systems conveying oil and gas in many developing countries. The consequences of this include human casualties, environmental degradation, economic loss, and loss of resources. To limit the damages caused by inevitable leakages, a low-time-response system for leakage detection is required. Response time derived from the LDS is compared to the typical response time obtained from an existing system to determine the efficiency of the developed system. A comparative analysis of the response time of the designed LDS and existing systems reveals that the designed LDS response time is 1146.7% faster and having a pictorial view of the localized area of interest would go a long way to preventing unnecessary mobilization for site visits and eradicating the costly effect of false alarms.