Methane Detection using Optical Gas Imaging: Passive Infrared Technology

The U.S. Environmental Protection Agency (EPA) has promulgated regulations governing the detection and repair of equipment leaks that cause fugitive emissions of volatile organic compounds (VOC). These regulations are embedded in various emission standards and are generally referred to as Leak Detection and Repair (LDAR) programs. The primary method used currently in the U.S. to detect leaks is EPA Method 21.1 Method 21 requires operators to use portable instruments, typically a Flame Ionization Detector (FID) or a Photon Ionization Detector (PID), to "sniff" around the circumference of individual equipment components (e.g., valves, flanges, pump seals, etc.)2. If the detector reading (parts per million or PPM) is higher than target thresholds, the component is deemed to be leaking and it must be repaired within a certain time. Fugitive VOC emissions from a facility are calculated based upon the PPM readings (referred to as screening values or SVs) and empirical correlations between SVs and mass emission rates.2 Because the leak check is performed on each individual component basis, the implementation of a Method 21 based LDAR program is tedious, labor intensive, and prone to errors. Optical gas imaging (OGI) technology has been developed and can be used to detect VOC leaks from process equipment. The OGI technology allows operators to use a specially designed Infrared (IR) video camera to see VOC plumes leaking from components that are not visible to the naked eye. Detecting VOC leaks using OGI is more efficient than Method 21 because leak checking using OGI is visual, making detection faster, and can be performed over an area instead of component-by-component. The OGI method allows operators to detect larger leaks easily and more frequently, achieving the same environmental benefit with a lower cost. For this reason, the OGI method is also referred to as "Smart LDAR". In December 2008, U.S. EPA promulgated the "Alternative Work Practice" (AWP) rule allowing operators to use OGI for LDAR compliance.3 However, the AWP rule requires operators to continue to perform leak checks using Method 21 at least once a year. Although OGI can be very effective in detecting leaks, it does not provide a quantitative measure of leak rate. This has been one of the shortcomings of OGI from a regulatory perspective, thereby hindering its adoption as a true alternative to Method 21. This paper describes development of quantitative OGI (QOGI) technology. Existing OGI camera technology is the basis the new GOCI technology. If an OGI camera detects a leak, then, the operator can apply the new QOGI technology quantify the mass leak rate from the captured video images.

Optical Gas Imaging (OGI) cameras represent an interesting tool for identifying leaking components in hydrocarbon processing and transport systems. They make it possible to see exactly where a leak originates, thereby enabling efficient leak detection and repair (LDAR) programs. The present paper reports on an OGI test campaign initiated by the Norwegian Environmental Agency (NEA), and how this campaign stimulated cross-disciplinary cooperation at an LNG plant for better control of both fugitive hydrocarbon emissions and safety-related leaks. A surprising potentially severe leak detected in the NEA campaign triggered the introduction of in-house OGI cameras at plants and refineries, and an inter-disciplinary cooperation between specialists in the environment, technical safety and operations. Some benefits of in-house OGI cameras, as well as some concerns regarding their use are presented and discussed. The general experience is that an Ex safe, i.e., rated for safe use in a combustible hydrocarbon gas atmosphere, OGI camera, represents a very valuable tool for detecting fugitive emissions as the start point for LDAR programs. An OGI camera did, however, also turn out to be a valuable tool for fire and explosion risk management, and has led to reduced downtime after leak incidents. The concerns relate to leaks seen through the OGI camera that may look overwhelming, even with concentrations well below the ignitable limits of the released gas. Based on the LNG plant experiences, it is generally recommended that specialists in the environment, technical safety, operations and teaching fields cooperate regarding the introduction and use of OGI cameras. Suggestions for training courses are also discussed.

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators.

Fugitive emissions, particularly methane and Volatile Organic Compounds (VOCs), present critical environmental and economic challenges, contributing to climate change and possibly incurring regulatory penalties. A Leak Detection and Repair (LDAR) program is crucial for minimizing fugitive emissions and ensuring the safety and efficiency of these facilities. Chiyoda Corporation is a worldleading engineering company and Engineering, Procurement, and Construction (EPC) contractor for large-scale projects across the globe. In these projects, there are thousands of fugitive emission sources, and using conventional LDAR programs that rely on periodic manual inspections of individual components (e.g., EPA Method 21) has disadvantages, such as being time-consuming, incurring high labor costs, delayed leak identification, and the introduction of potential human data-entry errors. We are developing a smart LDAR technology that achieves real-time, high-accuracy leak detection through the integration of gas leak detection devices, 3D digital twin software, and robotic platforms; optimizes operational costs and reduces downtime through proactive maintenance strategies. It also ensures compliance with evolving LDAR regulations, including US-EPA, EU methane strategy, OGMP 2.0 and API 624 requirements, ensuring effective monitoring and management of methane and other fugitive emissions. The proposed technology combines three core components: (1) gas leak detection sensors—such as Optical Gas Imaging (OGI) cameras and Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors—to detect, quantify, and visually identify gas leaks; (2) 3D digital twin software to develop an LDAR database by incorporating fugitive emission data (component type, location, concentration), enabling prioritized repair scheduling and optimized inspection routing; and (3) robotic platforms for continuous, autonomous monitoring over larger areas, collecting more data points than manual inspections and accessing challenging or hazardous zones for more accurate leak- flux quantification. The first demonstration study of the proposed technology was conducted at Chiyoda Corporation's SPERA Hydrogen demonstration plant in Japan. Results demonstrated the capabilities of the proposed technology for fugitive emission leak detection and quantification through safe and efficient monitoring without requiring proximity to potential emission sources. Future developments aim to integrate an autonomous robot instead of a manually controlled one, enabling continuous, high-precision monitoring, especially in hazardous or hard-to-access industrial environments. Our work will further enhance LDAR efficiency, improves safety, ensures regulatory compliance, and ultimately contributing to environmental sustainability and economic benefits.

This document is the final report to the U.S. Department of Energy (DOE for contract DE-FE0031873) awarded to Colorado State University (CSU). CSU and partners at Harrisburg University of Science and Technology, University of Texas Arlington, and University of Texas at Austin organized several testing rounds to provide knowledge focused on advancing the detection capabilities of emissions monitoring devices. With this funding opportunity the research group began by establishing the Advancing the Development of Emission Detection (ADED) program, with the goal focused on enhancing the accuracy, reliability, and field applicability of methane detection technologies. This effort aimed to address the critical challenges of identifying and quantifying methane emissions while enabling industry stakeholders to meet regulatory compliance and environmental sustainability goals. The program engaged with industry, government, and technology stakeholders to promote adoption and consensus on testing techniques for methane detection solutions. Methane, a potent greenhouse gas, contributes significantly to global warming, and the oil and natural gas (O&G) sector is a primary source of methane emissions. Regulatory measures such as leak detection and repair (LDAR) programs have been implemented to address emissions. However, traditional LDAR approaches, reliant on handheld and component-level measurements, are resource-intensive. To address these limitations and move with evolving regulations, advanced methane technologies are emerging. These solutions include ground-based sensors, mobile systems (e.g., drones, vehicles, and aircraft), and satellite-based platforms. They offer innovative capabilities for autonomous monitoring, larger spatial coverage, and emission quantification using methods such as tracer gas techniques and inverse modeling with Gaussian plume analysis. The ADED program began with creating protocols for methane controlled release (CR) testing these continuous monitoring (CM) and survey technologies that detect and monitor methane emissions at O&G facilities. The protocols were then implemented throughout testing of CM and survey devices at CSU’s Methane Emissions Technology Evaluation Center (METEC) facility from 2021 through 2024. As apart of the protocol, solutions that tested under the ADED program installed their solutions at METEC, documented their system under test, and provided detection reports to the METEC team for analysis. The METEC team would provide the solutions with analyzed reports of their emissions and ground truth data of the releases conducted during their testing session. Under the ADED program, CMs were also tested at O&G facilities for a six week test run of challenge release (ChR) releases. The findings from the ADED program underscore the critical role of collaborative research and innovation in tackling methane emissions, offering a pathway for the oil and gas sector to achieve significant environmental and economic benefits. Results from METEC testing saw improvement of performance and accuracy across all solutions over the extent of the ADED experiments. The results also showed a variance in CM solution performance between CRs and ChRs. That variance pushed the team to further analyze the differences between CR testing environments and field conditions. With the drive from regulations and that variance in field conditions, the ADED team began designing a new CR testing protocol and additions to the METEC testing facility. The METEC team is furthering the progress made through the ADED program with awarded funding from DE-FE0032276. This funding pushes the development of METEC’s addition with new equipment, allowing for an updated facility layout. METEC still facilitates for traditional facilities, with a legacy pad, while expanding an new design based on how O&G infrastructure has Final Report - Contract Number: DE-FE0031873 been changed over the last decade. Throughout the ADED program the team has also been working with international partners to ensure staying in the trend globally. International partners have been essential in moving the new protocol forward to implement into CR testing at the METEC facility in Spring 2025.

Reducing methane emissions from oil and gas systems is a central component of US and international climate policy. Leak detection and repair (LDAR) programs using optical gas imaging (OGI)-based surveys are routinely used to mitigate fugitive emissions or leaks. Recently, new technologies and platforms such as planes, drones, and satellites promise more cost-effective mitigation than existing approaches. To be approved for use in LDAR programs, new technologies must demonstrate emissions mitigation equivalent to existing approaches. In this work, we use the FEAST modeling tool to (a) identify cost vs mitigation trade-offs that arise from using new technologies and (b) provide a framework for effective design of alternative LDAR programs. We identify several critical insights. First, LDAR programs can trade sensitivity for speed without sacrificing mitigation outcomes. Second, low sensitivity or high detection threshold technologies have an effective upper bound on achievable mitigation that is independent of the survey frequency. Third, the cost effectiveness of tiered LDAR programs using site-level detection technologies depends on their ability to distinguish leaks from routine venting. Finally, "technology equivalence" based on mitigation outcomes differs across basins and should be evaluated independently. The FEAST model will enable operators and regulators to systematically evaluate new technologies in next-generation LDAR programs.

Pricing Methane Emissions from Oil and Gas Production

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper IPTC 18471, “New Optical Gas-Imaging Technology for Quantifying Fugitive- Emission Rates,” by Hazem Abdel-Moati, ExxonMobil Research Qatar; Jonathan Morris and Yousheng Zeng, Providence Photonics; Petroula Kangas, ExxonMobil Chemical Europe; and Duane McGregor, ExxonMobil Research and Engineering, prepared for the 2015 International Petroleum Technology Conference, Doha, Qatar, 7–9 December. The paper has not been peer reviewed. Optical gas-imaging (OGI) technology has been developed and can be used to detect leaks of volatile organic compounds (VOCs) from process equipment. Using OGI to detect leaks is more effective than using the US Environmental Protection Agency’s Method 21 because OGI is visual, making detection faster, and can survey an area instead of one component at a time. Although OGI can be very effective in detecting leaks, it does not provide a quantitative measure of leak rate (LR), hindering its adoption as a true alternative to Method 21. This paper describes development of quantitative OGI (QOGI) technology. Method and Preliminary Results Approaches have been proposed to establish a quantitative relationship between the pixel intensity difference with and without a plume (ΔI) and the product of concentration in ppm and path length in meters (ppm·m) for a gas column represented by a pixel in the infrared (IR) image for a given temperature differential (ΔT) between ambient air and the background. This quantitative relationship has been confirmed with a study showing that there is a monotonically increasing relationship between ΔI and concentration for uniform black background that was temperature controlled. That study’s data also showed that ΔI increases as the temperature of the background increases for a specific gas concentration.

<p>The Danish voluntary methane monitoring programme for agricultural biogas producing facilities was launched by the Danish Biogas Association in autumn, 2016. The Danish Biogas Association had set a san overall goal of reducing the total methane loss from Danish biogas and upgrading plants on a national level to less than 1% of production by 2020.</p><p>The Danish Government has allocated approximately 1.3 million € to monitor and reduce methane emissions from the biogas sector in the period 2019-2020, based on previous experiences. The funds are used to administer this new programme, method testing, validation of measurement service providers and to provide partial funding for the participation of biogas plants. Also, the project includes recommendations to the Danish Energy Agency regarding use of measurements in regulation based on the results of the programme. The programme includes agricultural biogas plants (both farm-scale and centralized plants), industrial biogas plants and biogas plants treating sludge from wastewater treatment. The biogas plants provide partial funding. Three levels of own-funding are used depending on the magnitude of gas production. Large biogas plants pay a larger fraction of expenses than smaller plants. This was chosen to ensure participation of smaller facilities.</p><p>At each biogas plant, two or more of the following actions are performed:</p><p>1. Initial agreement on services and expectations</p><p>2. Development of self-monitoring programme</p><p>3. Leakage search and mitigation recommendations</p><p>4. Quantification of methane loss using remote sensing</p><p>5. Draft project of emission reduction initiatives</p><p>Actions 1 and 4 are mandatory for the participating biogas plants. The leakage search (Action 3) is performed using Optical Gas Imaging (OGI). Here, a measurement technician uses OGI to identify leakages both outdoors on tanks, pipes etc., and indoors. The report lists and illustrates leakages (through video documentation) and provides advise on mitigation actions were applicable. Even though OGI is not used quantitatively, the leaks are categorized as low, medium and high emission rates based on the operator’s assessment from the OGI videos.</p><p>Quantification of the biogas plants total methane emission (Action 4) is done using a tracer gas dispersion method. This remote sensing method includes the controlled, continuous release of a tracer gas (C<sub>2</sub>H<sub>2</sub> in this case) combined with downwind, cross-plume measurements of methane and tracer gas. This method is described in Scheutz and Fredenslund (2019). It has been used to quantify emissions from biogas plants in the previous voluntary programme as well as to measure fugitive emissions from other area sources such as landfills, composting facilities and wastewater treatment plants.</p><p>About 60 plants have signed up and measurement and consultation activities are currently ongoing. The plants include about 35 manure-based biogas plants, 2 industrial biogas plants and 22 wastewater treatment plants. All activities are to be completed by the end of February, 2021.</p><p>This presentation will include a short introduction to the programme, observed emission rates and emission factors, typical sources of methane emission, mitigation options, recommendations regarding regulation and economic factors such as expenses for the monitoring activities.</p><p><strong>Reference</strong></p><p>Scheutz, C., Fredenslund, A.M. 2019. Total methane emission rates and losses from 23 biogas plants. Waste Management, 97, 38-46.</p>

Objective: To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection margin (CRM) of rectal cancer in the magnetic resonance imaging (MRI). Methods: This study was registered in the Chinese Clinical Trial Registry (ChiCTR-1800017410). Case inclusion criteria: (1) the positive area of CRM was located between the plane of the levator ani, anal canal and peritoneal reflection; (2) rectal malignancy was confirmed by electronic colonoscopy and histopathological examination; (3) positive CRM was confirmed by postoperative pathology or preoperative high-resolution MRI. Exclusion criteria: patients after neoadjuvant therapy, recurrent cancer after surgery, poor quality images, giant tumor with extensive necrosis and tissue degeneration, and rectal tissue construction changes in previous pelvic surgery. According to the above criteria, MRI plain scan images of 350 patients with rectal cancer and positive CRM in The Affiliated Hospital of Qingdao University from July 2016 to June 2019 were collected. The patients were classified by gender and tumor position, and randomly assigned to the training group (300 cases) and the validation group (50 cases) at a ratio of 6:1 by computer random number method. The CRM positive region was identified on the T2WI image using the LabelImg software. The identified training group images were used to iteratively train and optimize parameters of the Faster R-CNN model until the network converged to obtain the best deep learning model. The test set data were used to evaluate the recognition performance of the artificial intelligence platform. The selected indicators included accuracy, sensitivity, positive predictive value, receiver operating characteristic (ROC) curves, areas under the ROC curves (AUC), and the time taken to identify a single image. Results: The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CRM status determined by the trained Faster R-CNN artificial intelligence approach were 0.884, 0.857, 0.898, 0.807, and 0.926, respectively; the AUC was 0.934 (95% CI: 91.3% to 95.4%). The Faster R-CNN model's automatic recognition time for a single image was 0.2 s. Conclusion: The artificial intelligence model based on Faster R-CNN for the identification and segmentation of CRM-positive MRI images of rectal cancer is established, which can complete the risk assessment of CRM-positive areas caused by in-situ tumor invasion and has the application value of preliminary screening.

During rainy times, the impact of outdoor vision systems gets considerably decreased owing to the visibility barrier, distortion, and blurring instigated by raindrops. So, it is essential to eradicate it from the rainy images for ensuring the reliability of outdoor vision system. To achieve this, several rain removal studies have been performed in recent days. In this view, this paper presents a new Faster Region Convolutional Neural Network (Faster RCNN) with Optimal Densely Connected Networks (DenseNet)-based rain removal technique called FRCNN-ODN. The presented involves weighted mean filtering (WMF) is applied as a denoising technique, which helps to boost the quality of the input image. In addition, Faster RCNN technique is used for rain detection that comprises region proposal network (RPN) and Fast RCNN model. The RPN generates high quality region proposals that are exploited by the Faster RCNN to detect rain drops. Also, the DenseNet model is utilized as a baseline network to generate the feature map. Moreover, sparrow search optimization algorithm (SSOA) is applied to choose the hyperparameters of the DenseNet model namely learning rate, batch size, momentum, and weight decay. An extensive experimental validation process is performed to highlight the effectual outcome of the FRCNN-ODN model and investigated the results with respect to several dimensions. The FRCNN-ODN method produced a higher UIQI of 0.981 for the applied image 1. Furthermore, on the applied image 2, the FRCNN-ODN model achieved a maximum UIQI of 0.982. Furthermore, the FRCNN-ODN algorithm produced a higher UIQI of 0.998 on the applied image 3. The simulation outcome showcased the superior outcome of the FRCNN-ODN (Optimal Densely Connected Networks) model with existing methods in terms of distinct measures.

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Industri kopi global sangat dipengaruhi oleh peran masyarakat pecinta kopi, yang menciptakan ekosistem dimana kopi tetap menjadi komoditas istimewa. Salah satu aspek penting dalam pengelolaan kopi adalah penentuan grade kopi. Standar penilaian cacat pada biji kopi telah diakui secara nasional sejak tahun 1984 dan diperbarui dengan SNI 01-2907-2008 untuk memastikan kualitas dan konsistensi. Untuk meningkatkan efisiensi dan akurasi proses grading kopi, diperlukan sistem otomatis berbasis deep learning. Penelitian ini membahas penerapan CNN dan Faster R-CNN untuk klasifikasi biji kopi, dengan menggunakan biji kopi arabika, robusta, dan liberica. Algoritma CNN VGG-16 dan Faster R-CNN dibandingkan untuk mengidentifikasi pengaruh Region Proposal Network (RPN) terhadap efisiensi klasifikasi citra. Hasil penelitian menunjukkan bahwa CNN VGG-16 mencapai akurasi 86%, sementara Faster R-CNN mencapai rata-rata akurasi 93%, dengan presisi 93%, recall 92%, dan skor F1 92%. Penelitian ini bertujuan untuk memberikan pemahaman mendalam tentang kedua algoritma dalam konteks klasifikasi biji kopi serta meningkatkan efisiensi proses grading kopi. Industri kopi global sangat dipengaruhi oleh peran masyarakat pecinta kopi, yang menciptakan ekosistem dimana kopi tetap menjadi komoditas istimewa. Salah satu aspek penting dalam pengelolaan kopi adalah penentuan grade kopi. Standar penilaian cacat pada biji kopi telah diakui secara nasional sejak tahun 1984 dan diperbarui dengan SNI 01-2907-2008 untuk memastikan kualitas dan konsistensi. Untuk meningkatkan efisiensi dan akurasi proses grading kopi, diperlukan sistem otomatis berbasis deep learning. Penelitian ini membahas penerapan CNN dan Faster R-CNN untuk klasifikasi biji kopi, dengan menggunakan biji kopi arabika, robusta, dan liberica. Algoritma CNN VGG-16 dan Faster R-CNN dibandingkan untuk mengidentifikasi pengaruh Region Proposal Network (RPN) terhadap efisiensi klasifikasi citra. Hasil penelitian menunjukkan bahwa CNN VGG-16 mencapai akurasi 86%, sementara Faster R-CNN mencapai rata-rata akurasi 93%, dengan presisi 93%, recall 92%, dan skor F1 92%. Penelitian ini bertujuan untuk memberikan pemahaman mendalam tentang kedua algoritma dalam konteks klasifikasi biji kopi serta meningkatkan efisiensi proses grading kopi.

VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera

There is an opportunity to reduce methane emissions from Australian, open pit, metallurgical coal (MC) mines and substantially reduce the carbon intensity of a high-value Australian export industry. MC is critical to the production of steel products and is essential to meet international urbanisation and energy system transformation goals. Initial research on this topic, outlined the application of subterranean barriers in coal mining towards the reduction of gas influx and whole-of-life coal mine methane (CMM) emissions in underground applications. Initially, the technology lacked economic viability for a majority of cases and especially after carbon pricing was eliminated in 2014. Today, with the imperative for the industry to reduce emissions, the technology becomes significant in its potential for emissions reduction for both underground and open-cut coal mines.The basis of barriers is similar to naturally occurring barriers (i.e., igneous intrusions or dikes) that have shown effective separation of drained and undrained mine works (as observed in adjoining mines in Central Queensland). Barriers have also been applied in the environmental, civil engineering and oil and gas industries, to manage or restrict subterranean flow patterns or contain contaminants. In this case, subterranean barriers are being implemented in conjunction with mine pre-drainage to reduce methane emissions from an open pit, MC mine. In addition, barriers can be used in underground mining operations.to improve mine pre-drainage and safety as well as reduce methane emissions.This paper describes the design, execution, and evaluation workflows and the relative importance of variables required for a barrier implementation in subterranean applications for open-cut and underground mining applications. This paper details the results of ongoing planning and modelling to implement and assess a barrier application for reducing gas migration from unmined in-seam or underground mine sections into open-cut MC mining operations. Finally, this paper builds on previous research and available technologies and is complementary to current pre-drainage for surface or underground mining operations. Barrier implementation in conjunction with the beneficial use of gas provides a working framework to reduce mine emissions toward necessary methane reductions by 2030.