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Machine vision for natural gas methane emissions detection using an infrared camera

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  • Wang, Jingfan
  • Tchapmi, Lyne P.
  • Ravikumar, Arvind P.
  • McGuire, Mike
  • Bell, Clay S.
  • Zimmerle, Daniel
  • Savarese, Silvio
  • Brandt, Adam R.

Abstract

In a climate-constrained world, it is crucial to reduce natural gas methane emissions, which can potentially offset the climate benefits of replacing coal with gas. Optical gas imaging (OGI) is a widely-used method to detect methane leaks, but is labor-intensive and cannot provide leak detection results without operators’ judgment. In this paper, we develop a computer vision approach for OGI-based leak detection using convolutional neural networks (CNN) trained on methane leak images to enable automatic detection. First, we collect ∼1 M frames of labeled videos of methane leaks from different leaking equipment, covering a wide range of leak sizes (5.3–2051.6 g CH4/h) and imaging distances (4.6–15.6 m). Second, we examine different background subtraction methods to extract the methane plume in the foreground. Third, we then test three CNN model variants, collectively called GasNet, to detect plumes in videos. We assess the ability of GasNet to perform leak detection by comparing it to a baseline method that uses an optical-flow based change detection algorithm. We explore the sensitivity of results to the CNN structure, with a moderate-complexity variant performing best across distances. The generated detection probability curves show that the detection accuracy (fraction of leak and non-leak images correctly identified by the algorithm) can reach as high as 99%, the overall detection accuracy can exceed 95% across all leak sizes and imaging distances. Binary detection accuracy exceeds 97% for large leaks (∼710 g CH4/h) imaged closely (∼5–7 m). The GasNet-based computer vision approach could be deployed in OGI surveys for automatic vigilance of methane leak detection with high accuracy in the real world.

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  • Wang, Jingfan & Tchapmi, Lyne P. & Ravikumar, Arvind P. & McGuire, Mike & Bell, Clay S. & Zimmerle, Daniel & Savarese, Silvio & Brandt, Adam R., 2020. "Machine vision for natural gas methane emissions detection using an infrared camera," Applied Energy, Elsevier, vol. 257(C).
  • Handle: RePEc:eee:appene:v:257:y:2020:i:c:s030626191931685x
    DOI: 10.1016/j.apenergy.2019.113998
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    as
    1. Chaudry, Modassar & Jenkins, Nick & Qadrdan, Meysam & Wu, Jianzhong, 2014. "Combined gas and electricity network expansion planning," Applied Energy, Elsevier, vol. 113(C), pages 1171-1187.
    2. Dollevoet, T.A.B. & van Essen, J.T. & Glorie, K.M., 2017. "Solution methods for the tray optimization problem," Econometric Institute Research Papers EI2017-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Eleutério F. S. Prado, 2017. "Moseley on Marx’s Method," International Journal of Political Economy, Taylor & Francis Journals, vol. 46(1), pages 29-34, January.
    4. Paola Giuliano & Nathan Nunn, 2021. "Understanding Cultural Persistence and Change [Cultural Assimilation During the Age of Mass Migration]," Review of Economic Studies, Oxford University Press, vol. 88(4), pages 1541-1581.
    5. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    6. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    7. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    8. Shikhman, V. & Nesterov, Yu. & Ginsburgh, V., 2018. "Power method tâtonnements for Cobb–Douglas economies," Journal of Mathematical Economics, Elsevier, vol. 75(C), pages 84-92.
    9. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    10. Qadrdan, Meysam & Chaudry, Modassar & Jenkins, Nick & Baruah, Pranab & Eyre, Nick, 2015. "Impact of transition to a low carbon power system on the GB gas network," Applied Energy, Elsevier, vol. 151(C), pages 1-12.
    11. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    12. Magnus Gålfalk & Göran Olofsson & Patrick Crill & David Bastviken, 2016. "Making methane visible," Nature Climate Change, Nature, vol. 6(4), pages 426-430, April.
    13. Tom Wigley, 2011. "Coal to gas: the influence of methane leakage," Climatic Change, Springer, vol. 108(3), pages 601-608, October.
    14. Qiao, Zheng & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Liu, Yuquan & Xiong, Wen, 2017. "An interval gas flow analysis in natural gas and electricity coupled networks considering the uncertainty of wind power," Applied Energy, Elsevier, vol. 201(C), pages 343-353.
    15. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
    16. Souza, P.V.S. & Girardi, D. & de Oliveira, P.M.C., 2017. "Drag force in wind tunnels: A new method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 120-128.
    17. ., 2017. "Methodological issues," Chapters, in: An Autecological Theory of the Firm and its Environment, chapter 6, Edward Elgar Publishing.
    18. Martin D. D. Evans & Richard K. Lyons, 2017. "Understanding Order Flow," World Scientific Book Chapters, in: Studies in Foreign Exchange Economics, chapter 13, pages 507-546, World Scientific Publishing Co. Pte. Ltd..
    19. ., 2017. "Overview of the Anker living wage methodology," Chapters, in: Living Wages Around the World, chapter 2, pages 17-30, Edward Elgar Publishing.
    20. Mongibello, Luigi & Bianco, Nicola & Caliano, Martina & Graditi, Giorgio, 2016. "Comparison between two different operation strategies for a heat-driven residential natural gas-fired CHP system: Heat dumping vs. load partialization," Applied Energy, Elsevier, vol. 184(C), pages 55-67.
    21. Zhang, Xiaochun & Myhrvold, Nathan P. & Hausfather, Zeke & Caldeira, Ken, 2016. "Climate benefits of natural gas as a bridge fuel and potential delay of near-zero energy systems," Applied Energy, Elsevier, vol. 167(C), pages 317-322.
    22. Touretzky, Cara R. & McGuffin, Dana L. & Ziesmer, Jena C. & Baldea, Michael, 2016. "The effect of distributed electricity generation using natural gas on the electric and natural gas grids," Applied Energy, Elsevier, vol. 177(C), pages 500-514.
    23. Spoladore, Alessandro & Borelli, Davide & Devia, Francesco & Mora, Flavio & Schenone, Corrado, 2016. "Model for forecasting residential heat demand based on natural gas consumption and energy performance indicators," Applied Energy, Elsevier, vol. 182(C), pages 488-499.
    24. Anonymous, 2017. "Debating Methodology in Business History," Business History Review, Cambridge University Press, vol. 91(3), pages 443-455, October.
    25. Chiang, Nai-Yuan & Zavala, Victor M., 2016. "Large-scale optimal control of interconnected natural gas and electrical transmission systems," Applied Energy, Elsevier, vol. 168(C), pages 226-235.
    26. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
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    Cited by:

    1. Shuo Sun & Linwei Ma & Zheng Li, 2021. "Methane Emission Estimation of Oil and Gas Sector: A Review of Measurement Technologies, Data Analysis Methods and Uncertainty Estimation," Sustainability, MDPI, vol. 13(24), pages 1-29, December.
    2. Mark Agerton & Ben Gilbert & Gregory B. Upton Jr., 2021. "The Economics of Natural Gas Venting, Flaring and Leaking in U.S. Shale: An Agenda for Research and Policy," Working Papers 2021-02, Colorado School of Mines, Division of Economics and Business.
    3. Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
    4. Titchener, James & Millington-Smith, Doug & Goldsack, Chris & Harrison, George & Dunning, Alexander & Ai, Xiao & Reed, Murray, 2022. "Single photon Lidar gas imagers for practical and widespread continuous methane monitoring," Applied Energy, Elsevier, vol. 306(PB).
    5. Wang, Jingfan & Ji, Jingwei & Ravikumar, Arvind P. & Savarese, Silvio & Brandt, Adam R., 2022. "VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera," Energy, Elsevier, vol. 238(PB).

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