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Emission Quantification via Passive Infrared Optical Gas Imaging: A Review

Author

Listed:
  • Ruiyuan Kang

    (Department of Mechanical Engineering, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Panos Liatsis

    (Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Dimitrios C. Kyritsis

    (Research and Innovation Center on CO 2 and Hydrogen, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

Abstract

Passive infrared optical gas imaging (IOGI) is sensitive to toxic or greenhouse gases of interest, offers non-invasive remote sensing, and provides the capability for spatially resolved measurements. It has been broadly applied to emission detection, localization, and visualization; however, emission quantification is a long-standing challenge for passive IOGI. In order to facilitate the development of quantitative IOGI, in this review, we summarize theoretical findings suggesting that a single pixel value does not provide sufficient information for quantification and then we proceed to collect, organize, and summarize effective and potential methods that can support IOGI to quantify column density, concentration, and emission rate. Along the way, we highlight the potential of the strong coupling of artificial intelligence (AI) with quantitative IOGI in all aspects, which substantially enhances the feasibility, performance, and agility of quantitative IOGI, and alleviates its heavy reliance on prior context-based knowledge. Despite progress in quantitative IOGI and the shift towards low-carbon/carbon-free fuels, which reduce the complexity of quantitative IOGI application scenarios, achieving accurate, robust, convenient, and cost-effective quantitative IOGI for engineering purposes, interdisciplinary efforts are still required to bring together the evolution of imaging equipment. Advanced AI algorithms, as well as the simultaneous development of diagnostics based on relevant physics and AI algorithms for the accurate and correct extraction of quantitative information from infrared images, have thus been introduced.

Suggested Citation

  • Ruiyuan Kang & Panos Liatsis & Dimitrios C. Kyritsis, 2022. "Emission Quantification via Passive Infrared Optical Gas Imaging: A Review," Energies, MDPI, vol. 15(9), pages 1-32, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3304-:d:806963
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    References listed on IDEAS

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    1. Ögren, Yngve & Tóth, Pál & Garami, Attila & Sepman, Alexey & Wiinikka, Henrik, 2018. "Development of a vision-based soft sensor for estimating equivalence ratio and major species concentration in entrained flow biomass gasification reactors," Applied Energy, Elsevier, vol. 226(C), pages 450-460.
    2. Chen, Junghui & Chan, Lester Lik Teck & Cheng, Yi-Cheng, 2013. "Gaussian process regression based optimal design of combustion systems using flame images," Applied Energy, Elsevier, vol. 111(C), pages 153-160.
    3. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
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    Cited by:

    1. Guangxu Li & Lingyu Wang & Jie Hu, 2023. "Integration with Visual Perception—Research on the Usability of a Data Visualization Interface Layout in Zero-Carbon Parks Based on Eye-Tracking Technology," Sustainability, MDPI, vol. 15(14), pages 1-14, July.

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