Emission Quantification via Passive Infrared Optical Gas Imaging: A Review

Ruiyuan Kang, Panos Liatsis and Dimitrios C. Kyritsis
Additional contact information
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

Energies, 2022, vol. 15, issue 9, 1-32

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.

Keywords: infrared optical gas imaging (IOGI); emission quantification; artificial intelligence (AI) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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