Evaluation of Heat Losses Using 2D Thermographic Image Processing
Keywords:
thermal performance, infrared thermography, heat transfer, Image processing
Abstract
Assessing thermal performance is crucial for energy management and diagnostics in industry. This paper introduces an innovative method to quantify heat losses from free convection and radiation in industrial systems using infrared thermography (IRT), applied to the top section of a distillation column in a rum
distillery. The main contribution is an approach that calculates energy losses based on an optimized frequency analysis of two-dimensional (2D) thermographic images. The method employs the advanced Otsu multi-threshold algorithm for digital processing of IRT images. This process extracts the frequency distribution of surface temperatures for any geometry. The resulting frequency histogram is integrated to calculate the total heat loss, modeling the combined contribution of each pixel as a heat transfer element, scaled according to the real dimensions of the surface under study. The results demonstrate the effectiveness of 2D IRT digital image processing for this purpose. A comparison with conventional calculation methods revealed a difference of approximately 14% in the heat loss value, validating the potential of this new approach for achieving greater accuracy in determining thermal losses. In conclusion, the synergy between thermographic image processing and heat transfer equations constitutes a robust and precise tool with broad potential for evaluating heat transport phenomena in industrial settings.
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2. GUAN, Hang, et al. Automatic fault diagnosis algorithm for hot water pipes based on infrared thermal images. Building and environment, 2022, 218, p. 109111. [Consultado 15 enero 2026]. https://www.sciencedirect.com/science/article/pii/S0360132322003481
3. SCHMOLL, Robert, et al. Method and experimental investigation of surface heat dissipation measurement using 3D thermography. Journal of Sensors and Sensor Systems, 2022, 11(1), p. 41-49. [Consultado 3 febrero 2026]. https://jsss.copernicus.org/articles/11/41/2022/
4. SCHRAMM, Sebastian, et al. Target analysis for the multispectral geometric calibration of cameras in visual and infrared spectral range. IEEE Sensors Journal,
2020, 21(2), p. 2159-2168. [Consultado 5 enero 2026]. https://ieeexplore.ieee.org/abstract/document/9178752/
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6. NETO, Gerardo Aurélio Cruz, et al. Infrared thermography as a non-invasive method to quantify the heat stress response in weaned piglets after road transport in a semi-arid region. International Journal of Biometeorology, 2025, 69(3), p. 633-642. [Consultado 21 diciembre 2025]. https://link.springer.com/article/10.1007/s00484-024-02844-w
7. LIANG, Xiaolong, et al. Advancing railway infrastructure maintenance: Thermodynamic parameter inversion of ballast bed and feasibility assessment of fouling detection via infrared thermography (IRT). Infrared Physics & Technology, 2024, 141, p. 105398. [Consultado 11 enero 2026]. https://www.sciencedirect.com/science/article/pii/S1350449524002822
8. ZHANG, Dongjie, et al. An in-situ detection method for assessing the thermal transmittance of building exterior walls using unmanned aerial vehicle–infrared
thermography (UAV-IRT). Journal of Building Engineering, 2024, 91, p. 109724. [Consultado 3 febrero 2026]. https://www.sciencedirect.com/science/article/pii/S2352710224012920
9. PEACOK, Thayset Marino, et al. Infrared thermography: A new approach for the characterization and management of activated carbons applied in water treatment. Chemical Engineering Science, 2021, 246, p. 116881. [Consultado 3 enero 2026]. https://www.sciencedirect.com/science/article/pii/S0009250921004462
10. RAZMARA, Parsa; KHEZRESMAEILZADEH, Tina; JENKINS, B. Keith. Fever detection with infrared thermography: Enhancing accuracy through machine learning techniques. En 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2024. p. 1-8. [Consultado 23 febrero 2026]. https://ieeexplore.ieee.org/abstract/document/10913591/
11. PARENTE, Claudio; PEPE, Massimiliano. Benefit of the integration of visible and thermal infrared images for the survey and energy efficiency analysis in the
construction field. Journal of Applied Engineering Science, 2019, 17( 4) [Consultado 13 enero 2026]. https://aseestant.ceon.rs/index.php/jaes/article/view/22080
12. YOUSUF, Adeel; KHAWAJA, Hassan; VIRK, Muhammad S. A review of infrared thermography applications for ice detection and mitigation. Cold Regions
Science and Technology, 2024, 218, p. 104058. [Consultado 11 enero 2026]. https://www.sciencedirect.com/science/article/pii/S0165232X23002896
13. SADHUKHAN, Debanjan, et al. Estimating surface temperature from thermal imagery of buildings for accurate thermal transmittance (U-value): A machine learning perspective. Journal of Building Engineering, 2020, 32, p. 101637. [Consultado 23 diciembre 2025].
14. TANDA, Giovanni; MIGLIAZZI, Mauro. Infrared thermography monitoring of solar photovoltaic systems: A comparison between UAV and aircraft remote
sensing platforms. Thermal Science and Engineering Progress, 2024, 48, p. 102379. [Consultado 18 enero 2026]. https://www.sciencedirect.com/science/article/pii/S2451904923007321
15. POZZER, Sandra, et al. Passive infrared thermography for subsurface delamination detection in concrete infrastructure: Inference on minimum requirements. Computers & Structures, 2024, 305, p. 107529. [Consultado 5 diciembre 2025]. 16. PATHAK, P., et al. Examining infrared thermography based approaches to rapid fatigue characterization of additively manufactured compression molded short fiber thermoplastic composites. Composite Structures, 2025, 351, p. 118610. [Consultado 8 enero 2026]. https://www.sciencedirect.com/science/article/pii/S0263822324007384.
17. KAPLAN, Herbert. Practical applications of infrared thermal sensing and imaging equipment. SPIE press, 2007. [Consultado 3 febrero 2026].
https://books.google.com/bookshl=es&lr=&id=vuJ8dalstkC&oi=fnd&pg=PR13&dq=KAPLAN,+Herbert.+Practical+applications+of+infrared+thermal+sensing+and+imaging+equipment.+SPIE+press,+2007.+&ots=A6LCAJwDSz&sig=ld7CbOiLPdZTM6oMtqzk6zkRZAs
18. UKIWE, Ezechukwu Kalu, et al. Deep learning model for detection of hotspots using infrared thermographic images of electrical installations. Journal of Electrical Systems and Information Technology, 2024, 11(1), p. 24. [Consultado 9 diciembre 2025]. https://link.springer.com/article/10.1186/s43067-024-00148-y
19. FLIR Camera [Internet]. [Consultado 12 junio 2024]. https://www.manualslib.com/manual/2798538/Flir-A6-Series.html
20. FLIR T. Try out ResearchIR Software [Internet]. [Consultado 3 junio 2025]. https://www.flir.com/support-center/instruments2/try-out-researchir-software/
21. Portable multifunction PCE-MMK [Internet]. [Consultado 15 junio 2024]. https://www.pce-instruments.com/espanol/instrumento-medida/medidor/medidor-
de-humedad-de-papel-pce-instruments-medidor-de-humedad-de-papel-pce-mmk-1-det_4066075.htm
22. ZHENG, Jianfeng, et al. OTSU multi-threshold image segmentation based on improved particle swarm algorithm. Applied sciences, 2022, 12(22), p. 11514. [Consultado 20 noviembre 2025]. https://www.mdpi.com/2076-3417/12/22/11514
23. GUO, Yanmin, et al. Otsu multi-threshold image segmentation based on adaptive double-mutation differential evolution. Biomimetics, 2023, 8(5), p. 418. [Consultado 13 enero 2026]. https://www.mdpi.com/2313-7673/8/5/418
24. PENG, Zhongbo, et al. Multi-threshold image segmentation of 2D OTSU inland ships based on improved genetic algorithm. Plos one, 2023, 18(8), p. e0290750.
[Consultado 9 diciembre 2025]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0290750
25. INCROPERA, Frank P.; DEWITT, David P. Fundamentos de transferencia de calor. Pearson Educación, 1999.
26. RAMOS, Julio Lázaro, et al. Heat-loss measurement using infrared thermography by multi-threshold analysis. Applied Thermal Engineering, 2025, 279, p. 127433. [Consultado 22 enero 2026]. https://www.sciencedirect.com/science/article/pii/S1359431125020253
Published
2026-06-06
How to Cite
Ramos Martínez, J., González-Díaz, Y., Crespo Sariol, H., & Cambara González, D. (2026). Evaluation of Heat Losses Using 2D Thermographic Image Processing. Chemical Technology, 46, 178-198. Retrieved from https://tecnologiaquimica.uo.edu.cu/index.php/tq/article/view/5537
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