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Volume 14
Issue 22
10.3390/app142210508
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Article

Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions

by
José Antonio Hernandez-Torres
1,
Juan P. Torreglosa
1,*,
Reyes Sanchez-Herrera
1,
Aldo Bischi
2 and
Andrea Baccioli
2
1
Departament of Electrical, Thermal, Design and Projects Engineering, Escuela Técnica Superior de Ingeniería, Universidad de Huelva, Avda. de las Fuerzas Armadas s/n, 21007 Huelva, Spain
2
Department of Energy, Systems, Territory and Construction, DESTEC, Universitá di Pisa, Via Carlo Francesco Gabba 22, 56122 Pisa, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(22), 10508; https://doi.org/10.3390/app142210508
Submission received: 4 September 2024 / Revised: 31 October 2024 / Accepted: 12 November 2024 / Published: 14 November 2024
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Abstract

Accurate dew point estimation is crucial for measuring water condensation in various fields such as environmental studies, agronomy, or water harvesting, among others. Despite the numerous models and equations developed over time, including empirical and machine learning approaches, they often involve trade-offs between accuracy, simplicity, and computational cost. A major limitation of the current approaches is the lack of balance among these three factors, limiting their practical applications under diverse conditions. This research addresses these key challenges by developing a new, streamlined equation for dew point estimation. Using the Magnus–Tetens equation, deemed as the most reliable equation, as a benchmark, and by applying a process of non-linear regression fitting and parametric optimization, a new equation was derived. The results demonstrate high accuracy with a streamlined implementation, validated through extensive data and computational simulations. This study highlights the importance of accurate dew point modeling, especially under variable environmental conditions, provides a reliable solution to existing limitations, paving the way for enhanced efficiency in related processes and research endeavors, and offers researchers and practitioners a practical tool for more effective modeling of water condensation phenomena.
Keywords: dew point estimation; water condensation modeling; parametric optimization dew point estimation; water condensation modeling; parametric optimization

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MDPI and ACS Style

Hernandez-Torres, J.A.; Torreglosa, J.P.; Sanchez-Herrera, R.; Bischi, A.; Baccioli, A. Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions. Appl. Sci. 2024, 14, 10508. https://doi.org/10.3390/app142210508

AMA Style

Hernandez-Torres JA, Torreglosa JP, Sanchez-Herrera R, Bischi A, Baccioli A. Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions. Applied Sciences. 2024; 14(22):10508. https://doi.org/10.3390/app142210508

Chicago/Turabian Style

Hernandez-Torres, José Antonio, Juan P. Torreglosa, Reyes Sanchez-Herrera, Aldo Bischi, and Andrea Baccioli. 2024. "Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions" Applied Sciences 14, no. 22: 10508. https://doi.org/10.3390/app142210508

APA Style

Hernandez-Torres, J. A., Torreglosa, J. P., Sanchez-Herrera, R., Bischi, A., & Baccioli, A. (2024). Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions. Applied Sciences, 14(22), 10508. https://doi.org/10.3390/app142210508

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