RT Journal Article
T1 Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions
A1 Hernández Torres, José Antonio
A1 Pérez Torreglosa, Juan
A1 Sánchez Herrera, María Reyes
A1 Bischi, Aldo
A1 Baccioli, Andrea
AB Accurate dew point estimation is crucial for measuring water condensation in various fieldssuch as environmental studies, agronomy, or water harvesting, among others. Despite the numerousmodels and equations developed over time, including empirical and machine learning approaches,they often involve trade-offs between accuracy, simplicity, and computational cost. A major limitationof the current approaches is the lack of balance among these three factors, limiting their practicalapplications under diverse conditions. This research addresses these key challenges by developing anew, streamlined equation for dew point estimation. Using the Magnus–Tetens equation, deemedas the most reliable equation, as a benchmark, and by applying a process of non-linear regressionfitting and parametric optimization, a new equation was derived. The results demonstrate highaccuracy with a streamlined implementation, validated through extensive data and computationalsimulations. This study highlights the importance of accurate dew point modeling, especially undervariable environmental conditions, provides a reliable solution to existing limitations, paving theway 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.
PB MDPI
SN 2076-3417 (electrónico)
YR 2024
FD 2024-11
LK https://hdl.handle.net/10272/24556
UL https://hdl.handle.net/10272/24556
LA eng
NO 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. In Applied Sciences (Vol. 14, Issue 22, p. 10508). MDPI AG. https://doi.org/10.3390/app142210508
NO This work was supported by the project entitled “Renewable energies for Africa: Effectivevalorization of agri-food wastes (REFFECT AFRICA)”. This project has received funding from theEuropean Union’s Horizon 2020 Research and Innovation programme under the Grant Agreementnumber 101036900.
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026