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Conference object . 2019
https://doi.org/10.1063/1.5117...
Article . 2019 . Peer-reviewed
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Modelling the soiling rate: Dependencies on meteorological parameters

Authors: Wolfertstetter, Fabian; Wilbert, Stefan; Terhag, Felix; Hanrieder, Natalie; Fernandez-Garcia, Aranzazu; Sansom, Christopher; King, Peter; +2 Authors

Modelling the soiling rate: Dependencies on meteorological parameters

Abstract

Concentrating solar power (CSP) plants are often located in dusty environments. Soiling depends strongly on location, time, weather conditions and mirror orientation and is characterized by the soiling rate: the loss of the specular reflectance due to soiling per time interval. The average soiling rate can reach 2%/day on sites with heavy dust loads such as the Arabian Peninsula. On some days (for example during a sandstorm) the soiling rate can be significantly higher than that. Measurement campaigns for the soiling rate are of interest for the CSP plant site selection and the plant design, but they are time consuming and costly. In this study, a soiling model is presented that describes particle deposition processes based on physical equations from where the soiling rate can be derived. The model uses easily measureable meteorological parameters such as aerosol particle number concentration, wind speed and direction at 10 m height, relative humidity, temperature and precipitation as input parameters. The model has been optimized and validated using measurement data from two sites in Morocco and Spain. The measurement data have been divided into two parts. One was used to find optimum model parameters by parameterization. The second dataset was used to validate the model. The model reaches a bias of 0.1%/d and a root mean square deviation of 0.4 %/d. Days with weak soiling (<1%/d) rates are identified with an accuracy of more than 90 %, the question whether or not the soiling rate is above 1%/d is answered correctly in 85 % of the cases.

Country
Germany
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Keywords

parabolic trough, reinforced learning, CSP, soiling, cleaning optimization, Qualifizierung, ANN

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Top 10%
Top 10%
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