Daily reflectance measurements on mirrors used in CSP applications were obtained over a period of 4 months to monitor soiling as a function of meteorological and environmental parameters. The mirrors were placed at 45° increments from face down to face up, and the parameters monitored included temperature, relative humidity, wind speed, rainfall and particulate matter. In order to determine a relationship between the input parameters and the soiling, both multiple linear regression and artificial neural network models were employed. The feed-forward back-propagation neural network with two layers and 16 neurons per layer is the configuration that reaches the best predictive results without overfitting the data, reaching a correlation coefficient of 0.84, compared to a maximum correlation coefficient of 0.60 with the multiple linear regression allowing for quadratic interaction of terms. The model is used to predict the number of annual cleaning operations required for a CSP plant placed in a location with a similar climactic profile, in order to estimate the cleaning contribution to the annual operation and maintenance cost.
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11 December 2020
SOLARPACES 2019: International Conference on Concentrating Solar Power and Chemical Energy Systems
1–4 October 2019
Daegu, South Korea
Research Article|
December 11 2020
Characterization of mirror soiling in CSP applications
Aristides M. Bonanos;
Aristides M. Bonanos
a)
1
Energy Environment and Water Research Center, The Cyprus Institute
, 20 Kavafi Str, Nicosia, 2121, Cyprus
a)Corresponding author: a.bonanos@cyi.ac.cy
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Manuel J. Blanco;
Manuel J. Blanco
b)
1
Energy Environment and Water Research Center, The Cyprus Institute
, 20 Kavafi Str, Nicosia, 2121, Cyprus
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Kypros Milidonis
Kypros Milidonis
c)
1
Energy Environment and Water Research Center, The Cyprus Institute
, 20 Kavafi Str, Nicosia, 2121, Cyprus
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AIP Conf. Proc. 2303, 030007 (2020)
Citation
Aristides M. Bonanos, Manuel J. Blanco, Kypros Milidonis; Characterization of mirror soiling in CSP applications. AIP Conf. Proc. 11 December 2020; 2303 (1): 030007. https://doi.org/10.1063/5.0028528
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