Wet cooling tower performance prediction in CSP plants: A comparison between artificial neural networks and Poppe’s model

Juan Miguel Serrano, Pedro Navarro, Javier Ruiz, Patricia Palenzuela, Manuel Lucas and Lidia Roca

Energy, 2024, vol. 303, issue C

Abstract: The efficiency of Concentrated Solar Power (CSP) plants strongly depends on steam condensation temperatures. Current cooling systems, either wet (water-cooled) or dry (air-cooled), present trade-offs. Wet cooling towers (WCT) optimize performance but raise concerns due to substantial water usage, especially in water-scarce prone locations of CSP plants. Dry cooling conserves water but sacrifices efficiency, specially during high ambient temperatures, coinciding with peak electricity demand. A potential compromise is a combined cooling system, integrating wet and dry methods, offering lower water consumption, improved efficiency and flexibility. Incorporating such systems into CSP plants is of considerable interest, aiming to optimize operations under diverse conditions.

Keywords: Concentrated solar power; Cooling system; Modelling; Neural networks; Sensitivity analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:303:y:2024:i:c:s0360544224016177

DOI: 10.1016/j.energy.2024.131844

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