CERN Accelerating science

Article
Title	Machine learning discovery of cost-efficient dry cooler designs for concentrated solar power plants
Author(s)	Narasiah, Hansley (University Coll. London) ; Kitouni, Ouail (MIT) ; Scorsoglio, Andrea (Arizona U.) ; Sturdza, Bernd K (Oxford U.) ; Hatcher, Shawn (Mississippi State U.) ; Katcher, Kelsi (Texas U., San Antonio) ; Khalesi, Javad (North Carolina U., Charlotte) ; Garcia, Dolores (CERN) ; Kusner, Matt J (University Coll. London)
Publication	2024.
Number of pages	11 p
In:	Sci. Rep. 14 (2024) 19086
DOI	10.1038/s41598-024-67346-6
Subject category	Physics in General
Abstract	Concentrated solar power (CSP) is one of the few sustainable energy technologies that offers day-to-night energy storage. Recent development of the supercritical carbon dioxide (sCO2) Brayton cycle has made CSP a potentially cost-competitive energy source. However, as CSP plants are most efficient in desert regions, where there is high solar irradiance and low land cost, careful design of a dry cooling system is crucial to make CSP practical. In this work, we present a machine learning system to optimize the factory design and configuration of a dry cooling system for an sCO2 Brayton cycle CSP plant. For this, we develop a physics-based simulation of the cooling properties of an air-cooled heat exchanger. The simulator is able to construct a dry cooling system satisfying a wide variety of power cycle requirements (e.g., 10–100 MW) for any surface air temperature. Using this simulator, we leverage recent results in high-dimensional Bayesian optimization to optimize dry cooler designs that minimize lifetime cost for a given location, reducing this cost by 67% compared to recently proposed designs. Our simulation and optimization framework can increase the development pace of economically-viable sustainable energy generation systems.
Copyright/License	© 2024 The Author(s) (License: CC-BY-4.0)

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