机器学习发现了用于集中式太阳能发电厂的成本效益高的干冷器设计
Hansley Narasiah1, Ouail Kitouni2, Andrea Scorsoglio3
1University College London, London, UK.
Scientific reports
|August 17, 2024
概括
这项研究引入了一种机器学习系统,以优化使用超临界二氧化碳 (sCO2) 布雷顿循环的集中太阳能发电 (CSP) 厂的干冷. 该系统大大降低了成本,使得可持续能源更容易获得.
科学领域:
- 可再生能源系统可再生能源系统
- 热力学是一种热力学.
- 人工智能的人工智能
背景情况:
- 集中式太阳能 (CSP) 提供了必不可少的储能,但在干旱地区需要有效的干燥冷却.
- 超临界二氧化碳 (sCO2) 布雷顿循环为CSP技术提供了具有成本竞争力的进步.
- 优化干冷系统对于高辐射位置的CSP工厂的实际和经济可行性至关重要.
研究的目的:
- 开发和应用机器学习系统,优化sCO2布雷顿循环CSP工厂干冷系统的设计和配置.
- 创建一个基于物理的模拟,能够在各种条件下模拟空冷换热器的性能.
- 为了最大限度地降低CSP工厂干冷系统的寿命成本.
主要方法:
- 开发基于物理的模拟,用于空气冷却换热器的性能.
- 高维贝叶斯优化技术的应用.
- 模拟与优化工厂设计和配置的整合.
主要成果:
- 一个模拟框架,能够设计各种功率周期 (10-100兆瓦) 和环境温度的干冷系统.
- 与以前的设计相比,优化将干冷却器的寿命成本降低了67%.
- 在CSP工厂的干冷系统中显著降低了成本.
结论:
- 开发的机器学习框架有效优化了 sCO2 布雷顿循环 CSP 工厂的干冷系统.
- 这种方法提高了可持续能源生产的经济可行性.
- 加快开发具有成本效益,可持续的能源解决方案.
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