太阳能辅助干燥剂空调系统使用辐射基函数神经网络的性能预测:一个集成的机器学习方法
Sibghat Ullah1,2, Muzaffar Ali1,3, Muhammad Fahad Sheikh4
1Mechanical Engineering Department, University of Engineering and Technology, Taxila, Pakistan.
Heliyon
|May 22, 2024
概括
这项研究开发了一种太阳能干燥剂空调系统的预测模型,该系统与M周期冷却器集成. 辐射基函数神经网络准确地预测了系统性能,显示了与实验数据的最小偏差.
科学领域:
- 可再生能源系统可再生能源系统
- 暖通空调技术技术的使用
- 在工程领域的人工智能.
背景情况:
- 太阳能干燥剂空调 (Sol-DAC) 系统通过利用太阳能进行再生,为冷却提供了一个可持续的替代方案.
- 与Maisotsenko循环 (M-循环) 间接蒸发式冷却器的集成可以提高潜伏和感应负载处理能力.
- 准确的性能预测对于优化Sol-DAC系统设计和运行至关重要.
研究的目的:
- 开发和验证一种新型Sol-DAC系统的预测模型,其中包含一个M周期间接蒸发式冷却器.
- 用实验数据研究系统在各种操作参数下的性能.
- 使用辐射基函数神经网络 (RBF-NN) 来预测关键系统性能指标.
主要方法:
- 一个太阳能干燥剂空调系统,包括一个交叉流动的M循环间接蒸发冷却器和一个干燥剂轮 (DW),被实验评估.
- 一个太阳能疏散管电热器为DW提供了再生温度.
- 在短暂条件下,使用九个输入参数和四个输出参数 (温度,湿度,冷却能力 (CC),性能系数 (COP)) 训练了一个辐射基函数神经网络 (RBF-NN).
主要成果:
- RBF-NN模型在预测系统性能方面表现出高精度,所有输出参数的平均平方误差 (MSE) 和回归系数 (R) 值都非常出色.
- 在各种入口湿度条件下,在70°C的再生温度下实现了最佳预测.
- 实验和预测的性能参数显示密切一致,偏差最小,证实了模型的可靠性.
结论:
- 经过训练的RBF-NN模型有效地预测了在动态操作条件下集成的Sol-DAC和M循环系统的性能.
- 验证了RBF-NN模型的预测能力,为系统分析和优化提供了有价值的工具.
- 该研究强调了将太阳能干燥剂冷却与M循环技术相结合的潜力,以实现高效和可持续的空调.
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