模拟R600a气体在制冷系统中的能源消耗,使用基于混合优化的新可解释的人工智能方法
Sinem Akyol1, Mehmet Das2, Bilal Alatas1
1Software Engineering Department, Engineering Faculty, Firat University, Elazig 23279, Turkey.
Biomimetics (Basel, Switzerland)
|September 27, 2023
概括
这项研究引入了一种新的人工智能方法,用于模拟R600a制冷剂气体的压缩机能耗. 可解释的人工智能方法确定了在蒸汽压缩制冷系统中减少能源使用的最佳操作条件.
科学领域:
- 热力学和制冷工程 热力学和制冷工程
- 人工智能和机器学习
背景情况:
- 制冷剂气体对于冷却系统至关重要,其选择基于热物理性质和能源效率.
- 使用人工智能建模制冷剂特性,包括压缩机能耗,是日益增长的研究领域.
- 低全球变暖潜力和能源消耗是选择制冷剂的关键因素.
研究的目的:
- 开发可解释,可解释和透明的AI模型,用于R600a蒸汽压缩制冷系统中的压缩器能耗.
- 通过基于混合优化的AI分类方法来确定最大限度地降低能源消耗的最佳运行条件.
- 为制冷剂气体能源消耗建模应用一种新的AI方法.
主要方法:
- 基于混合优化的人工智能分类方法的实施.
- 基于操作参数来预测R600a制冷剂气体能耗的模型开发.
- 对系统能耗相对于蒸发器和冷凝器温度和压力的分析.
主要成果:
- 人工智能模型根据操作参数准确地确定R600a的能源消耗.
- 该方法自动发现导致能耗最低的操作条件.
- 与实验数据相比,开发的模型达到84.4%的准确性.
结论:
- 应用可解释的AI方法为制冷剂能耗提供了透明和可解释的模型.
- 这种创新方法可以确定不同制冷剂气体中最低能耗的最佳运行条件.
- 这项研究证明了人工智能在优化制冷系统效率和环境影响方面的潜力.
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