一种基于密度与声速相关的天然气能量计量方法
Bin Zhang1, Zhenwei Huang1, Wenlin Wang2
1College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一种用于精确测量天然气能量的新方法. 它使用机器学习来预测压缩因子和热量值等关键特性,提高能量测量精度.
科学领域:
- 能源科学 能源科学
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 准确的天然气能源计量对于商业交易至关重要.
- 现有的方法在不同的操作条件下,在精度方面面临挑战.
研究的目的:
- 开发一种新的,高精度的天然气能源计量方法.
- 利用物理属性相关性和机器学习来提高准确性.
主要方法:
- 构建了1万个样本的天然气特性数据集.
- 采用机器学习算法来构建压缩因子和热量值的预测模型.
- 开发了一个基于输入特征范围的模型切换策略,以提高预测准确性.
主要成果:
- 在压缩系数 (MAE: 0.00118,R2: 0.9987) 和热量值 (MAE: 0.1583,R2: 0.9736) 模型中实现了高精度.
- 在实流试验台上验证了该方法,预测误差最大为0.061% (压缩系数) 和1.19% (热量值).
- 在各种天然气样本中显示出1.21%的最大相对能量误差.
结论:
- 拟议的方法有效地在实际操作条件下实现精确的天然气能计.
- 将物理性质相关性与机器学习相结合,为能量测量提供了一个强大的解决方案.
- 模型切换策略显著提高了天然气性质的预测性能.
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