对天然气能源计量的混合智能方法的研究
Jingya Dong1,2, Bin Song1,3, Fei He1
1Natural Gas Research Institute, PetroChina & Southwest Oil and Gas Field Company, Chengdu 610213, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
多层感知子神经网络 (MLPNN) 的新综合图法 (CDM) 改善了天然气能量计量. 这种智能方法使用超声波流量计数据准确测量Z因子和热量值.
科学领域:
- 能源科学 能源科学
- 人工智能的人工智能
- 计量学 计量学 计量学
背景情况:
- 精确的天然气能源计量对于能源行业至关重要.
- 传统方法在精度和适应性方面面临挑战.
- 智能方法提供了提高测量精度的潜力.
研究的目的:
- 提出和验证天然气能源计量中的多层感知子神经网络 (MLPNN) 的综合图法 (CDM).
- 利用超声波流量计的温度,压力和声速数据进行改进的测量.
- 评估拟议智能方法的准确性和适应性.
主要方法:
- 开发一个综合图表方法 (CDM),与多层感知神经元网络 (MLPNN) 集成.
- 训练和测试MLPNN模型,使用Z-因子和热量值的1003个真实数据点.
- 对最佳的MLP 3-5-1网络和CDM进行实验选择,分别用于测量Z-因子和热值.
- 通过20天的现实世界测试和当地累积能量量测量的验证.
主要成果:
- 贝叶斯规范的MLPNN (BR-MLPNN) 3-5-1网络实现了Z-因子的最大相对误差为-0.44%.
- 在CDM方法中,热量值的最大相对误差为1.90%.
- 当地测试显示,天然气每日累计能量量的最大相对误差为2.39%.
结论:
- 拟议的CDM与MLPNN集成,为天然气能源计量提供了一个准确和适应性的智能方法.
- 该研究成功验证了BR-MLPNN 3-5-1网络用于Z因子测量和CDM用于热量测量的有效性.
- 该方法显示了在天然气行业实际应用的巨大潜力,提高了测量精度.
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