在不分离的情况下测量三相流的体积分数,采用基于人工智能和电容传感器的方法
Abdulilah Mohammad Mayet1, Farhad Fouladinia2, Seyed Mehdi Alizadeh3
1Electrical Engineering Department, King Khalid University, Abha, Saudi Arabia.
PloS one
|May 16, 2024
概括
这项研究引入了使用人工神经网络 (ANN) 和电容传感器的智能计量系统,以准确地测量同质流程中的气油水体积分,从而实现低误差率.
科学领域:
- 多相流量测量多相流量测量
- 人工智能在流体动力学中的应用
- 电容感应技术的技术感应能力.
背景情况:
- 由于各种行业的财务和运营原因,精确测量气油水体积分是非常重要的.
- 传统方法通常需要相位分离,导致中断和不准确.
- 基于容量的传感器提供非侵入性和自主测量能力.
研究的目的:
- 开发和验证一个智能计量系统,用于精确测量三相 (气油水) 均流量的体积分数.
- 利用人工神经网络 (ANN) 提高多相流量计的精度.
- 为了证明基于电容的传感器与ANN集成的有效性,用于实时流体分析.
主要方法:
- 利用基于电容的传感器,使用COMSOL多物理优化和模拟,生成231个独特的数据点.
- 使用多层感知器 (MLP) 网络,一种人工神经网络 (ANN),用于预测体积分数.
- 用70%的模拟数据 (161个数据点) 训练了MLP网络,并用剩余的30% (70个数据点) 验证了其性能.
主要成果:
- 开发的智能计量系统在天然气-石油-水同质模式下实现了高度准确的水体积分数估计.
- 该系统的平均绝对误差 (MAE) 非常低,为1.66,这表明预测准确度很高.
- 拟议的多层感知器 (MLP) 网络有效地处理传感器数据,以精确表征流体.
结论:
- 电容传感器和人工神经网络 (ANN),特别是MLP的集成,为智能多相流量计提供了强大而准确的解决方案.
- 开发的系统提供了一种精确且低误差的方法,用于确定同质气油水流中的体积分数.
- 未来的研究可以将这种方法扩展到非均的流动模式,并考虑温度和压力变化等环境因素.
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