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首页> 外文期刊>Flow Measurement and Instrumentation >A neural network to corrent mass flow errors caused by two-phase flow in a digital coriolis mass flowmeter
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A neural network to corrent mass flow errors caused by two-phase flow in a digital coriolis mass flowmeter

机译：神经网络来纠正数字科里奥利质量流量计中两相流引起的质量流量误差

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摘要

Coriolis mass flow meters provide accurate measurement of single-phase flows, typically to 0.2%. However gas-liquid two-phase flow regimes may cause severe operating difficulties as well as measurement errors in these flow meters. As part of the Sensor Validation (SEVA) research at Oxford University a new fully digital coriolis transmitter has been developed which can operate with highly aerated fluids. This paper describes how a neural network has been used to correct the mass flow measurement for two-phase flow effects, based entirely on internally observed parameters, keeping errors to within 2%. The correction strategy has been successfully implemented on-line in the coriolis transmitter. As required by the SEVA philosophy, the quality of the corrected measurement is indicated by the on-line uncertainty provided with each measurement value.
机译：科里奥利质量流量计可精确测量单相流量，通常精确到0.2％。然而，在这些流量计中，气液两相流态可能会导致严重的操作困难以及测量误差。作为牛津大学传感器验证（SEVA）研究的一部分，已经开发了一种新型的全数字科里奥利变送器，它可以在高充气流体下运行。本文介绍了如何完全基于内部观察到的参数将神经网络用于校正两相流效应的质量流量测量，并将误差保持在2％以内的方法。校正策略已成功在科里奥利变送器中在线实施。根据SEVA哲学的要求，校正后的测量的质量由每个测量值随附的在线不确定性表示。

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