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Error correction of a coriolis mass flow meter in two-phase flow measurment using Neuro-Fuzzy

机译:使用Neuro-Fuzzy在两相流量测量中校正科里奥利质量流量计的误差

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摘要

Coriolis mass flow meters despite proper performance and precision when faced with two-phased fluids will show a remarkable attenuation in performance and accuracy depending on the condition and the amount of variation of the density of passing fluid. In these situations Neuro-Fuzzy Algorithm helps determination of the error and improves performance and also looks a better choice compared to specific transmitters according to reduction in cost. Design and simulation have been made through MATLAB software. Previous measures made in this area encompassed using Neural Network [1] that this work was based on laboratory data and evidences in that research.
机译:尽管面对两相流体,科里奥利质量流量计尽管具有适当的性能和精度,但根据条件和通过流体的密度变化量的不同,其性能和精度也会显着降低。在这些情况下,神经模糊算法有助于确定错误并提高性能,并且根据成本的降低,与特定的变送器相比,它看起来也是更好的选择。通过MATLAB软件进行了设计和仿真。该领域以前的措施包括使用神经网络[1],该工作是基于实验室数据和该研究的证据。

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