Mass flow rate measurement of gas-liquid two-phase flow using acoustic-optical-Venturi mutisensors. (April 2023)

• Record Type: Journal Article
• Authors: Li, Chaofan; Zhu, Yan; Wang, Jing; Liu, Weiguang; Fang, Lide; Zhao, Ning
• Is Part Of: Flow measurement and instrumentation. Volume 90(2023)
• Publication Date: 2023-04
• Subjects: Multi-sensor -- Fusion -- Attenuation -- Gas volume fraction -- Mass flow rate; Fluid dynamic measurements -- Periodicals; Flow meters -- Periodicals; Fluides, Dynamique des -- Mesure -- Périodiques; Débitmètres -- Périodiques; 681.2805
• DOI: 10.1016/j.flowmeasinst.2023.102314
• Languages: English
• ISSNs: 0955-5986

Abstract

Abstract: The measurement of multiphase flow parameters is essential for the online monitoring of industrial production and energy metering. In this paper, a multi-sensor experimental measurement device is designed based on NIR, acoustic emission sensors, and throated Venturi. The measurement information is decomposed using modal decomposition, and the characteristic variables of the gas volume fraction are extracted by flow noise decoupling and light attenuation analysis. A new gas volume fraction model is proposed based on Gradient Boosting Decision Tree (GBDT) through feature-level fusion, and the Mean Absolute Percentage Error (MAPE) of the gas volume fraction prediction models is within 4% for the three flow patterns. A new flow rate model is established based on the Homogeneous and Collins models. Laboratory results indicate that the MAPE of the flow rate model is 1.56%, and 98.61% relative deviations are within ±20% error band. The study provides a new method for online measurement of multiphase fluid motion and a theoretical basis for sensing mechanism and measurement of multiphase flow. Highlights: A new multi-sensor sensing system is designed for flow rate measurement. Flow noise and light signals are decoupled and attenuation analyzed. The new gas volume fraction model is developed based on integrated Learning. A new fusion model of flow rate is developed based on Homogeneous flow and Collins correlation. A new solution is proposed for fusion and optimization of fluid mechanics and Gradient Boosting Decision Tree.