Mass Flowrate Measurement of Slurry Using Coriolis Flowmeters and Data Driven Modeling

Abstract

Coriolis flowmeters have been proven to be effective while measuring single phase flows, however, the measurement accuracy degrades in case of multiphase flows. This paper presents data-driven models that are incorporated into Coriolis flowmeters for mass flowrate measurement of two-phase (sand-water) slurry. Three different data-driven models based on Support Vector Machine (SVM), Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) are established through training and testing. To examine the behaviors of Coriolis flowmeter for slurry flow measurement, a series of experimental tests were conducted on a purpose-built slurry test rig under a range of mass flowrates (5435 -18582 kg/h) and Solid Volume Fractions (SVFs) between 0 -3.3%. The effects of the geometry and orientation conditions of Coriolis measuring tubes are also examined by installing two Coriolis flowmeters on horizontal pipe sections with their measuring tubes in upward and downward orientations. The factors that lead to measurement errors including density difference, asymmetry, damping, Coriolis tube geometry and orientation conditions are practically evaluated. The performances of the SVM, ANN and GPR models are assessed in comparison with the reference readings. A data augmentation technique is also applied to generate unseen condition data with ±5% deviation from the original data. The experimental results show that the GPR models are superior to the SVM and ANN models in terms of measurement accuracy. For the GPR models, 97% and 95.5% of the original data and 99% and 98% of the augmented data yield a relative error within ±0.2% for upward and downward orientations of Coriolis flowmeters, respectively, under all test conditions.