Sparse regression system identification in two-phase flow metering

Multiphase flow metering is an open research field, with challenges encompassing sensor development and thermofluid models, for instance. Sparse regression is a modern system identification strategy able to provide simple models with Physical interpretation. This papers applies sparse identification for the assessment of gas velocity, gas fraction and superficial velocities of liquid and gas in experimental air–water horizontal slug flow across a Venturi tube and a twin-plane capacitive sensor. We show that this technique can improve measurement accuracies, as deviations for superficial velocities fall below 2.8% for liquid and 8.7% for gas. More importantly, the analysis of three data sets discusses practical concerns when applying sparse regression methods in metering, including the choice of basis functions, the degree of sparsity and overfitting. Overall, sparse identification is perceived as an adequate method to simultaneously generate a measurement model and correction of measurement biases in a specific measurement setup. •Sparse identification of flow features in MPFM for air–water horizontal slug flow.•The simple models obtained correct for systematic errors and comply with Physics.•Deviations below 3% are reached for liquid superficial velocities.•Recommendations for broad usage of such algorithms in measurement setups. [Display omitted]