Multiphase metering and intelligent error correction for CO ₂ transport in CCUS systems: A review

High-precision measurement of CO 2 flow in carbon capture, utilization, and storage (CCUS) pipelines is difficult because captured CO 2 may cross gas, liquid, dense-phase, and supercritical states and is further affected by impurities. This review integrates four issues that have usually been treated separately: CO 2 thermophysical-property disturbances, hardware sensing boundaries, intelligent error correction, and engineering calibration in CCUS pipeline networks. Conventional mechanical and differential-pressure flowmeters are reliable under stable single-phase conditions but suffer from gas entrainment, damping shifts, and density-dependent errors near phase boundaries, whereas optical, acoustic, tomographic, and radiation-based methods provide nonintrusive routes for flow-regime visualization and impurity-tolerant monitoring. We further show that data-driven correction methods, including least-squares support vector machine, long short-term memory networks, Transformer models, and physics-informed neural networks, can compensate nonlinear signal drift when coupled with appropriate sensing hardware and calibration data. From the existing research progress, custody-grade CO 2 multiphase metering cannot rely on separate component optimization, but necessitates integrated hardware-software codesign, impurity-containing real-flow calibration and digital twin-assisted field verification.