Water holdup measurement of oil-water two-phase flow based on KPLS regression

Oil-water two-phase flow frequently exists in laboratorial researches and petroleum industry. Water holdup is significant parameters for oil-water two-phase flow. The kernel partial least squares (KPLS) is an excellent nonlinear regression model. Compared with partial least squares (PLS), KPLS is provided high accuracy prediction for complex system. Based on a 16-electrode electrical resistance tomography (ERT) sensor and the soft sensor technology, a new measurement of water holdup is proposed for oil-water two-phase flow. The soft sensor technology applied in the research is a KPLS nonlinear regression algorithm. Through choice of kernel function, the computational results of four typical flow regimes of oil-water two-phase flow is shown that the KPLS method can be real-time measured the water holdup effectively.