Development of a Real-Time Objective Gas–Liquid Flow Regime Identifier Using Kernel Methods

Currently, flow regime identification for closed channels has mainly consisted of direct subjective methods. This presents a challenge when dealing with opaque test sections of the pipe or at gas-liquid flow rates where unclear regime transitions occur. In this paper, we develop a novel real-time objective flow regime identification tool using conductance data and kernel methods. Our experiments involve a flush-mounted conductance probe that collects voltage signals across a closed channel. The channel geometry is a horizontal annulus, which is commonly found in many industries. Eight distinct flow regimes were observed at selected gas-liquid flow rate settings. An objective flow regime identifier was then trained by learning a mapping between the probability density function (PDF) of the voltage signals and the observed flow regimes via kernel principal components analysis and multiclass support vector machine. The objective identifier was then applied in realtime by processing a moving time-window of voltage signals. Our approach has: 1) achieved more than 90% accuracy against visual observations by an expert for static test data; 2) successfully visualized conductance data in 2-D space using virtual flow regime maps, which are useful for tracking flow regime transitions; and 3) introduced an efficient real-time automatic flow regime identifier, with only conductance data as inputs.