Abstract
This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of horizontal oil–water two-phase flow. Taking the Lorenz system as an example, we verify the MEMD is more suitable for simultaneous decomposition of multi-channel signals than empirical mode decomposition and ensemble empirical mode decomposition. In the proposed method, we employ the MEMD to decompose the multivariate conductance signal of oil–water two-phase flow to obtain the same intrinsic mode function modes, select the normalized energy of the high-frequency components as the eigenvalue, and utilize the trained ELM to achieve a good recognition result. The experimental results show that the proposed method is not only fast and generalized, but also has high accuracy in identifying flow patterns of oil–water two-phase flow.
Highlights
Oil–water two-phase flow is a major two-phase flow (Gao et al 2018), which is widely present in petroleum transportation pipelines
We verify the multiple empirical mode decomposition (MEMD) is more suitable for simultaneous decomposition of multi-channel signals than the EMD and the EEMD
We can use the MEMD to process the multi-channel signal of oil–water two-phase flow
Summary
Oil–water two-phase flow is a major two-phase flow (Gao et al 2018), which is widely present in petroleum transportation pipelines. Fuzzy logic with principal component analysis (PCA) and support vector machine (SVM) are applied to improve the classification accuracy of gas–liquid two-phase flow regimes (Shanthi and Pappa 2017). During the experiment, they proved SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive. This paper proposes to perform MEMD on the four channel conductance signal and use the IMF normalized energy as the characteristic values and utilize the ELM for training to achieve accurate identification of oil–water two-phase flow patterns. The experimentally results verify that the proposed method can identify the flow patterns of oil–water two-phase flow quickly and accurately
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