Multiphase flowrate measurement with multi-modal sensors and temporal convolutional network

Haokun Wang, Delin Hu, Maomao Zhang, Nan Li, Yunjie Yang

Research output: Contribution to journalArticlepeer-review


Accurate multiphase flow measurement is vital in monitoring and optimizing various production processes. Deep learning has as of late arose as a promising approach for assessing multiphase flowrate dependent on various customary flow meters. In this paper, we propose a multi-modal sensor and Temporal Convolution Network (TCN) based method to predict the volumetric flowrate of oil/gas two-phase flows. The volumetric flowrates of the liquid and gas phase vary from 0.96 - 6.13 m3/h and 5.5 - 121.2 m3/h, respectively. The multi-modal sequential sensing data are simultaneously collected from a Venturi tube and a dual-plane Electrical Capacitance Tomography (ECT) sensor in a pilot-scale multiphase phase flow facility. The reference data are derived from the single-phase flowmeters. Z-score and First-Difference (FD) data pre-processing methods are employed to manipulate the collected instantaneous time series multi-modal sensing data. The pre-processed data are utilized for training the TCN model. Experimental results reveal that the TCN model can effectively predict the multiphase flowrate based on the multi-modal sensing data.The results provide guidance on data pre-processing methods for multiphase flowrate estimation and demonstrate the effectiveness of combining multi-modal sensors and TCN for multiphase flowrate prediction under complex flow conditions.
Original languageEnglish
JournalIEEE Sensors Journal
Early online date29 Apr 2022
Publication statusE-pub ahead of print - 29 Apr 2022


  • Temporal Convolutional Network (TCN)
  • Z-Score
  • first difference method
  • multiphase flowrate measurement
  • time series data


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