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Soft measurement of oil-water two-phase flow using a multi-task sequence-based CapsNet.

Lei OuYang1, Ningde Jin1, Landi Bai1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, People's Republic of China.

ISA Transactions
|December 26, 2022
PubMed
Summary

This study developed an improved CapsNet for predicting oil-water two-phase flow. The enhanced model accurately predicts flow patterns and superficial velocity in large pipes.

Keywords:
Capsule networkMulti-task learningOil–water flowSoft measurementSuperficial velocity prediction

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Area of Science:

  • Multiphase flow dynamics
  • Fluid mechanics
  • Chemical engineering

Background:

  • Accurate measurement of flow parameters is crucial for understanding two-phase flow.
  • Predicting superficial velocity in oil-water two-phase flow within large diameter pipes remains a significant challenge due to complex flow structures.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate prediction of flow patterns and superficial velocity in vertical upward oil-water two-phase flow.
  • To address the challenges posed by high-dimensional, time-varying, and nonlinear characteristics of multiphase flow.

Main Methods:

  • Conducted vertical upward oil-water two-phase flow experiments in a 125 mm ID pipe using a vertical multi-electrode array (VMEA) conductance sensor.
  • Employed novel data pre-processing (1D to 2D) and information fusion techniques (network channels).
  • Optimized a multi-task sequence-based Capsule Network (CapsNet) with attention blocks, residual structures, inception blocks, and an improved dynamic routing algorithm for enhanced performance.

Main Results:

  • The proposed multi-task sequence-based CapsNet demonstrated superior performance in both flow pattern classification and superficial velocity prediction compared to its variants and other networks.
  • The optimized network architecture and improved dynamic routing algorithm effectively handled the complexities of the two-phase flow data.
  • Extensive experiments validated the effectiveness of the improved modules and the overall network.

Conclusions:

  • The developed multi-task sequence-based CapsNet shows significant potential for real-world applications in multiphase flow analysis.
  • The model offers a robust solution for high-dimensional, time-varying, and nonlinear problems encountered in industrial fluid dynamics.
  • This research advances the capabilities of sensor data analysis for precise flow parameter estimation.