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Gas⁻Solid Two-Phase Flow Pattern Identification Based on Artificial Neural Network and Electrostatic Sensor Array.

Fei-Fei Fu1, Jian Li2

  • 1School of Physics and Technology, University of Jinan, Jinan 250022, China. sps_fuff@ujn.edu.cn.

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|October 21, 2018
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Summary
This summary is machine-generated.

This study introduces an electrostatic sensor array (ESA) and artificial neural network (ANN) for identifying gas-solid two-phase flow patterns in pneumatic conveying pipelines. The method achieved 100% accuracy in distinguishing four distinct flow patterns.

Keywords:
artificial neural networkelectrostatic sensor arrayflow pattern identificationgas‒solid two-phase flow

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Area of Science:

  • Engineering
  • Physics

Background:

  • Pneumatic conveying systems are crucial for transporting granular materials.
  • Accurate identification of gas-solid two-phase flow patterns is essential for optimizing system performance and preventing operational issues.

Purpose of the Study:

  • To develop and validate a novel method for real-time identification of flow patterns in horizontal pneumatic conveying pipelines.
  • To assess the efficacy of an electrostatic sensor array (ESA) coupled with an artificial neural network (ANN) for this purpose.

Main Methods:

  • An electrostatic sensor array (ESA) with eight arc-shaped electrodes was designed and implemented.
  • Numerical simulations were performed to analyze the relationship between electrostatic signals and flow patterns.
  • Experimental data involving four flow patterns (fully suspended, stratified, dune, and slug flow) were collected.
  • Average values and standard deviations of ESA signal amplitudes were used as input features for an artificial neural network (ANN).

Main Results:

  • Electrostatic signal amplitudes provide crucial information regarding particle distribution, enabling flow pattern inference.
  • The ANN model, utilizing average signal values and standard deviations, achieved 100% correct classification rates for all four identified flow patterns.
  • The proposed method demonstrates high accuracy and reliability in distinguishing between different gas-solid flow regimes.

Conclusions:

  • The combination of an ESA and ANN offers a highly effective and accurate solution for gas-solid two-phase flow pattern identification in pneumatic conveying.
  • This approach has significant potential for real-time monitoring and control in industrial pneumatic conveying systems.