Identification of gas/solid two-phase flow regimes using electrostatic sensors and neural-network techniques

Abstract In the gas/solid two-phase system, solid particles can accumulate a large number of electrostatic charges because of collision, friction and separation between particles or between particles and the wall. Through the detection and processing of the induced fluctuation charge signal, a measuring system can obtain two-phase flow parameters, such as flow regime, concentration and velocity. A novel methodology via introducing the characteristics of speech emotion recognition into flow regime identification is proposed for improving the recognition rate in gas/solid two-phase flow systems. Three characteristics of electrostatic fluctuation signals detected from an electrostatic sensor are extracted as the input of back propagation (BP) neural networks for flow regime identification. They are short-term average energy, Mel-frequency cepstral coefficients (MFCC) and cepstrum. The results show that the method based on each characteristic of the electrostatic fluctuation signal and BP neural networks can identify the three flow regimes of gas/solid two-phase flow in a horizontal pipe, and the identification rate of the method based on the three characteristics and BP neural networks is up to 97%, much higher than the methods based on a single characteristic.

[1]  Loïc Kessous,et al.  Time-Frequency Features Extraction for Infant Directed Speech Discrimination , 2009, NOLISP.

[2]  Yong Yan,et al.  Guide to the Flow Measurement of Particulate Solids in Pipelines, Part 2: Utilisation in Pneumatic Conveying and Emission Monitoring , 2002 .

[3]  S. Pattanayak,et al.  Electrical impedance method for flow regime identification in vertical upward gas-liquid two-phase flow , 1993 .

[4]  Goichi Matsui,et al.  Identification of flow regimes in vertical gas-liquid two-phase flow using differential pressure fluctuations , 1984 .

[5]  J. A. Jendrzejczyk,et al.  Application of chaos theory in identification of two-phase flow patterns and transitions in a small, horizontal, rectangular channel , 1996 .

[6]  Wang Peng.  An Improved Speech Endpoint Detection Method based on Cepstrum Distance , 2008 .

[7]  Takaaki Ohishi,et al.  Performance comparison of artificial neural networks and expert systems applied to flow pattern identification in vertical ascendant gas-liquid flows , 2010 .

[8]  Hiroshige Kikura,et al.  Pattern recognition and signal processing of ultrasonic echo signal on two-phase flow , 2006 .

[9]  Yong Yan,et al.  Guide to the Flow Measurement of Particulate Solids in Pipelines, Part 1: Fundamentals and Principles , 2001 .

[10]  Ping Wang,et al.  A computer-aided MFCC-based HMM system for automatic auscultation , 2008, Comput. Biol. Medicine.

[11]  Jan Čermák,et al.  Diagnostics of gas—liquid flow patterns in chemical engineering systems , 1989 .

[12]  Ye Mao.  Improving the MFCC Features for Speech Recognition , 2009 .

[13]  Juliusz B. Gajewski,et al.  Monitoring electrostatic flow noise for mass flow and mean velocity measurement in pneumatic transport , 1996 .

[14]  Shien Hui,et al.  A novel capacitive system for the concentration measurement of pneumatically conveyed pulverized fuel at power stations , 2006 .

[15]  Shashidhar G. Koolagudi,et al.  Spectral Features for Emotion Classification , 2009, 2009 IEEE International Advance Computing Conference.

[16]  Yong Yan,et al.  Velocity Measurement of Pneumatically Conveyed Particles Using Intrusive Electrostatic Sensors , 2010, IEEE Transactions on Instrumentation and Measurement.

[17]  Hongjian Zhang,et al.  Flow Regime Identification of Gas-liquid Two-phase Flow Based on HHT , 2006 .

[18]  Goichi Matsui,et al.  Automatic identification of flow regimes in vertical two-phase flow using differential pressure fluctuations , 1986 .

[19]  Tov Elperin,et al.  Flow regime identification in a two-phase flow using wavelet transform , 2002 .

[20]  Gang Wei,et al.  Speech emotion recognition based on HMM and SVM , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[21]  Chuanlong Xu,et al.  Velocity measurement of pneumatically conveyed solid particles using an electrostatic sensor , 2008 .

[22]  Lifeng Zhang,et al.  Identification of oil–gas two-phase flow pattern based on SVM and electrical capacitance tomography technique , 2010 .

[23]  Mohamed Chetouani.  Advances in Nonlinear Speech Processing, International Conference on Non-Linear Speech Processing, NOLISP 2007, Paris, France, May 22-25, 2007, Revised Selected Papers , 2007, NOLISP.

[24]  Jun Dong,et al.  IDENTIFICATION OF GAS–SOLID TWO-PHASE FLOW REGIMES USING HILBERT–HUANG TRANSFORM AND NEURAL-NETWORK TECHNIQUES , 2011 .

[25]  Fei Liu,et al.  Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm , 2011 .