Ph.D. Dissertation Defense by Tao Xie
Tuesday, August 30, 2004
( Dr. S. Mostafa Ghiaasiaan, Chair)
"Hydrodynamic Characteristics of Gas/Liquid/Fiber Three-Phase Flows Based on Objective and Minimally-Intrusive Pressure Fluctuation Measurements"
Flow regime identification in industrial systems that rely on complex multi-phase flows is crucial for their safety, control, diagnostics, and operation. The objective of this investigation was to develop and demonstrate objective and minimally-intrusive flow regime classification methods for gas/water/paper pulp three-phase slurries, based on artificial neural network-assisted recognition of patterns in the statistical characteristics of pressure fluctuations.
Experiments were performed in an instrumented three-phase bubble column featuring
vertical, upward flow. The hydrodynamics of low consistency (LC) gas-liquid-fiber
mixtures, over a wide range of superficial phase velocities, were investigated.
Flow regimes were identified, gas holdup (void fraction) was measured, and near-wall
pressure fluctuations were recorded using high-sensitivity pressure sensors.
Artificial neural networks of various configurations were designed, trained
and tested for the classification of flow regimes based on the recorded pressure
fluctuation statistics. The feasibility of flow regime identification based
on statistical properties of signals recorded by a single sensor was thereby
demonstrated. The transportability of the developed method, whereby an artificial
neural network trained and tested with a set of data is manipulated and used
for the characterization of an unseen and different but plausibly similar data
set, was also examined. An artificial neural network-based method was developed
that used the power spectral characteristics of the normal pressure fluctuations
as input, and its transportability between separate but in principle similar
sensors was successfully demonstrated. An artificial neural network-based method
was furthermore developed that enhances the transportability of the aforementioned
artificial neural networks that were trained for flow pattern recognition. While
a redundant system with multiple sensors is an obvious target application, such
robustness of algorithms that provides transportability will also contribute
to performance with a single sensor, shielding effects of calibration changes
or sensor replacements.