M.S. Thesis Presentation by Tao Xie
Friday, April 6, 2001

(Drs. Mostafa Ghiaasiaan and Seppo Karilla, co-advisors)

"Hydrodynamics of Gas-Fiber Suspension Mixtures in Oxygen and Ozone-Based Pulp Bleaching Systems"


In paper industry, bleaching is a process in which residual lignin is removed from cellulose fiber, and the fiber is whitened. Traditional bleaching methods rely heavily on Chlorine, which generates organochlorine pollutants, and are being replaced with oxygen and ozone-based methods. Efficient operation of oxygen and ozone-based systems requires intimate and sustained mixing of the pulp, water and gas phases in order to provide for the needed rapid and extensive interfacial mass transfer processes. The hydrodynamics of the three-phase flow in bleaching systems are not well understood, however.

The purpose of this study is to experimentally investigate the hydrodynamic three-phase flow phenomena in gas-fibrous slurries, as they pertain to the mixing processes in bleaching systems. In addition, the feasibility of using an artificial neural network for the identification of flow regimes using measured pressure fluctuations will be examined. Artificial neural networks have excellent capability in pattern recognition for complex multi-parameter processes, and are increasingly applied in various fields of science and engineering. Corresponding strategies and retrofits will be developed for current bleaching systems, whenever possible.

Experiments will be performed using a fully-instrumented test loop for low consistency pulp flow mixed with air, oxygen and ozone. Among the instruments will be a Gamma-ray densitometer for the measurement of gas holdup, and a Flash X-ray Radiography (FXR) system for recording the morphological characteristics of the opaque fibrous flow field. Using FXR, the major hydrodynamic characteristics of the three-phase flow system will be measured over a wide range of parameters, and flow pattern will be identified. The obtained data will be correlated against the operating parameter of the system using artificial neural networks. The insight obtained will be used for the development of strategies for the control and optimization of bubble dispersion phenomena in pulp bleaching systems.