Bubble Size Control to Improve Oxygen-Based Bleaching: Characterization of Flow Regimes in Pulp-Water-Gas Three-Phase Flows

Karrila, S.M. Ghiaasiaan and Seppo

Georgia Institute of Technology/Institute of Paper Science and Engineering

关键词: Transducers;    Sodium Hydroxides;    Classification;    Absorption;    Bleaching;   

DOI  :  10.2172/877362
RP-ID  :  ID13871
RP-ID  :  FC36-00ID13871
RP-ID  :  877362

美国|英语

来源: UNT Digital Library

【 摘 要 】

Flow characteristics of fibrous paper pulp-water-air slurries were investigated in a vertical circular column 1.8 m long, with 5.08 cm diameter. Flow structures, gas holdup (void fraction), and the geometric and population characteristics of gas bubbles were experimentally investigated, using visual observation, Gamma-ray densitometry, and flash X-ray photography. Five distinct flow regimes could be visually identified: dispersed bubbly, layered bubbly, plug, churn-turbulent, and slug. Flow regime maps were constructed, and the regime transition lines were found to be sensitive to consistency. The feasibility of using artificial neural networks (ANNs) for the identification of the flow regimes, using the statistical characteristics of pressure fluctuations measured by a single pressure sensor, was demonstrated. Local pressure fluctuations at a station were recorded with a minimally-intrusive transducer. Three-layer, feed-forward ANNs were designed that could identify the four major flow patterns (bubbly, plug, churn, and slug) well. The feasibility of a transportable artificial neural network (ANN) - based technique for the classification of flow regimes was also examined. Local pressures were recorded at three different locations using three independent but similar transducers. An ANN was designed, trained and successfully tested for the classification of the flow regimes using one of the normalized pressure signals (from Sensor 1). The ANN trained and tested for Sensor 1 predicted the flow regimes reasonably well when applied directly to the other two sensors, indicating a good deal of transportability. An ANN-based method was also developed, whereby the power spectrum density characteristics of other sensors were adjusted before they were used as input to the ANN that was based on Sensor 1 alone. The method improved the predictions. The gas-liquid interfacial surface area concentration was also measured in the study. The gas absorption technique was applied, using CO2 as the transferred species and sodium hydroxide as the alkaline agent in water. Statistical analysis was performed to identify the parametric dependencies. The experimental data were empirically correlated.