CHARACTERIZATION AND MODELING OF GAS-LIQUID-SOLID FLUIDIZED BED REACTORS

This work characterizes the flow regime transitions of gas-liquid-solid fluidized beds by statistical analysis of the pressure fluctuations, and presents comprehensive models for both catalytic reactions and biological reactions in gas-liquid-solid fluidized beds. The pressure fluctuation behavior in a 4 inch ID gas-liquid-solid fluidized bed in studied in this work for a wide variety of particles. The average root mean square of the pressure fluctuations and power spectral density function of the pressure signals are used to characterize the transitions among the various flow regimes. These criteria for flow regime transitions agree well with visual observations.; The effect of particle size on the reactant conversion for a pseudo-first order reaction in a gas-liquid-solid fluidized bed catalytic reactor is examined based on a comprehensive model developed in this study. The reactant conversion predicted by the model exhibits a maximum with respect to particle size. Overall reaction rates in the fluidized bed system are compared to those predicted for a slurry bubble column utilizing a sedimentation-dispersion model for the solids.; A comprehensive model is presented for biological phenol degradation in a gas-liquid-solid fluidized bed containing a mixed culture of immobilized living cells. Double-substrate limiting kinetics and substrate inhibition are considered in the model. Biodegradation rates and phenol and dissolved oxygen concentrations predicted by the model are in excellent agreement with experimental data. The model is used to examine the effects of inlet phenol concentration and biofilm thickness on the biodegradation rate.; A mathematical model is also developed for the transient response of a draft tube gas-liquid-solid fluidized bed bioreactor to a step increase in influent phenol concentration. The model considers external mass transfer resistance, the simultaneous diffusion, reaction, and adsorption of phenol and oxygen inside the bioparticles, the dynamics of biofilm growth, the time delay of microbial growth during the transient period, and variations in biofilm thickness and density with biofilm growth. Simulation results predicted from the proposed model show satisfactory agreement with experimental data.