Numerical simulation of bubble columns by integration of bubble cell model into the population balance framework

• Main Author: Khama, Mopeli
• Other Authors: Rawatlal, Randhir
• Format: Dissertation
• Language: English
• Published: University of Cape Town 2014
• Online Access: http://hdl.handle.net/11427/9118

Includes bibliographical references. === Bubble column reactors are widely used in the chemicals industry including pharmaceuticals, waste water treatment, flotation etc. The reason for their wide application can be attributed to the excellent rates of heat and mass transfer that are achieved between the dispersed and continuous phases in such reactors. Although these types of contactors possess the properties that make them attractive for many applications, there still remain significant challenges pertaining to their design, scale-up and optimization. These challenges are due to the hydrodynamics being complex to simulate. In most cases the current models fail to capture the dynamic features of a multiphase flow. In addition, since most of the developed models are empirical, and thus beyond the operating conditions in which they were developed, their accuracy can no longer be retained. As a result there is a necessity to develop eneric models which can predict hydrodynamics, heat and mass transfer over a wide range of operating conditions. With regard to simulating these systems, Computational Fluid Dynamics (CFD) has been used in various studies to predict mass and heat transfer characteristics, velocity gradients etc (Martín et al., 2009; Guha et al., 2008; Olmos et al., 2001; Sanyal et al., 1999; Sokolichin et al., 1997).The efficient means for solving CFD are needed to allow for investigation of more complex systems. In addition, most models report constant bubble particle size which is a limitation as this can only be applicable in the homogenous flow regime where there is no complex interaction between the continuous and dispersed phase (Krishna et al., 2000; Sokolichin & Eigenberger., 1994). The efficient means for solving CFD intimated above is addressed in the current study by using Bubble Cell Model (BCM). BCM is an algebraic model that predicts velocity, concentration and thermal gradients in the vicinity of a single bubble and is a computationally efficient approach The objective of this study is to integrate the BCM into the Population Balance Model (PBM) framework and thus predict overall mass transfer rate, overall intrinsic heat transfer coefficient, bubble size distribution and overall gas hold-up. The experimental determination of heat transfer coefficient is normally a difficult task, and in the current study the mass transfer results were used to predict heat transfer coefficient by applying the analogy that exists between heat and mass transfer. In applying the analogy, the need to determine the heat transfer coefficient experimentally or numerically was obviated. The findings indicate that at the BCM Renumbers (Max Re= 270), there is less bubble-bubble and eddy-bubble interactions and thus there is no difference between the inlet and final size distributions. However upon increasing Re number to higher values, there is a pronounced difference between the inlet and final size distributions and therefore it is important to extend BCM to higher Re numbers. The integration of BCM into the PBM framework was validated against experimental correlations reported in the literature. In the model validation, the predicted parameters showed a close agreement to the correlations with overall gas hold-up having an error of ±0.6 %, interfacial area ±3.36 % and heat transfer coefficient ±15.4 %. A speed test was also performed to evaluate whether the current model is quicker as compared to other models. Using MATLAB 2011, it took 15.82 seconds for the current model to predict the parameters of interest by integration of BCM into the PBM framework. When using the same grid points in CFD to get the converged numerical solutions for the prediction of mass transfer coefficient, the computational time was found to be 1.46 minutes. It is now possible to predict the intrinsic mass transfer coefficient using this method and the added advantage is that it allows for the decoupling of mass transfer mechanisms, thus allowing for more detailed designs.The decoupling of mass transfer mechanisms in this context refers to the separate determination of the intrinsic mass transfer coefficient and interfacial area.