A One-Dimensional Combined Multifluid-Population Balance Model for the Simulation of Batch Bubble Columns

Author(s):  
Ferdinand Breit ◽  
Adam Mühlbauer ◽  
Erik von Harbou ◽  
Mark W. Hlawitschka ◽  
Hans-Jörg Bart
2020 ◽  
pp. 014459872098361
Author(s):  
Zhongbao Wu ◽  
Qingjun Du ◽  
Bei Wei ◽  
Jian Hou

Foam flooding is an effective method for enhancing oil recovery in high water-cut reservoirs and unconventional reservoirs. It is a dynamic process that includes foam generation and coalescence when foam flows through porous media. In this study, a foam flooding simulation model was established based on the population balance model. The stabilizing effect of the polymer and the coalescence characteristics when foam encounters oil were considered. The numerical simulation model was fitted and verified through a one-dimensional displacement experiment. The pressure difference across the sand pack in single foam flooding and polymer-enhanced foam flooding both agree well with the simulation results. Based on the numerical simulation, the foam distribution characteristics in different cases were studied. The results show that there are three zones during foam flooding: the foam growth zone, stable zone, and decay zone. These characteristics are mainly influenced by the adsorption of surfactant, the gas–liquid ratio, the injection rate, and the injection scheme. The oil recovery of polymer-enhanced foam flooding is estimated to be 5.85% more than that of single foam flooding. Moreover, the growth zone and decay zone in three dimensions are considerably wider than in the one-dimensional model. In addition, the slug volume influences the oil recovery the most in the foam enhanced foam flooding, followed by the oil viscosity and gas-liquid ratio. The established model can describe the dynamic change process of foam, and can thus track the foam distribution underground and aid in optimization of the injection strategies during foam flooding.


Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 17 ◽  
Author(s):  
Giorgio Besagni ◽  
Fabio Inzoli

A precise estimation of the bubble size distribution (BSD) is required to understand the fluid dynamics in gas-liquid bubble columns at the “bubble scale,” evaluate the heat and mass transfer rate, and support scale-up approaches. In this paper, we have formulated a population balance model, and we have validated it against a previously published experimental dataset. The experimental dataset consists of BSDs obtained in the “pseudo-homogeneous” flow regime, in a large-diameter and large-scale bubble column. The aim of the population balance model is to predict the BSD in the developed region of the bubble column using as input the BSD at the sparger. The proposed approach has been able to estimate the BSD correctly and is a promising approach for future studies and to estimate bubble size in large-scale gas–liquid bubble columns.


2018 ◽  
Vol 2 (1) ◽  
pp. 12 ◽  
Author(s):  
Camila Braga Vieira ◽  
Giuliana Litrico ◽  
Ehsan Askari ◽  
Gabriel Lemieux ◽  
Pierre Proulx

Author(s):  
Tamar Rosenbaum ◽  
Victoria Mbachu ◽  
Niall Mitchell ◽  
John Gamble ◽  
Patricia Cho ◽  
...  

In this work, the advantage of two-dimensional population balance modeling (2D PBM) for a needle-shaped API is highlighted by comparing the one-dimensional population balance model (1D PBM) developed for an antisolvent crystallization with the 2D PBM. The API utilized for this work had extremely slow desupersaturation, and was not able to achieve solubility concentration despite a ~50 h seed bed age. While the 1D PBM is useful in optimizing the crystallization process to enhance desupersaturation, it is unable to match the particle size quantiles well. 2D PBM was necessary to probe the impact of crystallization process parameters on particle aspect ratio (AR). Simulations utilizing the 2D PBM indicated that regardless of antisolvent addition rate or seed morphology, the final material would still be high aspect ratio. This knowledge saved the investment of much time and efforts in trying to minimize particle AR with changes in crystallization processing parameters alone.


2001 ◽  
Vol 27 (1) ◽  
pp. 63-71 ◽  
Author(s):  
S Sivakumar ◽  
Manjunath Subbanna ◽  
Satyam S Sahay ◽  
Vijay Ramakrishnan ◽  
P.C Kapur ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document