Methane bubble formation and dynamics in a rectangular bubble column: A CFD study

Pourtousi, M.; Ganesan, P.; Kazemzadeh, Amin; Sandaran, Shanti C.; Sahu, J.N.

doi:10.1016/j.chemolab.2015.08.003

Cited by 25 publications

(17 citation statements)

References 48 publications

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“…Such domain represents 2D cross‐sectional view of cylindrical bubble column. Literature data have adopted such domain for observing an asymmetry in bubble shape and rise characteristics. Further justification for selecting a 2D domain for the present study is based on findings of Zahedi et al who reported insignificant difference (max 7%) between 2D and 3D simulations in the bubble velocity and diameter.…”

Section: Methodsmentioning

confidence: 99%

“…Gas–liquid bubble columns are commonly used as multiphase reactors in chemical, petrochemical and pharmaceutical industries for its advantages such as a high mass and heat transfer and effective interphase contact . The heat and mass transfer, as well as the performance of a bubble column, depends firmly on the bubble flow dynamics, bubble coalescence and bubble breakup phenomenon . The details of bubble rise dynamics and coalescence of co‐axial and lateral arrangements of individual bubbles are necessary for better understanding of the bubbly flow in bubble columns.…”

Section: Introductionmentioning

confidence: 99%

“…Numerical methods such as VOF, lattice Boltzmann (LB) and front tracking (FT) are quite popular at present for investigating bubble formation and coalescence characteristics as they have the advantage of revealing many fundamental details that cannot be measured. Computational studies are found to provide very reasonable results in comparison with experimental data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coalescence and rising behavior of co‐axial and lateral bubbles in viscous fluid: a CFD study

Ganesan

Islam

Pokrajac

et al. 2017

Asia-Pacific J Chem Eng

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The coalescence and rising behavior of co-axial and lateral bubbles play a valuable role in industrial bubble column which operates at non-ambient conditions. The volume of fluid with the continuum surface force (VOF-CSF) model is used for the present study. Three main case studies, namely under ambient and aqueous condition, μ* = σ* = 1, under reduced viscosity, μ* = 0.1, and surface tension, σ* = 0.1, are investigated and compared using two co-axial bubbles and three lateral bubble rise configuration models. The latter two cases represent operating conditions at above the ambient temperature or pressure. Prior to investigating the main cases, the model is validated with the experimental data from the literature for a single bubble and two lateral bubble coalescence. It is found that reducing viscosity and surface tension has a significant effect on the co-axial bubbles (or a pair of bubbles rising in a vertical line) and three lateral bubble growth and coalescence characteristics, as compared to ambient condition.Noted: μ r and σ r represent the reduced liquid viscosity and surface tension, respectively. P. B. GANESAN ET AL. Asia-Pacific Journal of Chemical Engineering 608 S c = 0.136 or Δx c = 0.816 mm; t = 0.046 s; (c) S c = 0.11 or Δx c = 0.88 mm, t = 0.039 s. Please refer to inset arrows for graphical clarity.Figure 11. (a-f) Effect of initial lateral gap, S, for different diameters of bubble, d b . P. B. GANESAN ET AL. Asia-Pacific Journal of Chemical Engineering 614

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Coalescence and rising behavior of co‐axial and lateral bubbles in viscous fluid: a CFD study

Ganesan

Islam

Pokrajac

et al. 2017

Asia-Pacific J Chem Eng

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“…Recently, ANFIS was implemented to simulate flow pattern in a bubble column reactor. Pourtousi et al used information about the hydrodynamics of multiphase reactors for the training step. They found that CFD plus ANFIS is a great tool to estimate BCR behaviour.…”

Section: Introductionmentioning

confidence: 99%

Liquid‐phase chemical reactors: Development of 3D hybrid model based on CFD‐adaptive network‐based fuzzy inference system

Babanezhad¹,

Rezakazemi

Hajilary³

et al. 2018

Can J Chem Eng

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The Euler-Euler method plus an intelligent algorithm was used to predict bubbly flow in a reactor as a function of column height. The combination of computational fluid dynamics (CFD) and the adaptive network-based fuzzy inference system (ANFIS) method was used for a chemical bubble column reactor to understand the complex behaviour of fluids in a multiphase reactor. Air fraction as one of the main factors in the scale-up of reactors was selected as an output parameter for the prediction tool (ANFIS method) at different positions in the reactor. To train and test the prediction ability of this method, a 3D position of one CFD element was selected and, based on that position, a training algorithm was started. After an appropriate learning step, the method was used to simulate gas at different locations of the reactor. The different structures of the ANFIS algorithm were designed to obtain a correct predictive tool for fluid behaviour inside the bubble column. The ANFIS approach shows that it can simulate the liquid behaviour and the CFD results and ANFIS output correlate with one another.

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“…The flow regime in the reactor changes with different operating conditions such as superficial gas velocity, column dimensions, sparger arrangement and specifications, gas and liquid properties and reactor operating pressure and temperature (Buwa & Ranade, 2003;Deen, Solberg, & Hjertager, 2000;M. T. Dhotre, Niceno, Smith, & Simiano, 2009;Díaz et al, 2008;Masood & Delgado, 2014;Pourtousi, Ganesan, Kazemzadeh, Sandaran, & Sahu, 2015;Pourtousi, Ganesan, Sandaran, & Sahu, 2016). There are two main flow regimes such as homogeneous (low rate of break-up and coalescence) and heterogeneous regime (high rate of break-up and coalescence) (Masood, Khalid, & Delgado, 2015;Silva, d'Ávila, & Mori, 2012;Sobrino, Acosta-Iborra, Izquierdo-Barrientos, & De Vega, 2015).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Study of ANFIS Model Parameters to Predict the Pressure Gradient with Combined Input and Outputs Hydrodynamics Parameters in the Bubble Column Reactor

Mosavi¹,

Chau²

2019

Preprint

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Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to 2 > 0.99 almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm.This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

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Methane bubble formation and dynamics in a rectangular bubble column: A CFD study

Cited by 25 publications

References 48 publications

Coalescence and rising behavior of co‐axial and lateral bubbles in viscous fluid: a CFD study

Coalescence and rising behavior of co‐axial and lateral bubbles in viscous fluid: a CFD study

Liquid‐phase chemical reactors: Development of 3D hybrid model based on CFD‐adaptive network‐based fuzzy inference system

Sensitivity Study of ANFIS Model Parameters to Predict the Pressure Gradient with Combined Input and Outputs Hydrodynamics Parameters in the Bubble Column Reactor

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