Studies of Gas Holdup in Bubble Column Reactors

Jamialahmadi, M.; Müller‐Steinhagen, Hans; Sarrafi, Amir; Smith, J. M.

doi:10.1002/1521-4125(200010)23:10<919::aid-ceat919>3.0.co;2-r

Cited by 17 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, Ohki and Inoue (1970) concluded that as the column diameter increases the transition gas superficial velocity increases too. This observation was also noted by Zahradink et al (1997); Sarrafi et al (1999); Jamialahmadi et al (2000); Urseanu (2000); and Ruzicka et al (2001). The effect of liquid purity was investigated by several researchers such as Anderson and Quinn (1970), who compared the gas void fraction in distilled and tap water.…”

Section: Introductionsupporting

confidence: 66%

Destabilisation of homogeneous bubbly flow in an annular gap bubble column

2010

View full text Add to dashboard Cite

Experimental results are presented to show that there are very significant differences in the mean gas void fractions measured in an open tube and a annular gap bubble column, when operated at the same gas superficial velocity, using a porous sparger. Measurements were carried out in a vertical 0.102 m internal diameter column, with a range of concentric inner tubes to form an annular gap, giving diameter ratios from 0.25 to 0.69; gas superficial velocities in the range 0.014–0.200 m/s were investigated. The mean gas void fraction decreases with increasing ratio of the inner to outer diameter of the annular gap column and the transition to heterogeneous flow occurs at lower gas superficial velocities and lower void fractions. Two reasons are proposed and validated by experimental investigations: (1) the presence of the inner tube causes large bubbles to form near the sparger, which destabilise the homogeneous bubbly flow and reduce the mean void fraction; this was confirmed by deliberately injecting large bubbles into a homogeneous dispersion of smaller bubbles, and (2) the shape of the void fraction profiles changes with gap geometry and this affects the distribution parameter in the drift‐flux model. Both of these effects serve to reduce the mean gas void fraction in an annular gap bubble column compared to an open tube at the same gas superficial velocity.

show abstract

Section: Introductionsupporting

confidence: 66%

Destabilisation of homogeneous bubbly flow in an annular gap bubble column

2010

View full text Add to dashboard Cite

show abstract

“…for which U ∞ b should be estimated with the generalised correlation of Jamialahmadi and Müller-Steinhagen [26] based on the bubble diameter given by the model of Gaddis and Vogelpohl [27].…”

Section: Available Relations For Regime Transitionmentioning

confidence: 99%

On the estimation of the regime transition point in bubble columns

Ribeiro

2008

Chemical Engineering Journal

View full text Add to dashboard Cite

“…In past years several experimental and theoretical investigations have been reported on heat transfer in bubble column reactors, but their applicability are limited largely to the original experimental conditions and contain several constants determined by empirical fitting (Jamialahmadi 2001).Thus, in the present study we made use of artificial neural network to present a more comprehensive model to predict the heat transfer coefficient in these columns. A list of these correlations found in literature is summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Heat Transfer Coefficient in a Bubble Column Using an Artificial Neural Network

Al-Hemiri¹,

Ahmedzeki²

2008

International Journal of Chemical Reactor Engineering

View full text Add to dashboard Cite

An artificial neural network (ANN) was applied for the prediction of the heat transfer coefficient in bubble columns, in order to obtain a general model and to facilitate the scale up of these multiphase contactors, covering a wide range of operating conditions, physical properties, and column dimensions, obtained from literature. A large number of data was collected (more than 1000) via a comprehensive literature survey. Selected parameters affecting the heat transfer coefficient were organized in six groups to serve as the input parameters. These were: gas superficial velocity, gas density, liquid density, diameter of the column, liquid viscosity, and gas hold-up. Four Back-Propagation Networks (BPNNS) were built. Two were trained using a different number of input parameters. The first ANN was trained with six inputs, which were the aforementioned parameters. The second was trained with three inputs only. These were gas velocity, liquid viscosity and gas hold-up. Each ANN was examined for two structures i.e., one hidden layer and two hidden layers. Comparison between these networks was made to find the optimal ANN structure with minimum %AARE and the maximum correlation coefficient (%R). It was found that the ANN structure of [6-13-1] with a %AARE of 16.2 and a %R of 94 was the best.

show abstract

Studies of Gas Holdup in Bubble Column Reactors

Cited by 17 publications

References 6 publications

Destabilisation of homogeneous bubbly flow in an annular gap bubble column

Destabilisation of homogeneous bubbly flow in an annular gap bubble column

On the estimation of the regime transition point in bubble columns

Prediction of the Heat Transfer Coefficient in a Bubble Column Using an Artificial Neural Network

Contact Info

Product

Resources

About