Determination of bubble size distribution in a bubble column reactor using artificial neural network

2011 ◽  
Vol 7 (4) ◽  
pp. 613-623 ◽  
Author(s):  
Sahar Amiri ◽  
Mohammad Reza Mehrnia ◽  
Davood Barzegari ◽  
Aryan Yazdani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Size Distribution ◽  
Bubble Size ◽  
Bubble Column ◽  
Bubble Size Distribution ◽  
Bubble Column Reactor ◽  
Column Reactor ◽  
Artificial Neural

Bubble Size Distribution for A Bubble Column Reactor Undergoing Forced Oscillations

2009 ◽  
Vol 48 (4) ◽  
pp. 1786-1796 ◽  
Author(s):  
Y. G. Waghmare ◽  
C. A. Dorao ◽  
H. A. Jakobsen ◽  
F. Carl Knopf ◽  
R. G. Rice
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Bubble Column ◽  
Bubble Size Distribution ◽  
Forced Oscillations ◽  
Bubble Column Reactor ◽  
Column Reactor

Bubble Trajectory in a Bubble Column Reactor using Combined Image Processing and Artificial Neural Network

2017 ◽  
Vol 15 (1) ◽  
Author(s):  
Balachandar Chidambaram ◽  
Arunkumar Seshadri ◽  
Venkatesan Muniyandi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Bubble Column ◽  
Numerical Results ◽  
Newtonian Fluids ◽  
Bubble Column Reactor ◽  
Column Reactor ◽  
Bubble Column Reactors ◽  
Artificial Neural

Abstract The applications of Bubble column reactors in gas-liquid multiphase reactions are widely observed in process industries. Biochemical reactions such as wet oxidation and algae bio-reactions are carried out in bubble column reactors. In this article, an image processing based comprehensive algorithm is developed to identify the trajectory of bubbles in a bubble column reactor. Photographs of bubbles moving up in a bubble column reactor are recorded for different velocities using a high speed camera. An algorithm is developed to plot the trajectory of the bubble. The developed algorithm can be used with experimental and numerical results to trace the trajectory of bubbles. The algorithm is applied to the results of volume of fluids (VOF) simulation to identify the bubble path in Newtonian and non-Newtonian fluids. Based on the algorithm, numerical results obtained on Newtonian fluids are used to train an Artificial Neural Network (ANN) to find the temporal position of the bubble. Superficial fluid velocities, nozzle diameter and time are the input parameters. The trained Levenberg-Marquardt based neural network can find the position of the bubble at any instant of time. The designed algorithm can study the dynamics and position of a bubble in process applications carried out in a bubble column reactor.

CFD Simulation of Hydrodynamic of a Bubble Column Reactor Operating in Churn-Turbulent Regime and Effect of Gas Inlet Distribution on System Characteristics

2016 ◽  
Vol 14 (1) ◽  
pp. 213-224 ◽  
Author(s):  
Amir Azimi Yancheshme ◽  
Jamshid Zarkesh ◽  
Davod Rashtchian ◽  
Arezou Anvari
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Cfd Simulation ◽  
Bubble Column ◽  
Continuous Phase ◽  
Bubble Size Distribution ◽  
Bubble Column Reactor ◽  
Turbulent Regime ◽  
Turbulent Flow Regime ◽  
Reactor Operating

AbstractCFD simulation of cylindrical bubble column including air as dispersed phase and water as continuous phase operating in churn-turbulent flow regime with diameter of 0.49 m, height of 3.6 m and gas superficial velocity of 0.14 m/s have been conducted. All simulations have been carried out in a 2D axisymmetric, unsteady and Euler/Euler framework with the aid of commercial software FLUENT v. 14.5. Simulations were validated by our experimental results through residence time distribution (RTD) data. Effect of bubble size distribution at inlet on column hydrodynamic was investigated and results clearly showed that equilibrium bubble size distribution in most parts of column is independent of bubble size distribution at inlet. In addition, liquid axial velocity and gas hold-up profiles results in column center were approximately the same for cases with different inlet distribution and confirmed similarity of their hydrodynamic.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

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