scholarly journals Developing Intelligent Algorithm as a Machine Learning Overview over the Big Data Generated by Euler–Euler Method To Simulate Bubble Column Reactor Hydrodynamics

ACS Omega ◽  
2020 ◽  
Vol 5 (32) ◽  
pp. 20558-20566 ◽  
Author(s):  
Meisam Babanezhad ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Saeed Shirazian
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Bubble Column ◽  
Euler Method ◽  
Bubble Column Reactor ◽  
Intelligent Algorithm ◽  
Column Reactor ◽  
Reactor Hydrodynamics

A structured catalytic bubble column reactor: hydrodynamics and mixing studies

2001 ◽  
Vol 69 (1-4) ◽  
pp. 105-113 ◽  
Author(s):  
M.I. Urseanu ◽  
J. Ellenberger ◽  
R. Krishna
Keyword(s):  
Bubble Column ◽  
Bubble Column Reactor ◽  
Column Reactor ◽  
Reactor Hydrodynamics

scholarly journals Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rasool Pelalak ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
Saeed Shirazian
Keyword(s):  
Machine Learning ◽  
Bubble Column ◽  
Input Parameter ◽  
Bubble Column Reactor ◽  
Learning Approach ◽  
Membership Functions ◽  
Column Reactor ◽  
Input And Output ◽  
Machine Learning Approach ◽  
Anfis Method

AbstractTo understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas–liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.

Flow visualization of bubble structure in bubble column reactor for fluid mixing

2017 ◽  
Author(s):  
Nur Afizah Ibrahim ◽  
Amir Khalid ◽  
Izzuddin Zaman ◽  
Azwan Sapit ◽  
Bukhari Manshoor
Keyword(s):  
Flow Visualization ◽  
Bubble Column ◽  
Fluid Mixing ◽  
Bubble Column Reactor ◽  
Column Reactor ◽  
Bubble Structure

Modeling and simulation of a bench-scale bubble column reactor for slurry phase hydrocracking of vacuum residue

Fuel ◽  
2021 ◽  
pp. 122481
Author(s):  
Hung Hai Pham ◽  
Suk Hyun Lim ◽  
Kang Seok Go ◽  
Nam Sun Nho ◽  
Eun Hee Kwon ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
Bubble Column ◽  
Bubble Column Reactor ◽  
Vacuum Residue ◽  
Column Reactor ◽  
Bench Scale ◽  
Slurry Phase
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