Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS

RSC Advances ◽  
2015 ◽  
Vol 5 (104) ◽  
pp. 85652-85672 ◽  
Author(s):  
M. Pourtousi ◽  
Mohammadjavad Zeinali ◽  
P. Ganesan ◽  
J. N. Sahu
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Bubble Column Reactor ◽  
Inference System ◽  
Column Reactor ◽  
Flow Characterization ◽  
Computational Fluid Dynamics Cfd

This work presents a combination of Computational Fluid Dynamics (CFD) and Adaptive Network-based Fuzzy Inference System (ANFIS) developed for flow characterization inside a cylindrical bubble column reactor.

scholarly journals Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
Saeed Shirazian
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Thermal Properties ◽  
Fuzzy Inference ◽  
Learning Processes ◽  
Membership Functions ◽  
Inference System ◽  
Computational Fluid Dynamics Cfd ◽  
Partition Clustering ◽  
Training Background

AbstractA nanofluid containing water and nanoparticles made of copper (Cu) inside a cavity with square shape is simulated utilizing the computational fluid dynamics (CFD) approach. The nanoparticles made up 15% of the nanofluid. By performing the simulation, the CFD output is characterized by the coordinates in the x, y, nanofluid temperature, and velocity in the y-direction that these outputs are obtained for different physical time iterations. Moreover, the CFD outputs are examined by one of the artificial techniques, i.e. adaptive network-based fuzzy inference system (ANFIS). For this purpose, the data was clustered via grid partition clustering, and the type of membership functions (MFs) was chosen product of two sigmoidal membership functions (psigmf). After reaching 99.9% of intelligence in ANFIS, the nanofluid temperature is predicted for the entire data, which are included in the learning processes. The results showed that the method of ANFIS can predict the thermal properties in different physical times at different computing points without having a training background at those times. Additionally, this study shows that with three membership functions at each input, the model’s accuracy is higher than four functions.

scholarly journals Prediction of Multi-Inputs Bubble Column Reactor Using a Novel Hybrid Model of Computational Fluid Dynamics and Machine Learning

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Shahab Shamshirband ◽  
Ely Salwana ◽  
Kwok-wing Chau ◽  
Joseph H. M. Tah
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Bubble Column Reactor ◽  
Model Parameters ◽  
Gas Velocity ◽  
Inference System ◽  
Anfis Method ◽  
Input Variables

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system  (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

scholarly journals Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Pso Algorithm ◽  
Cfd Modeling ◽  
Swarm Optimization ◽  
Inference System ◽  
Mesh Density

AbstractHerein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

Validation of a Computationally Efficient Computational Fluid Dynamics (CFD) Model for Industrial Bubble Column Bioreactors

2014 ◽  
Vol 53 (37) ◽  
pp. 14526-14543 ◽  
Author(s):  
Dale D. McClure ◽  
Hannah Norris ◽  
John M. Kavanagh ◽  
David F. Fletcher ◽  
Geoffrey W. Barton
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Bubble Column ◽  
Cfd Model ◽  
Computationally Efficient ◽  
Computational Fluid Dynamics Cfd
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