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.

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.

Gas-Liquid Phase Recirculation in Bubble Column Reactors: Development of a Hybrid Model Based on Local CFD – Adaptive Neuro-Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 44 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Mashallah Rezakazemi ◽  
Saeed Shirazian
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Scale Up ◽  
Flow Simulation ◽  
Continuous Phase ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Anfis Method ◽  
Different Dimensions

Abstract The Euler–Euler method and soft computing methods are recently utilized for the purpose of bubbly flow simulation and evolution of the dispersed and continuous phase in a two-phase reactor. Joining computational fluid dynamics (CFD) to the adaptive neuro-fuzzy inference system (ANFIS) method can enable the researchers to avoid several runs for heavy numerical methods (multidimensional Euler–Euler) to optimize fluid conditions. This overview can also help the researchers to carefully analyze fluid conditions and categorize their huge number of data in their artificial neural network nodes and avoid a complex non-structure CFD mesh. In addition, it can provide a neural geometry without limitation of an increasing mesh number in the fluid domain. In this study, gas and liquid circulation were considered as one of the main CFD factors in the scale-up of reactors used as an output parameter for prediction tool (ANFIS method) in different dimensions. This study shows that a combination of ANFIS and CFD methods provides the non-discrete domain in various dimensions and makes a smart tool to locally predict multiphase flow. The integration of numerical calculation and smart methods also shows that there is a great agreement between CFD results and ANFIS output depending on different dimensions.

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

2019 ◽  
Author(s):  
Shahab Shamshirband ◽  
Amir Mosavi ◽  
Kwok-wing Chau
Keyword(s):  
Multiphase Flow ◽  
Pressure Gradient ◽  
Learning Process ◽  
Bubble Column ◽  
Bubble Column Reactor ◽  
Model Parameters ◽  
Function Type ◽  
Column Reactor ◽  
Anfis Method ◽  
Inputs And Outputs

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 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.

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

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Shahab Shamshirband ◽  
Kwok-wing Chau
Keyword(s):  
Pressure Gradient ◽  
Learning Process ◽  
Chemical Engineering ◽  
Bubble Column ◽  
Bubble Column Reactor ◽  
Model Parameters ◽  
Function Type ◽  
Column Reactor ◽  
Anfis Method ◽  
Inputs And Outputs

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 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. 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  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.

scholarly journals Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Azam Marjani ◽  
Saeed Shirazian
Keyword(s):  
Liquid Velocity ◽  
Bubble Column ◽  
Machine Learning Algorithms ◽  
Column Height ◽  
Bubble Column Reactor ◽  
Data Set ◽  
Inference System ◽  
Column Reactor ◽  
Anfis Method ◽  
Cfd Method

AbstractFor understanding the complex behavior of fluids in a multiphase chemical bubble column reactor, a combination of the computational fluid dynamic (CFD) method and the adaptive network-based fuzzy inference system (ANFIS) method is used to predict bubble flow inside a reactor based on the function of column height. In this study, the Euler–Euler model is employed as a CFD method. In the Eulerian method, continuity and momentum governing equations are mathematically computed for each phase, while the equations are connected together by source terms. After calculating the flow pattern and turbulence flow in the reactor, all data sets are used to prepare a fully artificial method for further prediction. This algorithm contains different learning dimensions such as learning in different directions of reactor or large amount of input parameters and data set representing “big data”. The ANFIS method was evaluated in three steps by using one, two, and three inputs in each one to predict the liquid velocity in the x-direction (Ux). The x, y, and z coordinates of the location of the node of the liquid were considered as the inputs. Different percentages of data and various iterations and membership functions were used for training in the ANFIS method. The ANFIS method showed the best prediction using three inputs. This combination also shows the ability of computer science and computational methods in learning physical and chemical phenomena.

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.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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