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Fluid Velocity Prediction Inside Bubble Column Reactor Using ANFIS Algorithm Based on CFD Input Data

  • Research Article-Chemical Engineering
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Abstract

Since machine learning and smart methods can be used to study hydrodynamics in the bubble column reactor, it is possible to create highly intelligent bubble column reactors that have not been previously simulated and optimized them with computational fluid dynamics (CFD) methods. The previous studies considered the position of each node (in three directions) inside the bubble column reactor as the input in the artificial intelligence model. Machine learning methods have been used for processing big data related to the bubble column reactor. These big data are associated with the points inside the bubble column reactor, which represent the gas volume fraction and the fluid velocity in the x-direction. In this study, adaptive-network-based fuzzy inference system (ANFIS) was used to find out the relationship between the outputs of the bubble column reactor. The present study also intends to investigate the relationship between two outputs, namely the amount of gas in the bubble column reactor and the velocity of the fluid in the x-direction. Various parameters were investigated in this system, including the number of rules, the type of membership function, and the amount of input data. The mentioned parameters were regularly changed to find out the state where the system can achieve its intelligence. In this study, the best parameter that helped the system was the amount of data in the training process. The results showed that the lower the amount of data used in training, the better the prediction.

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Abbreviations

C D :

Coefficient of drag force for dispersed phase (–)

C TD :

Turbulent dispersion coefficient for dispersed phase (–)

\(C_{\varepsilon 1}\) :

Turbulent dissipation energy equation (–)

\(C_{\varepsilon 2}\) :

Turbulent dissipation energy equation (–)

\(C_{\mu }\) :

Constant in turbulence modeling of dispersed phase (–)

\(C_{{\mu ,{\text{BI}}}}\) :

Constant in bubble-induced turbulence modeling of dispersed phase (–)

d B :

Dispersed phase size (m)

D :

Size of reactor (m)

D S :

Ring sparger size (m)

g :

Gravitational force in modeling (m s−2)

H :

Height of reactor in modeling (m)

k :

Turbulent kinetic energy for modeling of dispersed phase (m2 s−2)

M I :

Interfacial force (N m−3)

M D :

Drag force for modeling of dispersed phase (N m−3)

P :

Pressure in the reactor (N m−2)

MF:

Membership function for ANFIS modeling

RMSE:

Root mean square error for ANFIS modeling

\(\varepsilon\) :

Turbulent energy dissipation rate per unit mass (m2 s−3)

\(\in\) :

Phase holdup (–)

\(\bar{ \in }\) :

Average phase holdup (–)

\(\mu\) :

Molecular viscosity (Pa s−1)

\(\mu_{\text{BI}}\) :

Bubble-induced viscosity (Pa s−1)

\(\mu_{\text{eff}}\) :

Effective viscosity (Pa s−1)

\(\rho\) :

Density of phases (kg m−3)

\(\mu_{\text{T}}\) :

Turbulent viscosity (Pa s−1)

\(\tau_{k}\) :

Shear stress of phase k (Pa)

\(\varepsilon_{\text{g}}\) :

Volume of dispersed phase (–)

G :

Dispersed phase

L :

Matrix/Continuous phase

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Author information

Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

    Quyen Nguyen

  2. Mechanical and Energy Engineering Department, Shahid Beheshti University, Tehran, Iran

    Iman Behroyan

  3. Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran

    Mashallah Rezakazemi

  4. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Saeed Shirazian

  5. Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Saeed Shirazian

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  1. Quyen Nguyen
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  2. Iman Behroyan
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  3. Mashallah Rezakazemi
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  4. Saeed Shirazian
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Correspondence to Saeed Shirazian.

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Nguyen, Q., Behroyan, I., Rezakazemi, M. et al. Fluid Velocity Prediction Inside Bubble Column Reactor Using ANFIS Algorithm Based on CFD Input Data. Arab J Sci Eng 45, 7487–7498 (2020). https://doi.org/10.1007/s13369-020-04611-6

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  • DOI: https://doi.org/10.1007/s13369-020-04611-6

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