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 relate… Show more
“…The diameter of the orifice hole is 0.7 mm, and these orifices are arranged in a regular shape. 29 The operating pressure and temperature of the bubble column are designed based on ambient conditions. The top surface of the reactor is opened and connected to the air.…”
Section: Methodsmentioning
confidence: 99%
“…The derived equation for the momentum transfer in the gas phase and the liquid phase 14 is as follows 29 …”
A bubble column reactor
is simulated by a combination of Euler–Euler
and adaptive network-based fuzzy inference system (ANFIS) method to
develop an understanding of the machine learning (ML) technique in
describing complex behavior of multiphase flow in bubble column reactors
and for deep learning of input and output connections. In the validation
stage of simulations, an intelligent bubble column is created that
uses artificial intelligence nodes or neural network nodes, and the
results of prediction indicated excellent agreement with computational
fluid dynamics (CFD) simulation results. The hydrodynamic characteristics
of the air bubbles and the amount of stress inside the bubble column
reactor are used as the output of the ANFIS method. This study showed
that when a three-dimensional bubble column is trained by a ML method,
a similar CFD simulation can be created, which is independent of CFD
source data. This type of smart simulation also enables us to avoid
repeating the simulations with CFD methods that are time-consuming
and computationally expensive for process modeling and optimization.
“…The diameter of the orifice hole is 0.7 mm, and these orifices are arranged in a regular shape. 29 The operating pressure and temperature of the bubble column are designed based on ambient conditions. The top surface of the reactor is opened and connected to the air.…”
Section: Methodsmentioning
confidence: 99%
“…The derived equation for the momentum transfer in the gas phase and the liquid phase 14 is as follows 29 …”
A bubble column reactor
is simulated by a combination of Euler–Euler
and adaptive network-based fuzzy inference system (ANFIS) method to
develop an understanding of the machine learning (ML) technique in
describing complex behavior of multiphase flow in bubble column reactors
and for deep learning of input and output connections. In the validation
stage of simulations, an intelligent bubble column is created that
uses artificial intelligence nodes or neural network nodes, and the
results of prediction indicated excellent agreement with computational
fluid dynamics (CFD) simulation results. The hydrodynamic characteristics
of the air bubbles and the amount of stress inside the bubble column
reactor are used as the output of the ANFIS method. This study showed
that when a three-dimensional bubble column is trained by a ML method,
a similar CFD simulation can be created, which is independent of CFD
source data. This type of smart simulation also enables us to avoid
repeating the simulations with CFD methods that are time-consuming
and computationally expensive for process modeling and optimization.
“…However, for the output parameter, the flow characteristics such as velocity distribution is used in the training method. Detailed descriotion of ANFIS method can be found in our previous publications 23 , 24 , 27 – 29 , 46 , 48 – 50 . Figure 2 ANFIS structure with three inputs, number of inputs MFs = 4, type of MFs = dsigmf .…”
In this study, a square cavity is modeled using Computational Fluid Dynamics (CFD) as well as artificial intelligence (AI) approach. In the square cavity, copper (Cu) nanoparticle is the nanofluid and the flow velocity characteristics in the x-direction and y-direction, and the fluid temperature inside the cavity at different times are considered as CFD outputs. CFD outputs have been assessed using one of the artificial intelligence algorithms, such as a combination of neural network and fuzzy logic (ANFIS). As in the ANFIS method, we have a non-dimension procedure in the learning step, and there is no issue in combining other characteristics of the flow and thermal distribution beside the x and y coordinates, we combine two coordinate parameters and one flow parameter. This ability of method can be considered as a meshless learning step that there is no instability of the numerical method or limitation of boundary conditions. The data were classified using the grid partition method and the MF (membership function) type was dsigmf (difference between two sigmoidal membership functions). By achieving the appropriate intelligence in the ANFIS method, output prediction was performed at the points of cavity which were not included in the learning process and were compared to the existing data (the results of the CFD method) and were validated by them. This new combination of CFD and the ANFIS method enables us to learn flow and temperature distribution throughout the domain thoroughly, and eventually predict the flow characteristics in short computational time. The results from AI in the ANFIS method were compared to the ant colony and fuzzy logic methods. The data from CFD results were inserted into the ant colony system for the training process, and we predicted the data in the fuzzy logic system. Then, we compare the data with the ANFIS method. The results indicate that the ANFIS method has a high potentiality compared to the ant colony method because the amount of R in the ANIFS system is higher than R in the ant colony method. In the ANFIS method, R is equal to 0.99, and in the ant colony method, R is equal to 0.91. This shows that the ant colony needs more time for both the prediction and training of the system. Also, comparing the pattern recognition in the two systems, we can obviously see that by using the ANFIS method, the predictions completely match the target points. But the other method cannot match the flow pattern and velocity distribution with the CFD method.
“…Recently some studies considered the contribution of the ANFIS (adaptive network-based fuzzy inference system) with the CFD to predict fluid flow characteristics 15 – 17 . The studies just simply discussed the application of the ANFIS for the facilitation of the CFD.…”
The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system’s boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.
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