Accurate prediction of solubility of gases within H 2 -selective nanocomposite membranes using committee machine intelligent system

Dashti, Amir; Harami, Hossein Riasat; Rezakazemi, Mashallah

doi:10.1016/j.ijhydene.2018.02.046

Cited by 67 publications

(19 citation statements)

References 48 publications

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“…Researchers in the field of artificial intelligence have used experimental and numerical data and tried to test behavioral simulation of processes in engineering and science by artificial intelligence [24][25][26][27][28][29]. Some of them have used existing CFD data for training, testing, and predicting bubble column reactors [30][31][32][33]; however, due to the limitations of the artificial intelligence model in data training [34,35], it cannot be shown various hydrodynamic aspects of bubble column reactors [36]. There is also no connection between the outputs of the bubble column reactor.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…To improve the heat transmission features inactively, utilizing fluid with high thermal conductivity is usual. Furthermore, rich heat dissipation properties can be achieved by using material that has a higher surface area 4 – 6 . There are also several research studies focusing on the effect of physical parameters on the nanofluid system and the thermal distribution of nanofluid in physical problems 7 – 10 .…”

Section: Introductionmentioning

confidence: 99%

ANFIS grid partition framework with difference between two sigmoidal membership functions structure for validation of nanofluid flow

Pishnamazi

Babanezhad

Nakhjiri

et al. 2020

Sci Rep

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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.

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“…There are a number of soft computing methods including neural networks, support vector machines, evolutionary algorithms, simulated annealing, and adaptive neuro‐fuzzy inference system (ANFIS) that have been suggested in other literature to simulate physics in real‐life applications. For instance, ANFIS has attracted more attention due to its ability for training complicated relationships.…”

Section: Introductionmentioning

confidence: 99%

Liquid‐phase chemical reactors: Development of 3D hybrid model based on CFD‐adaptive network‐based fuzzy inference system

Babanezhad¹,

Rezakazemi

Hajilary³

et al. 2018

Can J Chem Eng

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The Euler-Euler method plus an intelligent algorithm was used to predict bubbly flow in a reactor as a function of column height. The combination of computational fluid dynamics (CFD) and the adaptive network-based fuzzy inference system (ANFIS) method was used for a chemical bubble column reactor to understand the complex behaviour of fluids in a multiphase reactor. Air fraction as one of the main factors in the scale-up of reactors was selected as an output parameter for the prediction tool (ANFIS method) at different positions in the reactor. To train and test the prediction ability of this method, a 3D position of one CFD element was selected and, based on that position, a training algorithm was started. After an appropriate learning step, the method was used to simulate gas at different locations of the reactor. The different structures of the ANFIS algorithm were designed to obtain a correct predictive tool for fluid behaviour inside the bubble column. The ANFIS approach shows that it can simulate the liquid behaviour and the CFD results and ANFIS output correlate with one another.

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Accurate prediction of solubility of gases within H 2 -selective nanocomposite membranes using committee machine intelligent system

Cited by 67 publications

References 48 publications

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

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

ANFIS grid partition framework with difference between two sigmoidal membership functions structure for validation of nanofluid flow

Liquid‐phase chemical reactors: Development of 3D hybrid model based on CFD‐adaptive network‐based fuzzy inference system

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