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

Pishnamazi, Mahboubeh; Babanezhad, Meisam; Nakhjiri, Ali Taghvaie; Rezakazemi, Mashallah; Marjani, Azam; Shirazian, Saeed

doi:10.1038/s41598-020-72182-5

Cited by 36 publications

(8 citation statements)

References 47 publications

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“…It stands for an adaptive neurofuzzy inference system. It has the ability to accurately predict the performance of particular systems which have complex as well as nonlinear characteristics (Babanezhad, Masoumian, Nakhjiri, Marjani, & Shirazian, 2020;Najafi, Faizollahzadeh Ardabili, Shamshirband, Chau, & Rabczuk, 2018;Pishnamazi, Babanezhad, Nakhjiri, Rezakazemi, Marjani, et al, 2020;R. Razavi, Sabaghmoghadam, Bemani, Baghban, Chau, et al, 2019;Rezakazemi, Dashti, Asghari, & Shirazian, 2017;Sefeedpari, Rafiee, Akram, Chau, & Komleh, 2015;Yan, Safdari, & Kim, 2020).…”

Section: Methodsmentioning

confidence: 99%

Diffusion Analysis With High and Low Concentration Regions by Finite Difference Method, Adaptive Network-Based Fuzzy Inference System, and Bilayered Neural Network Method

Shao¹,

Ardabili²,

Mafarja³

et al. 2021

Preprint

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The diffusion of molecules in aqueous solutions in the domain of membrane technology is very critical in the efficiency of chemical engineering and purification processes. In this study, the diffusion in high and low concentration regions is simulated with finite difference method (FDM), and then the results of numerical computations are coupled with adaptive network-based fuzzy inference system (ANFIS) and bilayered neural network method (BNNM). Machine learning approach can individually predict diffusion phenomena across the domain based on understanding of the machine instead of the discretization of an ordinary differential equation (ODE). The findings of the machine learning method are in good agreement with those of FDM at different times of the simulation. In addition to numerical computation, the error of the system is computed for different iterations. The results show that by increasing the number of iterations and training datasets, all errors reduce significantly for both training and testing. BNN method is also used to train the prediction process of diffusion for a more accurate comparison. This technique is similar to ANFIS method in terms of prediction capability. According to the findings, ANFIS approach predicts diffusion slightly better than BNN method. In this regard, ANFIS technique produces R>0.99 while BNN method produces R around 0.98. Both machine learning methods are accurate enough to predict diffusion throughout the domain for different time steps. The computational time for both algorithms is less than that of FDM method to predict low and high concentrations in the domain. Besides, based on the results, artificial intelligence (AI) can find the relationship between inputs and outputs and determine which input has the main influence on the output in this study to optimize the process. As such, future studies can be focused on AI and other methods for faster prediction and optimization processes.

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Section: Methodsmentioning

confidence: 99%

Diffusion Analysis With High and Low Concentration Regions by Finite Difference Method, Adaptive Network-Based Fuzzy Inference System, and Bilayered Neural Network Method

Shao¹,

Ardabili²,

Mafarja³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…ANFIS has the ability to predict accurately the performance of particular systems that have complex as well as nonlinear characteristics (Babanezhad et al, 2020;Najafi et al, 2018;Pishnamazi et al, 2020;Razavi et al, 2019;Rezakazemi et al, 2017;Sefeedpari et al, 2015;Yan et al, 2020). ANFIS can be considered a sort of artificial neural network, and its origin is from Takagi et al (1985).…”

Section: Methodsmentioning

confidence: 99%

Diffusion analysis with high and low concentration regions by the finite difference method, the adaptive network-based fuzzy inference system, and the bilayered neural network method

Shao

Ardabili

Mafarja

et al. 2021

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

The diffusion of molecules in aqueous solutions in the domain of membrane technology is critical in the efficiency of chemical engineering and purification processes. In this study, the diffusion in high and low concentration regions is simulated with finite difference method (FDM), and then the results of numerical computations are coupled with adaptive network-based fuzzy inference system (ANFIS) and bilayered neural network method (BNNM). Machine learning (ML) approach can individually predict diffusion phenomena across the domain based on understanding of the machine instead of the discretization of an ordinary differential equation (ODE). The findings of the ML model confirm the FDM's simulation results. In addition to numerical computation, the error of the system is computed for different iterations. The results show that by increasing the number of iterations and training datasets, all errors reduce significantly for both training and testing. BNN method is also used to train the prediction process of diffusion for a more accurate comparison. This technique is similar to ANFIS method in terms of prediction capability. According to the findings, ANFIS approach predicts diffusion slightly better than BNN method. In this regard, ANFIS technique produces R > 0.99 while BNN method produces R around 0.98. Both ML methods are accurate enough to predict diffusion throughout the domain for different time steps. The computational time for both algorithms is less than that of FDM method to predict low and high concentrations in the domain.

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“…The mass transfer coefficient for CO 2 absorption would be improved when NPs are added to the absorbent solution. The interphase boundary layer mixing because of the presence of NP and Grazing effect phenomena occurs when NPs are properly dispersed in the solvent media 10 – 13 . NPs dispersion increases the fluid turbulence in the gas–liquid boundary layer and subsequently improves CO 2 mass transfer rate 14 .…”

Section: Introductionmentioning

confidence: 99%

Intensification of CO2 absorption using MDEA-based nanofluid in a hollow fibre membrane contactor

Cao

Rehman

Ghasem

et al. 2021

Sci Rep

Self Cite

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Porous hollow fibres made of polyvinylidene fluoride were employed as membrane contactor for carbon dioxide (CO2) absorption in a gas–liquid mode with methyldiethanolamine (MDEA) based nanofluid absorbent. Both theoretical and experimental works were carried out in which a mechanistic model was developed that considers the mass transfer of components in all subdomains of the contactor module. Also, the model considers convectional mass transfer in shell and tube subdomains with the chemical reaction as well as Grazing and Brownian motion of nanoparticles effects. The predicted outputs of the developed model and simulations showed that the dispersion of CNT nanoparticles to MDEA-based solvent improves CO2 capture percentage compared to the pure solvent. In addition, the efficiency of CO2 capture for MDEA-based nanofluid was increased with rising MDEA content, liquid flow rate and membrane porosity. On the other hand, the enhancement of gas velocity and the membrane tortuosity led to reduced CO2 capture efficiency in the module. Moreover, it was revealed that the CNT nanoparticles effect on CO2 removal is higher in the presence of lower MDEA concentration (5%) in the solvent. The model was validated by comparing with the experimental data, and great agreement was obtained.

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

Cited by 36 publications

References 47 publications

Diffusion Analysis With High and Low Concentration Regions by Finite Difference Method, Adaptive Network-Based Fuzzy Inference System, and Bilayered Neural Network Method

Diffusion Analysis With High and Low Concentration Regions by Finite Difference Method, Adaptive Network-Based Fuzzy Inference System, and Bilayered Neural Network Method

Diffusion analysis with high and low concentration regions by the finite difference method, the adaptive network-based fuzzy inference system, and the bilayered neural network method

Intensification of CO2 absorption using MDEA-based nanofluid in a hollow fibre membrane contactor

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