Comparison of Phenomenological and Hybrid Models in the Description of the Ion Exchange Process in a Fixed-Bed Column

D'Arisbo, T.; Silva, Edson Antônio da; Borba, Carlos Eduardo; Ostroski, Indianara Conceição; Arroyo, Pedro Augusto; Barros, Maria Angélica Simões Dornellas de

doi:10.1080/01496395.2012.723104

Cited by 2 publications

(1 citation statement)

References 26 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the prediction and optimization of some parameters in the mass transfer, the neural network could also be employed to build a high-precision model to simulate the entire mass transfer process. D'Arisbo et al 100 constructed two models, a phenomenological model based on the law of mass transfers and a mixed model based on ANN, to simulate the ion exchange in a fixed bed. The total concentration of the liquid phase, the equivalent fraction of the liquid phase, and the liquid phase sodium ion were input into ANN as the input parameters, while the equivalent fractions of the solid phase and the solid phase sodium ion were the outputs.…”

Section: Application Of Ai In Mass Transfermentioning

confidence: 99%

Application of Artificial Intelligence in Computational Fluid Dynamics

Wang

2021

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

This review discusses the recent application of artificial intelligence (AI) algorithms in five aspects of computational fluid dynamics: aerodynamic models, turbulence models, some specific flows, and mass and heat transfer. Currently, there are three main coupling models. The first is the data-driven model to obtain the input−output relationship without involving any physical mechanisms. The second is the physical model to optimize the existing models by AI algorithms. The third is the hybrid model involving both data and physical mechanisms. Among various AI algorithms, artificial neural network is usually applied to build data-driven models and has been successfully employed in the mentioned five fields. Other AI algorithms such as recursive neural network, support vector machine, and naive Bayes are mainly used for the physical models. Finally, the development tendency of coupling models and how to choose an appropriate model are given in the conclusions and prospects.

show abstract

Section: Application Of Ai In Mass Transfermentioning

confidence: 99%

Application of Artificial Intelligence in Computational Fluid Dynamics

Wang

2021

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

Prediction of ion exchange equilibrium of $$\hbox {Cu}^{2+}{-}\hbox {Na}^{+}{-}\hbox {Zn}^{2+}$$ Cu 2 + - Na + - Zn 2 + ternary system using artificial neural networks

et al. 2014

View full text Add to dashboard Cite

The law of mass action (LMA) is generally used to describe the equilibrium of ion exchange processes. This is a rigorous methodology in terms of thermodynamics and considers the non-idealities in the solid and solution phases. However, artificial neural networks (ANNs) can also be effectively used in phase equilibrium modeling. In the current study, ANNs were used to describe the ion exchange equilibrium in the binary systems Cu 2þ ÀNa þ , Zn 2þ ÀNa þ and Zn 2þ ÀCu 2þ and in the ternary system Cu 2þ ÀNa þ ÀZn 2þ , using the resin Amberlite IR 120 as ion exchanger. The datasets used in the training stage of the ANNs in this study were generated by the application of the LMA in the binary systems. Results showed that, in the equilibrium modeling of the binary systems and in the prediction of the ternary system, the two methodologies had similar performance and can be used to describe binary and ternary equilibrium.

show abstract

Comparison of Phenomenological and Hybrid Models in the Description of the Ion Exchange Process in a Fixed-Bed Column

Cited by 2 publications

References 26 publications

Application of Artificial Intelligence in Computational Fluid Dynamics

Application of Artificial Intelligence in Computational Fluid Dynamics

Prediction of ion exchange equilibrium of $$\hbox {Cu}^{2+}{-}\hbox {Na}^{+}{-}\hbox {Zn}^{2+}$$ Cu 2 + - Na + - Zn 2 + ternary system using artificial neural networks

Contact Info

Product

Resources

About