Development of artificial neural network model for predicting dynamic viscosity and specific heat of MWCNT nanoparticle-enhanced ionic liquids with different [HMIM]-cation base agents

Tsogtbilegt Boldoo; Minjung Lee; Yong Tae Kang; Honghyun Cho

doi:10.1016/j.molliq.2021.117356

Development of artificial neural network model for predicting dynamic viscosity and specific heat of MWCNT nanoparticle-enhanced ionic liquids with different [HMIM]-cation base agents

Tsogtbilegt Boldoo, Minjung Lee, Yong Tae Kang, Honghyun Cho

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

The specific heat and dynamic viscosity of various 1-hexyl-3-methylimidazolium [HMIM]-cation with multiwalled carbon nanotube (MWCNT) nanoparticles are measured and used to develop an artificial neural network (ANN) model. The specific heat values of [C₁₂MIM][Tf₂N], [HMIM][Tf₂N], [HMIM][TfO], and [HMIM][Pf₆] ionic-liquid-based MWCNT nanofluids decrease with increasing nanoparticle concentration and increase with temperature. Also, the dynamic viscosity of the MWCNT nanoparticle-enhanced ionic liquids decreases at low concentrations; however, it increases significantly when the concentration increases up to 1 wt%. A new ANN model for predicting the dynamic viscosity and specific heat is developed, and the predictive values agree with the experimental data with high accuracy. The mean square error and R-value of the proposed predictive ANN model are 0.001291 and 0.9985, respectively. The maximum margin of deviation of the proposed ANN model for dynamic viscosity and specific heat is 9.63% and 4.3%.

Original language	English
Article number	117356
Journal	Journal of Molecular Liquids
Volume	341
DOIs	https://doi.org/10.1016/j.molliq.2021.117356
Publication status	Published - 2021 Nov 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government (MSIT) (No. 2020R1A5A118153).

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

Artificial neural network
Dynamic viscosity
Ionic liquid
Multiwalled carbon nanotube
Specific heat

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Condensed Matter Physics
Spectroscopy
Physical and Theoretical Chemistry
Materials Chemistry

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10.1016/j.molliq.2021.117356

Cite this

Development of artificial neural network model for predicting dynamic viscosity and specific heat of MWCNT nanoparticle-enhanced ionic liquids with different [HMIM]-cation base agents. / Boldoo, Tsogtbilegt; Lee, Minjung; Kang, Yong Tae et al.
In: Journal of Molecular Liquids, Vol. 341, 117356, 01.11.2021.

Research output: Contribution to journal › Article › peer-review

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title = "Development of artificial neural network model for predicting dynamic viscosity and specific heat of MWCNT nanoparticle-enhanced ionic liquids with different [HMIM]-cation base agents",

abstract = "The specific heat and dynamic viscosity of various 1-hexyl-3-methylimidazolium [HMIM]-cation with multiwalled carbon nanotube (MWCNT) nanoparticles are measured and used to develop an artificial neural network (ANN) model. The specific heat values of [C12MIM][Tf2N], [HMIM][Tf2N], [HMIM][TfO], and [HMIM][Pf6] ionic-liquid-based MWCNT nanofluids decrease with increasing nanoparticle concentration and increase with temperature. Also, the dynamic viscosity of the MWCNT nanoparticle-enhanced ionic liquids decreases at low concentrations; however, it increases significantly when the concentration increases up to 1 wt%. A new ANN model for predicting the dynamic viscosity and specific heat is developed, and the predictive values agree with the experimental data with high accuracy. The mean square error and R-value of the proposed predictive ANN model are 0.001291 and 0.9985, respectively. The maximum margin of deviation of the proposed ANN model for dynamic viscosity and specific heat is 9.63% and 4.3%.",

keywords = "Artificial neural network, Dynamic viscosity, Ionic liquid, Multiwalled carbon nanotube, Specific heat",

author = "Tsogtbilegt Boldoo and Minjung Lee and Kang, {Yong Tae} and Honghyun Cho",

note = "Funding Information: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government (MSIT) (No. 2020R1A5A118153). Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

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AU - Kang, Yong Tae

AU - Cho, Honghyun

N1 - Funding Information: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government (MSIT) (No. 2020R1A5A118153). Publisher Copyright: © 2021 Elsevier B.V.

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N2 - The specific heat and dynamic viscosity of various 1-hexyl-3-methylimidazolium [HMIM]-cation with multiwalled carbon nanotube (MWCNT) nanoparticles are measured and used to develop an artificial neural network (ANN) model. The specific heat values of [C12MIM][Tf2N], [HMIM][Tf2N], [HMIM][TfO], and [HMIM][Pf6] ionic-liquid-based MWCNT nanofluids decrease with increasing nanoparticle concentration and increase with temperature. Also, the dynamic viscosity of the MWCNT nanoparticle-enhanced ionic liquids decreases at low concentrations; however, it increases significantly when the concentration increases up to 1 wt%. A new ANN model for predicting the dynamic viscosity and specific heat is developed, and the predictive values agree with the experimental data with high accuracy. The mean square error and R-value of the proposed predictive ANN model are 0.001291 and 0.9985, respectively. The maximum margin of deviation of the proposed ANN model for dynamic viscosity and specific heat is 9.63% and 4.3%.

AB - The specific heat and dynamic viscosity of various 1-hexyl-3-methylimidazolium [HMIM]-cation with multiwalled carbon nanotube (MWCNT) nanoparticles are measured and used to develop an artificial neural network (ANN) model. The specific heat values of [C12MIM][Tf2N], [HMIM][Tf2N], [HMIM][TfO], and [HMIM][Pf6] ionic-liquid-based MWCNT nanofluids decrease with increasing nanoparticle concentration and increase with temperature. Also, the dynamic viscosity of the MWCNT nanoparticle-enhanced ionic liquids decreases at low concentrations; however, it increases significantly when the concentration increases up to 1 wt%. A new ANN model for predicting the dynamic viscosity and specific heat is developed, and the predictive values agree with the experimental data with high accuracy. The mean square error and R-value of the proposed predictive ANN model are 0.001291 and 0.9985, respectively. The maximum margin of deviation of the proposed ANN model for dynamic viscosity and specific heat is 9.63% and 4.3%.

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Development of artificial neural network model for predicting dynamic viscosity and specific heat of MWCNT nanoparticle-enhanced ionic liquids with different [HMIM]-cation base agents

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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