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 journalArticlepeer-review

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 languageEnglish
Article number117356
JournalJournal of Molecular Liquids
Volume341
DOIs
Publication statusPublished - 2021 Nov 1

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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