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

被引：7

作者：

Boldoo, Tsogtbilegt ^{[1
]}

Lee, Minjung ^{[1
]}

Kang, Yong Tae ^{[2
]}

Cho, Honghyun ^{[3
]}

机构：

[1] Chosun Univ, Dept Mech Engn, Grad Sch, 309 Pilmundaero, Gwangju 61452, South Korea

[2] Korea Univ, Sch Mech Engn, 145 Anam Ro, Seoul 02841, South Korea

[3] Chosun Univ, Dept Mech Engn, 309 Pilmundaero, Gwangju 61452, South Korea

来源：

JOURNAL OF MOLECULAR LIQUIDS | 2021年 / 341卷

基金：

新加坡国家研究基金会;

关键词：

Artificial neural network; Ionic liquid; Multiwalled carbon nanotube; Dynamic viscosity; Specific heat; ABSORPTION-REFRIGERATION SYSTEM; THERMAL-CONDUCTIVITY; ANTIFREEZE; SOLVENTS; DENSITY;

D O I：

10.1016/j.molliq.2021.117356

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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][Pf(6)] 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%. (C) 2021 Elsevier B.V. All rights reserved.

引用

页数：16

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