Neural networks applied to determine the thermophysical properties of amino acid based ionic liquids

2016 ◽  
Vol 18 (10) ◽  
pp. 7435-7441 ◽  
Author(s):  
John C. Cancilla ◽  
Ana Perez ◽  
Kacper Wierzchoś ◽  
José S. Torrecilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ionic Liquids ◽  
Amino Acid ◽  
Thermophysical Properties ◽  
Artificial Neural

A series of models based on artificial neural networks (ANNs) have been designed to estimate the thermophysical properties of different amino acid-based ionic liquids (AAILs).

scholarly journals Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids

2020 ◽  
Author(s):  
Behzad Vaferi
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermophysical Properties ◽  
Convective Heat Transfer Coefficient ◽  
Industrial Applications ◽  
Mean Square ◽  
Ann Models ◽  
Artificial Neural ◽  
Mean Square Errors

Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid.

Density of Ionic Liquids Using Group Contribution and Artificial Neural Networks

2009 ◽  
Vol 48 (6) ◽  
pp. 3254-3259 ◽  
Author(s):  
José O. Valderrama ◽  
Alfonso Reátegui ◽  
Roberto E. Rojas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ionic Liquids ◽  
Group Contribution ◽  
Artificial Neural

Viscosity of ionic liquids using the concept of mass connectivity and artificial neural networks

2011 ◽  
Vol 28 (6) ◽  
pp. 1451-1457 ◽  
Author(s):  
José Omar Valderrama ◽  
Jéssica Makarena Muñoz ◽  
Roberto Erasmo Rojas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ionic Liquids ◽  
Artificial Neural
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