TY  - JOUR
VL  - 286
PB  - Elsevier Ltd
AV  - none
JF  - Chemosphere
ID  - scholars17897
A1  - Maqsood, K.
A1  - Ali, A.
A1  - Ilyas, S.U.
A1  - Garg, S.
A1  - Danish, M.
A1  - Abdulrahman, A.
A1  - Rubaiee, S.
A1  - Alsaady, M.
A1  - Hanbazazah, A.S.
A1  - Mahfouz, A.B.
A1  - Ridha, S.
A1  - Mubashir, M.
A1  - Lim, H.R.
A1  - Khoo, K.S.
A1  - Show, P.L.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111558607&doi=10.1016%2fj.chemosphere.2021.131690&partnerID=40&md5=d68f8741e0e23811e03c1c46197e146f
N1  - cited By 17
SN  - 00456535
N2  - The experimental determination of thermophysical properties of nanofluid (NF) is time-consuming and costly, leading to the use of soft computing methods such as response surface methodology (RSM) and artificial neural network (ANN) to estimate these properties. The present study involves modelling and optimization of thermal conductivity and viscosity of NF, which comprises multi-walled carbon nanotubes (MWCNTs) and thermal oil. The modelling is performed to predict the thermal conductivity and viscosity of NF by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Both models were tested and validated, which showed promising results. In addition, a detailed optimization study was conducted to investigate the optimum thermal conductivity and viscosity by varying temperature and NF weight per cent. Four case studies were explored using different objective functions based on NF application in various industries. The first case study aimed to maximize thermal conductivity (0.15985 W/m oC) while minimizing viscosity (0.03501 Pa s) obtained at 57.86 °C and 0.85 NF wt. The goal of the second case study was to minimize thermal conductivity (0.13949 W/m °C) and viscosity (0.02526 Pa s) obtained at 55.88 °C and 0.15 NF wt. The third case study targeted maximizing thermal conductivity (0.15797 W/m °C) and viscosity (0.07611 Pa s), and the optimum temperature and NF wt were 30.64 °C and 0.0.85,' respectively. The last case study explored the minimum thermal conductivity (0.13735) and maximum viscosity (0.05263 Pa s) obtained at 30.64 °C and 0.15 NF wt. © 2021 Elsevier Ltd
KW  - Multiobjective optimization; Multiwalled carbon nanotubes (MWCN); Nanofluidics; Soft computing; Surface properties; Thermal conductivity of liquids; Viscosity
KW  -  Case-studies; Experimental determination; Multi-objectives optimization; Multi-walled-carbon-nanotubes; Nanofluids; Neural-networks; Property; Response-surface methodology; Thermal; Thermophysical
KW  -  Neural networks
KW  -  artificial neural network; carbon nanotube; multiobjective programming; optimization; response surface methodology; thermal conductivity; viscosity
KW  -  carbon nanotube
KW  -  temperature; thermal conductivity; viscosity
KW  -  Nanotubes
KW  -  Carbon; Temperature; Thermal Conductivity; Viscosity
TI  - Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids
Y1  - 2022///
ER  -