TY - JOUR
T1 - Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids
AU - Maqsood, Khuram
AU - Ali, Abulhassan
AU - Ilyas, Suhaib Umer
AU - Garg, Sahil
AU - Danish, Mohd
AU - Abdulrahman, Aymn
AU - Rubaiee, Saeed
AU - Alsaady, Mustafa
AU - Hanbazazah, Abdulkader S.
AU - Mahfouz, Abdullah Bin
AU - Ridha, Syahrir
AU - Mubashir, Muhammad
AU - Lim, Hooi Ren
AU - Khoo, Kuan Shiong
AU - Show, Pau Loke
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-20
PY - 2022/1/1
Y1 - 2022/1/1
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%.
AB - 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%.
UR - https://linkinghub.elsevier.com/retrieve/pii/S0045653521021627
UR - http://www.scopus.com/inward/record.url?scp=85111558607&partnerID=8YFLogxK
U2 - 10.1016/j.chemosphere.2021.131690
DO - 10.1016/j.chemosphere.2021.131690
M3 - Article
C2 - 34352553
SN - 1879-1298
VL - 286
JO - Chemosphere
JF - Chemosphere
ER -