Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids

Maqsood K 1 , Ali A 2 , Ilyas SU 3 , Garg S 4 , Danish M 5 , Abdulrahman A 1 Show all authors , Rubaiee S 6 , Alsaady M 1 , Hanbazazah AS 7 , Mahfouz AB 1 , Ridha S 3 , Mubashir M 8 , Lim HR 9 , Khoo KS 9 , Show PL 10

Affiliations 

  • 1 Department of Chemical Engineering, University of Jeddah, Jeddah, Saudi Arabia
  • 2 Department of Chemical Engineering, University of Jeddah, Jeddah, Saudi Arabia. Electronic address: [email protected]
  • 3 Institute of Hydrocarbon Recovery, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia
  • 4 School of Chemical Engineering, The University of Queensland, St Lucia, 4072, Australia. Electronic address: [email protected]
  • 5 Department of Mechanical and Materials Engineering, University of Jeddah, Jeddah, Saudi Arabia
  • 6 Department of Mechanical and Materials Engineering, University of Jeddah, Jeddah, Saudi Arabia; Department of Industrial and Systems Engineering, University of Jeddah, Jeddah, Saudi Arabia
  • 7 Department of Industrial and Systems Engineering, University of Jeddah, Jeddah, Saudi Arabia
  • 8 Department of Petroleum Engineering, School of Engineering, Asia Pacific University of Technology and Innovation, 57000, Kuala Lumpur, Malaysia
  • 9 Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, 43500, Selangor Darul Ehsan, Malaysia
  • 10 Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, 43500, Selangor Darul Ehsan, Malaysia. Electronic address: [email protected]
Chemosphere, 2022 Jan;286(Pt 2):131690.
PMID: 34352553 DOI: 10.1016/j.chemosphere.2021.131690

Abstract

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%.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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