%0 Journal Article
%@ 09307516
%A Ul Islam, B.
%A Mukhtar, A.
%A Saqib, S.
%A Mahmood, A.
%A Rafiq, S.
%A Hameed, A.
%A Khan, M.S.
%A Hamid, K.
%A Ullah, S.
%A Al-Sehemi, A.G.
%A Ibrahim, M.
%D 2020
%F scholars:12873
%I Wiley-VCH Verlag
%J Chemical Engineering and Technology
%K Backpropagation; Forecasting; Genetic algorithms; Multiwalled carbon nanotubes (MWCN); Nanotubes; Neural networks; Oils and fats; Thermal conductivity, Kapok seed oil; Levenberg-Marquardt; Multiwalled carbon nanotube (MWCNTs); Nano-particle dispersions; Nanofluids; One-step methods, Nanofluidics
%N 8
%P 1638-1647
%R 10.1002/ceat.201900600
%T Thermal Conductivity of Multiwalled Carbon Nanotubes-Kapok Seed Oil-Based Nanofluid
%U https://khub.utp.edu.my/scholars/12873/
%V 43
%X The synthesis of a nanofluid from multiwalled carbon nanotubes (MWCNTs) and Kapok seed oil by a one-step method is reported. The nanofluid showed excellent stability of nanoparticle dispersion in the base fluid. Furthermore, this study deals with the prediction of the thermal conductivity of the MWCNTs-kapok seed oil nanofluid. To improve the prediction of the thermal conductivity of the nanofluid, the artificial neural network (ANN) computing approach was used with different algorithms including the back-propagation, Levenberg-Marquardt, and genetic algorithm (GA). Finally, the ANN-GA model is recommended for the prediction of thermal conductivity with higher accuracy. © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
%Z cited By 4