@ARTICLE{Hanief_Mohammad_Modeling_2022,
 author={Hanief, Mohammad and Irfan, Qureshi and Parvez, Malik},
 volume={vol. 43},
 number={No 2 (The International Chemical Engineering Conference 2021 (ICHEEC): 100 Glorious Years of Chemical Engineering and Technology, held from September 16–19, 2021 at Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India. Guest editor: Dr Raj Kumar Arya, Dr Anurag Kumar Tiwari)},
 journal={Chemical and Process Engineering},
 pages={159-163},
 howpublished={online},
 year={2022},
 publisher={Polish Academy of Sciences Committee of Chemical and Process Engineering},
 abstract={In this study, the thermal conductivity ratio model for metallic oxide based nano-fluids is proposed. The model was developed by considering the thermal conductivity as a function of particle concentration (percentage volume), temperature, particle size and thermal conductivity of the base fluid and nano-particles. The experimental results for Al2O3, CuO, ZnO, and TiO2 particles dispersed in ethylene glycol, water and a combination of both were adopted from the literature. Artificial neural network (ANN) and power law models were developed and compared with the experimental data based on statistical methods. ANOVA was used to determine the relative importance of contributing factors, which revealed that the concentration of nano-particles in a fluid is the single most important contributing factor of the conductivity ratio.},
 type={Article},
 title={Modeling and prediction of thermal conductivity ratio of metal-oxide based nano-fluids using artificial neural network and power law},
 URL={http://ochroma.man.poznan.pl/Content/124695/PDF-MASTER/art06_int.pdf},
 doi={10.24425/cpe.2022.140818},
 keywords={nano-fluids, thermal conductivity ratio, artificial neural network, regression, ANOVA},
}