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2 Chemical and Process Engineering

2 2022

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Modeling and prediction of thermal conductivity ratio of metal-oxide based nano-fluids using artificial neural network and power law

Hanief Mohammad, Irfan Quresh, Parvez Malik

Chemical and Process Engineering

2022

Vol. 43, nr 2

159–-163

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.

Enhancement of heat transfer in helical coil heat exchangers using nano-fluids

Parveez Malik, Hanief Mohammad

Chemical and Process Engineering

2022

Vol. 43, nr 2

279--283

Investigation for heat transfer behaviour of Al2O3 and CuO nano-fluid in helical coil heat exchangers was carried out in this study. The thermo-physical properties of the fluids have temperature dependent nature. The main emphasis was to depict the influence of nano-particle concentration by volume on the characteristics of temperature, rate of heat transfer and heat transfer coefficients (convective). In order to enhance efficiency, density and thermal conductivity are considered to be the most important variables. In comparison to water and for equal flow rate, the rate of heat transfer of nano-fluid increases conspicuously. Efficiency of the helical coil heat exchanger increased by 38.80%.