Iranian Journal of Chemical Engineering (IJChE)

Abstract In this study, the effect of the presence of a magnetic field (MF) on the thermal conductivity of the nanofluid (NF) ( ) containing spinel ferrite nanoparticles (NPs) (MFe₂O₄, M=Fe, Co) was investigated. CoFe₂O₄ NPs were concentrated by the coprecipitation method. Both NPs were characterized by SEM, EDX, XRD, and VSM. The thermal conductivity was investigated and compared in the presence and absence of an MF. In addition to the intensity of MF (100, 200, 300, and 400 G), the effect of the concentration of NPs (from 0.25 to 2 Vol%) on  at a constant temperature of 25 °C was investigated. According to the results, in the absence of MF, the  of CoFe₂O₄/water ferrofluid (FF) was higher than that of Fe₃O₄/water FF in different concentrations. Furthermore, as the intensity of the MF increased, the  of both Fe₃O₄/water and CoFe₂O₄/water FFs increased. This increase was more observed for the FFs containing Fe₃O₄ NPs. At the highest concentration (2 Vol%), with the increase of MF up to 400 G, the  of Fe₃O₄/water has increased by about 3.2%, while this increase was about 1.8% for CoFe₂O₄/water. Increasing the volume percentage of NPs also had a positive effect on the thermal conductivity coefficient. Finally, according to the obtained results, correlations were presented to predict the  of both FFs according to the intensity of the MF and the concentration of NPs. The proposed correlations had a satisfactory accuracy with R² values of 0.98 for both FFs.

Abstract The artificial neural network (ANN) approach was applied to develop simple correlations for predicting the thermal conductivity of nitrogen-methane and carbon dioxide-methane mixtures. The genetic algorithm method was used to obtain global optimum parameters (weights and biases) of the ANNs. The methane mole fraction, temperature, pressure, and density as effective parameters on thermal conductivity were network input variables. 171 and 180 data points related to the nitrogen-methane and carbon dioxide-methane gas mixtures, respectively, divided to test and train datasets. Simple correlations were obtained due to the small number of optimal neurons in the ANN structures. The mean relative errors of 0.206% and 0.199% for the testing dataset indicate the high accuracy and validation of the correlations. The work indicates that artificial intelligence approaches are very useful for thermal conductivity modeling in natural gases. A sensitivity analysis was performed on all input variables that indicates that the gas mixture density has the greatest impact on the thermal conductivity.

Abstract In this paper, we report for the first time, thermal conductivity behavior of nanofluids containing decorated MWCNTs with different amount of TiO2 nanoparticles. TEM image confirmed that the outer surface of MWCNTs successfully decorated with TiO2 nanoparticles. The results of thermal conductivity behavior of nanofluids revealed that the thermal conductivity and enhancement ratio of thermal conductivity of MWCNTsTiO2 at different amount of TiO2 nanoparticles are higher than those of TiO2 and MWCNTs nanofluids. Temperature and weight fraction dependence study also shows that the thermal conductivity of all nanofluids increases with temperature and weight fraction. However, the influence of temperature is more significant than that of weight fraction. We also found that decreasing amount ofTiO2 nanoparticles which introduce the outer surface of MWCNTs leads to the augmentation of thermal conductivity of nanofluids containing MWCNTs-TiO2.

Abstract In this study, functionalized multi-walled carbon nanotubes using mixed acid treatment were synthesized using solvothermal method by TiCl4 as a precursor and the thermal conductivity enhancement of MWNT-TiO2 nanofluids  in  various  temperatures  were compared. The treated nanotubes have been characterized using Fourier Transform
Infrared Spectroscopy (FTIR). Hybrid materials were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results showed that MWNTs are uniformly decorated with anatase nanocrystals. Temperature effects on thermal conductivity of MWNT-TiO2 nanofluids at different concentrations have been studied. The best result showed enhancement of thermal conductivity around 12.1% for the sample with 0.08 wt% of MWNT-TiO2 compared to distilled water at 36°C and 13.71% at 52°C. Also, zeta potential of 0.02 wt% nanofluids and particle size distribution
of nanoparticle were measured.

Abstract In this paper, the thermal properties including molar heat capacity, CP, thermal conductivity, λ, and thermal diffusivity, αD, of the pure physical solvents sulfolane (SFL), N,N-dimethylformamide (DMF), dimethylsulfoxide (DMSO), ethylene glycol
(ETG), choloroform (CCL3H), acetonitrile (CH3CN), and pure chemical solvents monoethanolamine (MEA), diethanolamine (DEA), triethanolamine (TEA), methyldiethanolamine (MDEA), 2-amino-2-methyl-1-propanol (AMP) which all are extensively used in natural gas refinery processes were measured at temperatures ranging from (303.15 to 353.15) K and atmospheric pressure. All experimental measurements were carried out by using a PSL Systemtechnik instrument in which
transient hot-wire method was employed to measure transport properties, λ and αD. All obtained data were correlated by using empirical linear temperature function with a very good correlation coefficient, better than R2 = 0.99. Among the solvents tested in this paper, except for TEA, the thermal diffusivity decreased by increasing temperature and also except for TEA and ETG, thermal conductivity decreased with temperature.