Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al<Subscript>2</Subscript>O<Subscript>3</Subscript> nanoparticles by correlation and ANN methods using experimental data

In the present paper, the effects of temperature and volume fraction on thermal conductivity of SWCNT–Al₂O₃/EG hybrid nanofluid are investigated. Single-walled carbon nanotube with outer diameter of 1–2 nm and aluminum oxide nanoparticles with mean diameter of 20 nm with the ratio of 30 and 70%, respectively, were dispersed in the base fluid. The measurements were conducted on samples with volume fractions of 0.04, 0.08, 0.15, 0.3, 0.5, 0.8, 1.5 and 2.5. In order to investigate the effects of temperature on thermal conductivity of the nanofluid, this characteristic was measured in five different temperatures of 30, 35, 40, 45 and 50 °C. The results indicate that enhancement of nanoparticles’ thickness in low volume fractions and at any temperature causes a considerable increment in thermal conductivity of the nanofluid. In this study, the highest enhancement of thermal conductivity was 41.2% which was achieved at the temperature of 50 °C and volume fraction of 2.5%. Based on the experimental data, an experimental correlation and a neural network are presented and for thermal conductivity of the nanofluid in terms of volume fraction and temperature. Comparing outputs of the experimental correlation and the designed artificial neural network with experimental data, the maximum error values for the experimental correlation and the artificial neural network were, respectively, 2.6 and 1.94% which indicate the excellent accuracy of both methods in prediction of thermal conductivity.     

Kernels of Gramian operators for frames in shift-invariant subspaces

For an invertible n×n matrix B and Φ a finite or countable subset of L²(Rⁿ), consider the collection X={?(·-Bk):?∈Φ,k∈Zⁿ} generating the closed subspace M of L²(Rⁿ). Our main objects of interest in this paper are the kernel of the associated Gramian G(.) and dual Gramian operator-valued functions. We show in particular that the orthogonal complement of M in L²(Rⁿ) can be generated by a Parseval frame obtained from a shift-invariant system having m generators where . Furthermore, this Parseval frame can be taken to be an orthonormal basis exactly when almost everywhere. Analogous results in terms of dim(Ker(G(.))) are also obtained concerning the existence of a collection of m sequences in the orthogonal complement of the range of analysis operator associated with the frame X whose shifts either form a Parseval frame or an orthonormal basis for that orthogonal complement.     

Synthesis of nanoscale zero‐valent iron modified graphene oxide nanosheets and its application for removing tetracycline antibiotic: Response surface methodology

Nowadays, pharmaceutical antibiotics are known as a serious class of pollutants. Therefore, it is important to develop effective methods for removing these pollutants from aqueous media. Different methods were applied for this purpose, and among these methods, chemical reduction by a cheap and eco‐friendly nanocatalyst is the most efficient and simplest method. In this research, based on graphene oxide supported by zero‐valent iron in mono‐, bi‐, and tri‐metallic systems, various nanocomposites were synthesized and used to degrade tetracycline as a model antibiotic from aqueous media. An investigation was carried out on the synergic effect among graphene oxide and the nano zero‐valent iron‐based tri‐metallic system as well as removal efficiencies. It was found that higher degradation efficiency is yielded by graphene oxide supported by Fe/Cu/Ag tri‐metallic system. The maximum synergic effect occurs at an acidic medium. The Brunauer–Emmett–Teller, Fourier transform spectroscopy, scanning electron microscopy‐energy dispersive X‐ray analysis, transmission electron microscopy, and X‐ray diffraction analysis were used to characterize the synthesized nanocomposites, which has successfully proved the loading of nanoscale Fe/Cu/Ag tri‐metallic on a graphene oxide support. The central composite design was used to model and optimize all involved variables affecting antibiotic removal efficiency. The consequences illustrated the optimum condition regarding the removal of 50 ppm of tetracycline, for the nanocomposites dose of 3.0 mg ml^?1, the contact time of 30 min, and pH of 2, was achieved using the simplex non‐linear optimization method. Moreover, antibiotic adsorption kinetic models were also investigated. Finally, the tetracycline removal from aqueous media at different concentrations, 25, 50, and 75 ppm, was successful by applying the proposed nanocomposite, and the results showed tetracycline removal efficiencies of above 70%.     

Synthesis and characterization of Co (III) salen complex immobilized on cobalt ferrite‐silica nanoparticle and their application in the synthesis of spirooxindoles

The preparation, characterization and catalytic application of Co (III) salen complex loaded on cobalt ferrite‐silica nanoparticle [CoFe₂O₄@SiO₂@ Co (III) salen complex] are described. Co (III) salen complex loaded on ferrite cobalt‐silica nanoparticles is characterized by transmission electron microscopy, scanning electron microscopy coupled with energy‐dispersive X‐ray, vibrating‐sample magnetometer and Fourier transform‐infrared analyses. The thermal stability of the material is also determined by thermal gravimetric analysis. An average crystallite size is determined from the full‐width at half‐maximum of the strongest reflection by using Scherrer's approximation by powder X‐ray diffractometry. The efficiency of CoFe₂O₄@SiO₂@Co (III) salen complex is investigated in the synthesis of spirooxindoles of malononitrile, various isatins with 1,3‐dicarbonyles. The nanocatalyst demonstrated excellent catalytic activity that gave the corresponding coupling products in good to excellent yields. Moreover, the recoverability and reusability of CoFe₂O₄@SiO₂@Co (III) salen complex is investigated where nanocatalyst could be recovered and reused at least five times without any appreciable decrease in activity and selectivity, which confirmed its high efficiency and high stability under the reaction conditions and during recycling stages.     

Prediction of rheological behavior of MWCNTs–SiO2/EG–water non-Newtonian hybrid nanofluid by designing new correlations and optimal artificial neural networks

In this paper, at the first, new correlations were proposed to predict the rheological behavior of MWCNTs–SiO₂/EG–water non-Newtonian hybrid nanofluid using different sets of experimental data for the viscosity, consistency and power law indices. Then, based on minimum prediction errors, two optimal artificial neural network models (ANNs) were considered to forecast the rheological behavior of the non-Newtonian hybrid nanofluid. One hundred and ninety-eight experimental data were employed for predicting viscosity (Model I). Two sets of forty-two experimental data also were considered to predict the consistency and power law indices (Model II). The data sets were divided to training and test sets which contained respectively 80 and 20% of data points. Comparisons between the correlations and ANN models showed that ANN models were much more accurate than proposed correlations. Moreover, it was found that the neural network is a powerful instrument in establishing the relationship between a large numbers of experimental data. Thus, this paper confirmed that the neural network is a reliable method for predicting the rheological behavior of non-Newtonian nanofluids in different models.

     

Thermal conductivity of a hybrid nanofluid

Journal of Thermal Analysis and Calorimetry - Hybrid nanofluid can be considered as a new generation of nanofluids. Despite the success of the researchers in the field of hybrid nanofluids, no...     

Rheological behavior of CuO/EG:W (20:80 v/v) nanofluid from a thermal perspective

Journal of Thermal Analysis and Calorimetry - This study investigates the viscosity of CuO/EG:W (20:80 v/v) nanofluid in the solid volume fraction from 0 to 1% and temperatures between 15 and...     

CoFe2O4@SiO2@ Co (III) salen complex nanoparticle as a green and efficient magnetic nanocatalyst for the oxidation of benzyl alcohols by molecular O2

In the presence of cobalt (III) salen complex, selective oxidation of alcohols to carbonyl compounds was studied by molecular oxygen using isobutyraldehyde as an oxygen acceptor. The effect of cobalt (III) salen complex in the oxidation reaction was studied, and the results showed that Co (III) salen complex is very active and selective in the oxidation of various alcohols. Also, the effect of important factors including catalyst amount, solvent and temperature was investigated on the reaction. Furthermore, the catalytic activities of CoFe₂O₄@SiO₂‐supported Schiff base metal complex as well as the effect of molecular oxygen (O₂) as a green oxidant were studied. The results showed that benzaldehyde was the major product and the heterogeneous catalyst was highly reusable.