Soret and Dufour effects between two rectangular plane walls with heat source/sink

This study addresses the thermo‐diffusion and the diffusion‐thermo phenomena in a semi‐infinite absorbent channel whose walls are contracting/expanding, with heat source/sink effects. The governing partial differential equations with suitable boundary conditions are transformed to a system of dimensionless ordinary differential equations. An analytic solution of the problem has been found using a technique called homotopy analysis method (HAM). HAM gives consistently valid answers to the problem over an extensive variety of parameters and also provides better accuracy. To validate the analytical results, a comparison has been presented with a numerical solution calculated by using the parallel shooting method. The effects of dimensionless parameters, that is, deformation parameter, Reynolds number, Soret and Dufour numbers, and heat source/sink parameter on the expressions of velocity, temperature, and concentration profiles are analyzed graphically to understand the physics of the deformable channel. It has been noted that the velocity across the channel is higher for the expanding channel, as compared to that for the contracting channel. Also the Soret and Dufour number increases the temperature of the fluid, and decreases the concentration. The temperature profile has an increasing behavior in the case of heat source, and a decreasing behavior in the case of heat sink.     

Effect of processing method on vitamin profile,antioxidant properties and total phenolic content of coconut (Cocos nucifera L.) sugar syrup

Coconut (Cocos nucifera L.) sugar is a more nutritious alternative sugar source as compared to sugar palm (Borassus flabellifer) and sugarcane (Saccharum officinarum L.). This work was aimed to investigate the browning index (BI), vitamin profile and antioxidant properties of coconut sap sugar syrups, which were produced by different processing methods: rotary evaporation (RE), microwave evaporation (ME) and open-heat evaporation (OHE). The results obtained showed that coconut sugar syrup produced by RE-60 contained high antioxidant activities [DPPH (36.71%) and ABTS (34.84%), TPC (299.87 mg per 100 g sample) and FRAP (3.74 mm )]. These values were slightly lower than those of ME and OHE. Coconut sugar syrup (RE-60) also contained higher amounts of vitamin C (1587.27 mg L⁻¹), vitamin B1 (97.44 mg L⁻¹) and vitamin B3 (19.84 mg L⁻¹) compared with those of ME and OHE coconut sugar syrups. RE-60 was the best method to produce coconut sugar syrup in a shorter time with lower browning index and higher vitamin contents.     

Mass‐transfer enhancement in single drop extraction in the presence of magnetic nanoparticles and magnetic field

Magnetite nanoparticles with an average particle size of 28.8 nm were synthesized, coated with oleic acid, and characterized using various techniques such as DLS, FT‐IR, SEM, XRD, VSM, and UV‐Vis analysis. A nanofluid consisting of synthesized nanoparticles and 5 wt % acetic acid in toluene as the dispersed phase was prepared and used in the chemical test system, Toluene‐Acetic Acid‐Water, for the single drop extraction in the presence and absence of an external oscillating magnetic field. Influences of various operating and design parameters such as nanoparticle concentration, drop diameter, and the applied current and frequency on the overall mass‐transfer coefficients for the mass‐transfer direction from d→c were investigated carefully. The obtained results were used to propose a general correlation for the mass‐transfer enhancement. It was found that the maximum mass‐transfer enhancement compared with that obtained in the absence of nanoparticles and the oscillating magnetic field is about 259%. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4466–4479, 2016     

Development of High Strength and Toughness Non-Heated Al–Mg–Si Alloys for High-Pressure Die-Casting

Based on the 3 factors and 3 levels orthogonal experiment method, compositional effects of Mg, Si, and Ti addition on the microstructures, tensile properties, and fracture behaviors of the high-pressure die-casting Al-x Mg-y Si-z Ti alloys have been investigated. The analysis of variance shows that both Mg and Si apparently infl uence the tensile properties of the alloys, while Ti does not. The tensile mechanical properties are comprehensively infl uenced by the amount of eutectic phase(α-Al + Mg_2Si), the average grain size, and the content of Mg dissolved into α-Al matrix. The optimized alloy is Al-7.49 Mg-3.08 Si-0.01 Ti(wt%), which exhibits tensile yield strength of 219 MPa, ultimate tensile strength of 401 MPa, and elongation of 10.5%. Furthermore, contour maps, showing the relationship among compositions, microstructure characteristics, and the tensile properties are constructed, which provide guidelines for developing high strength and toughness Al–Mg–Si–Ti alloys for high-pressure die-casting.     

The effect of pH,solid content,water chemistry and ore mineralogy on the galvanic interactions between chalcopyrite and pyrite and steel balls

The role of pH, solid content, water chemistry and ore mineralogy on the galvanic interactions between chalcopyrite and pyrite and low alloy steel balls were investigated in the grinding of Sarcheshmeh porphyry copper sulfide ore. All these factors strongly affect the galvanic current between the minerals and the steel during the grinding process. The galvanic current density decreased as the solution pH and percent solids increased. In addition, changing the water in the ball mill from tap to distilled water reduced the galvanic current between the minerals and the balls. Potentiodynamic polarization curves showed that pyrite and chalcopyrite demonstrated typical active-passive-transpassive anodic behavior in the grinding of copper ore. However, the nature of their transitions from the active to the passive state differed. This behavior was not seen in the grinding of pure minerals. In addition, an EDTA extraction technique was employed to quantify the amount of oxidized iron in the mill discharge. The amount of extractable iron was influenced by the same experimental factors and in the same way as the galvanic current.     

SYNERGISTIC EFFECTS OF ANIONIC SURFACTANT AND NONIONIC POLYMER ADDITIVES ON DRAG REDUCTION

Turbulent drag reduction (DR) behavior of mixed nonionic polymer and anionic surfactant solutions in water was studied in a pipeline set up to explore the synergic effects of mixed additives on DR. The concentration of polymer polyethylene oxide (PEO) was varied from 0 to 2000 ppm and the concentration of surfactant sodium dodecyl sulfate (SDS) was varied from 0 to 5000 ppm. The critical aggregation concentration (CAC), where the interaction between the polymer and the surfactant begins, and the polymer saturation point (PSP), where the polymer molecules become saturated with the surfactant, were determined using electrical conductivity and surface tension measurements. As the polymer concentration was increased the CAC decreased but the PSP increased. The relative viscosity showed a remarkable increase upon the addition of surfactant to the polymer solution due to extension of polymer chains caused by the formation of micelles on the backbone of the polymer molecules. The data exhibited a considerable increase in DR in the case of mixed polymer/surfactant systems. The percent reduction in friction factor was as high as 79 when 3000 ppm or more surfactant was added to the 500 ppm polymer solution. Furthermore, the drag reduction behavior of the polymer solution changed from so-called Type A to Type B. In Type A drag reduction, a transition from laminar to turbulent regime is observed with a clear-cut onset point. In Type B drag reduction, no transition or onset point is observed; the data fall on a gradual extension of the laminar line.     

CFD and artificial neural network modeling of two-phase flow pressure drop

A large number of experiments in a 2 cm diameter and 6 m length tube were carried out in order to study the two-phase flow regimes and pressure drops in it. The two-phase flow in the experimental tube was modeled using commercial CFD code, Fluent 6.2. An Artificial Neural Network (ANN) with three inputs including gas and liquid velocities and tube slope was designed and trained to predict average pressure drop across the tube. The comparison between CFD and ANN predictions of pressure drops with experimental measurements shows that the CFD results are more accurate than the ANN evaluations for new conditions.     

Variation in structural and dielectric properties of co-precipitated nanoparticles strontium ferrites due to value of pH

Nanoparticles of strontium ferrites with nominal composition SrFe₁₂O₁₉ were prepared by co-precipitation method, by decreasing pH from 13 to 8 with a regular step of 1. The secondary phase of α-Fe₂O₃ was increased with the decrease in pH. The crystallite size estimated from X-rays diffraction data was in the range 52-70 nm, which is much smaller than that already reported. Most of the particles formed had hexagonal structure, as observed by the scanning electron microscopy. Particle size and dielectric loss were increased where as dc electrical resistivity and dielectric constant were decreased with decrease in pH. The results show that the material synthesized with higher pH is phase pure and is potentially more suitable for high frequency applications.     

A genetic programming model for bankruptcy prediction: Empirical evidence from Iran

Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms’ impending failure is indeed desirable. By this time, several methods have been used for predicting bankruptcy but some of them suffer from underlying shortcomings. In recent years, Genetic Programming (GP) has reached great attention in academic and empirical fields for efficient solving high complex problems. GP is a technique for programming computers by means of natural selection. It is a variant of the genetic algorithm, which is based on the concept of adaptive survival in natural organisms. In this study, we investigated application of GP for bankruptcy prediction modeling. GP was applied to classify 144 bankrupt and non-bankrupt Iranian firms listed in Tehran stock exchange (TSE). Then a multiple discriminant analysis (MDA) was used to benchmarking GP model. Genetic model achieved 94% and 90% accuracy rates in training and holdout samples, respectively; while MDA model achieved only 77% and 73% accuracy rates in training and holdout samples, respectively. McNemar test showed that GP approach outperforms MDA to the problem of corporate bankruptcy prediction.