LIQUID-LIQUID MIXING IN STIRRED VESSELS: A REVIEW

Liquid-liquid mixing is a key process in industries that is commonly accomplished in mechanical agitation systems. Liquid-liquid mixing performance in a stirred tank can be evaluated by various parameters, namely minimum agitation speed, mixing time, circulation time, power consumption, drop size distribution, breakup and coalescence, interfacial area, and phase inversion. The importance of these liquid-liquid mixing parameters, the measurement method, and the results are discussed briefly. Input parameters such as impeller type, power number, flow pattern, number of impellers, and dispersed phase volume fraction, in addition to physical properties of phases such as viscosity and density, are reviewed. Scale-up aspects are also included.     

Solid-phase electrochemical reduction of graphene oxide films in alkaline solution

Graphene oxide (GO) film was evaporated onto graphite and used as an electrode to produce electrochemically reduced graphene oxide (ERGO) films by electrochemical reduction in 6 M KOH solution through voltammetric cycling. Fourier transformed infrared and Raman spectroscopy confirmed the presence of ERGO. Electrochemical impedance spectroscopy characterization of ERGO and GO films in ferrocyanide/ferricyanide redox couple with 0.1 M KCl supporting electrolyte gave results that are in accordance with previous reports. Based on the EIS results, ERGO shows higher capacitance and lower charge transfer resistance compared to GO.     

Effect of hexagonal structure nanoparticles on the morphological performance of the ceramic scaffold using analytical oscillation response

Porous bony scaffolds are utilized to manage the growth and migration of cells from adjacent tissues to a defective position. In the current investigation, the effect of titanium oxide (TiO₂) nanoparticles on mechanical and physical properties of porous bony implants made of polymeric polycaprolactone (PCL) is studied. The bio-nanocomposite scaffolds are prepared with composition of nanocrystalline hydroxyapatite (HA) and TiO₂ powder using the freeze-drying technique for different weight fractions of TiO₂ (0 wt%, 5 wt%, 10 wt%, and 15 wt%). In order to identify the microstructure and morphology of the fabricated porous bio-nanocomposites, the X-ray diffraction (XRD), atomic force microscope (AFM) and scanning electron microscopy (SEM) are employed. Also, the biocompatibility and biodegradability of the manufactured scaffolds are examined by placing them in a simulated body fluid (SBF) for 21 days, their weight and pH changes are measured. The rate of degradation of the PCL-HA scaffold can be controlled by varying the percentage of its constituent components. Due to an increasing growth and activity of bone cells and the apatite formation on the free surface of the fabricated bio-nanocomposite implants as well as their reasonable mechanical properties, they have the potential to be used as a bone substitute. Additionally, with the aid of the experimentally extracted mechanical properties of the scaffolds, the vibrational characteristics of a beam-type implant made of the proposed porous bio-nanocomposites are explored. The results obtained from SEM image indicate that the scaffolds produced by the employed method have high total porosity (70%–85%) and effective porosity. The pore size is obtained between 60 and 200 μm, which is desirable for the growth and propagation of bone cells. Also, it is revealed that the addition of TiO₂ nanoparticles leads to reduce the rate of dissolution of the fabricated bio-nanocomposite scaffolds.     

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations.     

Microstructure and Mechanical Properties of a Dissimilar Friction Stir Weld between Austenitic Stainless Steel and Low Carbon Steel

Dissimilar fusion welding of austenitic stainless steels to carbon steels has some metallurgical and technical problems.It was suggested that the solid-state nature of friction stir welding(FSW) can overcome these problems and produce a sound weld with reliable mechanical properties.In this study,plates of 304 stainless steel and st37 steel were welded together by FSW at tool rotational speed of 600 r/min and welding speed of 50 mm/min.In the stir zone(SZ) of 304 stainless steel,the results showed a refined grain structure with some features of metadynamic recrystallization.In the SZ of st37 steel,the hot deformation of material in the austenite region produced small austenite grains.These grains transformed to fine ferrite and pearlite by cooling the material after FSW.The production of fine grains increased the hardness and tensile strength in the SZ of both sides with respect to their base metals(BMs).     

Application of discrete event simulation for robust system design of a melt facility

This paper presents a methodology to identify robust operating regions through the selection of controllable factory variables, using discrete event simulation. A casting plant melt facility was used as an industrial test bed to develop these techniques. A robust system design was determined by response surface analysis of key production parameters. Furthermore, robust operating policies that maximise throughput, while minimizing work-in-progress and thus energy consumption were identified.     

Estimation of Thermal Stresses in Hot-Rolled Steel Coils During Natural Air Cooling

Hot-rolled steel coils are stored upon manufacturing to cool to about 50°C. High temperatures of coiling cause thermal stresses during the cooling stage that result in spot welding between strip layers of coil. Under these conditions, surface defects or strip ruptures may occur during uncoiling. This paper will initially introduce the heat transfer model in the coil based on stress-dependent thermal conductivity in the radial direction of the coil. The two approaches of “solid body” and “thin-walled, concentric cylinders” for thermal stress analysis will then be used to compute the thermal compressive stresses in the coil. Comparison of the results obtained from these two approaches in the mathematical model reveals that the solid body approach overestimates the thermal stresses when compared to the thin-walled concentric cylinders approach, the inequality rising with reduced strip thickness. This difference, however, is found to have no considerable impact on predicting coil temperature.     

On the efficiency of bootstrap method into the analysis contingency table

The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorical data analysis, in particular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient.     

Classification of Geothermal Energy Resources in Japan Applying Exergy Concept

Higher demand for energy consumption and importance of environmental issues has encouraged researchers and policy makers to consider renewable energies more seriously. Geothermal resources are a green energy source that can make a considerable contribution in some countries. Japan has the third ranking geothermal energy potential, and its geothermal electricity production is currently eighth in the world. Since the nature of geothermal resources dictates its method of utilization, it is important to categorize available resources. There is no consensus on classification of geothermal resources. Most scientists, from geologist to engineers, agree on the term temperature. However, temperature or enthalpy alone cannot describe the nature of fluids; they can have same temperature with different phases, such as saturated water or saturated steam. Using exergy for resource classification benefits their comparison, according to their ability to do work. In this paper, exergetic classification of geothermal resources was applied to 18 under‐operating geothermal power plants in Japan. Six geothermal fields have high exergy resources according to their SExI values in excess of 0.5. The remaining geothermal fields in Japan are classified in the medium resources zone. Classification results can be used by decision makers as a reference for future geothermal development. Copyright © 2012 John Wiley & Sons, Ltd.     

Monitoring the aging of beers using a bioelectronic tongue

This paper deals with the implementation and the application of a bioelectronic tongue including three enzymatic biosensors based on tyrosinase and phthalocyanines as electron mediators, to evaluate the changes that occur during the aging of beers. For this purpose, alcoholic and non alcoholic beers, packaged in can and bottle, have been analyzed using cyclic voltammetry. The electrochemical signals showed significant changes during the aging process. The features extracted from the cyclic voltammograms have been used to perform Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Data have revealed a clear discrimination among the beer classes in the aging process and the results were confirmed by Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Backpropagation (BP) learning method. The bioelectronic tongue has demonstrated a good capability to discriminate and classify the beer types satisfactorily in such a way, for all beer treatments, full classification accuracy was found.