Ion-exchange kinetics and thermal decomposition characteristics of Fe2+-exchanged alginic acid membrane for the formation of iron oxide nanoparticles

The ion-exchange kinetics of Fe²⁺ cations in aqueous solution with H⁺ from alginic acid have been analyzed in this study as a function of contact time lengths using inductively coupled plasma-atomic emission spectrometry analysis. The kinetic parameters have been evaluated using pseudo 1st or 2nd order models, and a consistent ion-exchange mechanism is suggested. Furthermore, an insight into the calcination of Fe²⁺ ion-exchanged alginic acid process has been obtained by using simultaneous thermo-gravimetric analysis coupled with differential scanning calorimetry and high temperature X-ray diffraction.     

Using Taguchi method to determine optimum process conditions for flue gas desulfurization through an amine scrubber

This study was done to determine the optimum process conditions for absorption of sulfur dioxide in a mixture of flue gases. Using a selective amine-based absorber; a high amount of SO₂ was absorbed in the scrubbing process. The process was designed to reduce the amount of sulfur dioxide. The pilot plant was designed and set up in the Research Center of Petroleum University of Technology, containing absorption and desorption stages. This paper reports an investigation of the effect and optimization of parameters that improve the efficiency of the whole process. The experiments were conducted under varying levels of desorption temperature, pH of the absorber solution, concentration of SO₂ inlet gas, and flow rate of the absorbing solution. Using Taguchi experimental design method, the optimum conditions were identified for the absorption process. An efficiency of more than 99 % could be obtained by varying the parameters in which all the released SO₂ gas was absorbed from the inlet flue gas; an achievement that is much favorable for industrial purposes.     

Hybrid Fractal-Wavelet Method for Multi-Channel EEG Signal Compression

In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analysis of different channels to efficiently compress the multi-channel EEG data. Then, the decorrelated EEG signal is decomposed using wavelet packet transform (WPT). Finally, fractal encoding is applied to the low frequency coefficients of WPT, and a modified wavelet-based coding is used for coding the remaining high frequency coefficients. This new method provides improved compression results as compared to the wavelet and fractal compression methods.     

A nondominated ranked genetic algorithm for bi-objective single machine preemptive scheduling in just-in-time environment

Motivated by just-in-time (JIT) manufacturing, we study the bi-objective scheduling problem of minimizing the total weighted earliness and the number of tardy jobs on a single machine, in which machine idle time and preemption are allowed. The problem is known to be NP-hard. In this paper, we propose a new mathematical model, with nonlinear terms and integer variables which cannot be solved efficiently for medium- and large-sized problems. A method combining the new ranked-based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named nondominated ranking genetic algorithm (NRGA), has been presented to find nondominated solutions in a reasonable time. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The solutions obtained via NRGA are compared against solutions obtained via ε-constraint method in small-sized problems. Experimental results show that the proposed NRGA is competitive in terms of the quality and diversity of solutions in medium- and large-sized problems.     

Wear assessment of plasma nitrided AISI H11 steel

Plasma nitriding is one of the effective methods for improvement of the hardness, wear and corrosion resistance of steels. In this research AISI H11 hot working tool steel was plasma nitrided in various gas mixtures for different times and temperatures. The morphology, size and composition of nitride nanoparticles formed on the surface of the specimens were investigated using scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray diffraction (XRD). The wear behavior of plasma nitrided samples was studied by means of unlubricated pin-on-disc method under constant load of 80 N, sliding speed of 1 m/s, sliding distance of 2000 m at room temperature. The results showed plasma nitriding process improved the wear behavior of H11 steel. The increase in time and temperature of plasma nitriding decreased the hardness and increased the wear weigh loss of the specimens.     

Measurement of Urbach edge and midgap states in amorphous silicon p-i-n devices

Urbach energy of valence band tails and mid-gap defect densities are important parameters for determining the performance of amorphous silicon (a-Si:H) devices. In this paper, we examine a technique which allows one to determine these parameters in device structures, as opposed to in films. The technique consists of measuring sub-gap quantum efficiency as a function of reverse bias and photon wavelength. We show that there is a distinction between the response of tail states and mid-gap states to the application of reverse bias, and that by analyzing these differences, one can locate the energies of the mid-gap states in the device. The technique gives an accurate measurement of Urbach edge of tail states, even on textured substrates, but only estimates mid-gap states within a factor of 2.     

Switched diversity approach for multireceiving optical wireless systems

A number of existing spatial diversity schemes have been shown to improve the performance of optical wireless communication systems in diversity-rich environments. Among all, switched diversity has low complexity and is simple to implement. In this paper, an innovative spatial diversity scheme based on switched diversity is proposed. The scheme, namely switch-to-dominant combining, contributes to a higher bit error rate (BER) performance when compared to conventional switched diversity schemes, including switch-and-stay and switch-and-examine diversity. The optical multireceiver wireless system operates in a spatially correlated and lognormally distributed fading channel. Analytical analyses are conducted to demonstrate BER and processing load performance offered by the new scheme and compare them to available schemes, i.e., conventional switched combining and selection combining.     

Mixed sulphide-oxide lead and zinc ores: Problems and solutions

Mixed sulphide-oxide lead and zinc ores are most often found in the transition, and occasionally in the oxidised, zones of lead-zinc ore-bodies. They are of great importance because there are numerous unexploited or abandoned reserves of these ores in the world.However they present difficulties for conventional mineral processing due to complex mineralogy. In this paper, the specific problems associated with these types of ores are described and methods for solving these problems, combining economic and technical considerations, are discussed.The results of experiments carried out at laboratory scale are presented, in which the dissolution of mixed ore in sulphuric acid without oxidising agents was investigated. The results show the feasibility of zinc recovery from mixed sulphide-oxide lead and zinc ores, which underlines the potential of this approach. We also propose a conceptual flow diagram for the hydrometallurgical processing of these ores.     

Preparation,characterization and photocatalytic activity of TiO<Subscript>2</Subscript> / Methylcellulose nanocomposite films derived from nanopowder TiO<Subscript>2</Subscript> and modified sol–gel titania

TiO₂—methylcellulose (MC) nanocomposite films processed by the sol-gel technique were studied for phocatalytic applications. Precalcined TiO₂ nanopowder was mixed with a sol and heat treated. The sol suspension was prepared by first adding titanium tetra isopropoxide (Ti(OPr)₄ or TTP) to a mixture of ethanol and HCl (molar ratio TTP:HCl:EtOH:H₂O = 1:1.1:10:10) and then adding a 2 wt.% solution of methylcellulose (MC). The TiO₂ nanopowder was dispersed in the sol and the mixture was deposited on a microscope glass slide by spin coating. Problems of film inhomogeneity and defects which caused peeling and cracking during calcinations, because of film shrinkage, were overcome by using MC as a dispersant. Effect of MC on the structure evaluation, crystallization behavior and mechanical integrity with thermal treatment up to 500 °C are followed by SEM, XRD and scratch test. XRD Scanning electron microscopy (SEM) showed that the composite films with MC have much rougher surface than films made without MC. Composite films heat treated at approximately 500 °C have the greatest hardness values. For the composite thick film, the minimum load which caused the complete coating removal was 200 g/mm², an indication of a strong bond to the substrate. Photocatalytic activities of the composite film were evaluated through the degradation of a model pollutant, the textile dye, Light Yellow X6G (C.I. Reactive Yellow 2) and were compared with the activity of (i) a similar composite film without MC, and (ii) a TiO₂ nanopowder. The good mechanical integrity make this composite film an interesting candidate for practical catalytic applications.     

A density-based fuzzy clustering technique for non-destructive detection of defects in materials

In non-destructive testing and evaluation of materials, defects contain visible aggregations of similar levels of brightness with large scale of correlation between them. In most cases, these brightnesses have no notable contrast relative to non-defect counterparts. However, the density and the size of the defect are visually the most notable features. In this paper, we have utilized human conception for classifying defects by the fusion of fuzzy clustering method and fuzzy logic rules based on the density and the size of the defect. The probability of detection and the probability of error are compared with the Bayes classifier. The proposed approach shows that there is less dependency between the variation of density and size of a defect and variations of noise density and distribution. Experimental images from eddy current, ultrasonic and radiography techniques are investigated. It is shown that the new approach reduces the noise and drift, leading to a better detection of defects.