Effect of microwave sintering on the microstructure and electrical properties of low-voltage ZnO varistors

Coarse-grained ZnO varistors for low-voltage applications were prepared by microwave sintering technique under different soaking times of 5–150?min. For comparison, a low-voltage ZnO varistor was also prepared through a conventional sintering process. Microwave sintering remarkably enhanced the grain growth rate of ZnO varistors. Average grain size of the sample prepared by microwave sintering in 15?min was about 20?µm, which is similar to the grain size of sample prepared conventionally in 150?min time. In addition to grain growth, an increase in microwave sintering time led to precipitation of zinc titanate (Zn₂TiO₄) on the top surface of samples which sintered for long dwell times. X-ray diffraction and scanning electron microscopy results from different points of the samples declared that precipitation of Zn₂TiO₄ phase is due to the high rate of bismuth evaporation of Bi-rich liquid from top surface and the reaction between remaining titanium ions on the surface with ZnO. The results showed that increasing sintering time from 5 to 150?min increased the grain size from 14 to 33?µm, consequently, the breakdown field decreased from 90 to 27?V/mm, respectively. These changes led to a switch in the varistor application, from low to very low voltage.     

Application of artificial neural networks for prediction of coercivity of highly ordered cobalt nanowires synthesized by pulse electrodeposition

This study aims to predict the coercivity of cobalt nanowires fabricated by Alternating Current (AC) pulse. Coercivity is one of the most important properties of magnetic materials and its value shows the needed magnetic field in a way that magnetization of system is decreased to zero. There are many parameters such as pH of solution, oxidative and reductive times, oxidative and reductive voltages, interval between pulses (off-time), and concentration of deposition solution that have direct effect on materials magnetic properties of. Change of initial conditions to obtain the best results is very time consuming, therefore employing a method which can save both the time and cost is necessary. Hence, it this study Artificial Neural Network (ANN), which has numerous applications and has attracted many attentions in various fields, was applied. Through this study, an ANN was designed to present a template that is capable for predicting output data (coercivity) according to input data (pH, oxidative and reductive times, oxidative and reductive voltages, and off-time). Besides, in this research, the results for pH = 4 and 6 were investigated and the effect of off-time as well as the deposition time on coercivity were studied.     

Sulfidation and regeneration of iron-based sorbents supported on activated-chars prepared by pressurized impregnation for coke oven gas desulfurization

The sulfidation and regeneration properties of lignite char-supported iron-based sorbent for coke oven gas (COG) desulfurization prepared by mechanical stirring (MS), ultrasonic assisted impregnation (UAI), and high pressure impregnation (HPI) were investigated in a fixed-bed reactor. During desulfurization, the effects of process parameters on sulfidation properties were studied systematically. The physical and chemical properties of the sorbents were analyzed by X-ray diffraction (XRD), scanning electron microscope coupled with energy dispersive spectroscopy (SEM-EDS), Fourier transform infrared (FTIR) and BET surface area analysis. The results of desulfurization experiments showed that high pressure impregnation (HPI) enhanced the sulfidation properties of the sorbents at the breakthrough time for char-supported iron sorbents. HPI method also increased the surface area and pore volume of sorbents. Sulfur capacity of sorbents was enhanced with increasing sulfidation temperatures and reached its maximum value at 400 °C. It was observed that the presence of steam in coke oven gas can inhibit the desulfurization performance of sorbent. SO₂ regeneration of sorbent resulted in formation of elemental sulfur. HPIF10 sorbent showed good stability during sulfide-regeneration cycles without changing its performance significantly.     

Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user's experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (P_b) and the oil formation volume factor at bubble point (B_ob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions.     

Nonlinear electrostatic behavior for two elastic parallel fixed–fixed and cantilever microbeams

In this paper, the electrostatic pull-in behavior of two elastic parallel fixed–fixed and cantilever microbeams in microelectromechanical systems (MEMS) are investigated. The nonlinear electrostatic equations are considered due to some important effects including: residual stresses, fringing field and axial stresses. Various residual stresses in two elastic parallel fixed–fixed models are considered. Step by step linearization method is used to solve the equations. The numerical results reveal that the step by step linearization method is highly efficient, and it is the easiest one to calculate the pull-in voltage. In the proposed models, the pull-in voltages are considerably decreased when compared to the pull-in voltages of simple fixed–fixed and cantilever models.     

Biochemical and molecular characterization of novel PAH-degrading bacteria isolated from polluted soil and sludge

Polycyclic aromatic hydrocarbons (PAHs) containing more than three rings are mainly less biodegradable. Therefore, the isolation of PAHs-degrading bacteria is of great importance to be augmented for bioremediation of polluted sites with PAHs. PAHs-degrading bacteria were isolated from contaminated sites of an oil refinery. The strains were confirmed by biochemical tests and 16S rRNA, which were Stenotrophomonas maltophilia (two strains), Thermomonas koreensis (three strains), Achromobacter (two strains), Pseudomonas stutzeri (one strain), Azospirillum brasilense (one strain) and Brevibacillus brevis (one strain). The isolate strains can be applied as a bacterial consortium for purification of polluted soil with high levels of PAHs.