Effect of molybdenum tri-oxide molar ratio on the optical and some physical properties of tellurite-vanadate-molybdate glasses

Glasses with composition (60 − x)V₂O₅-40TeO₂ − x MoO₃ with 20 ? x ? 60 (in mol%) have been prepared using the usual melt quenching method. The optical absorption spectra of the glasses have been recorded in the wavelength range 300-800 nm. The position of the absorption edge and therefore the optical band gap values were found to be depend on the glass composition. For these glasses, the optical band gap was found to be in the range 2.03-2.86 eV with increasing of MoO₃ concentration. The absorption spectrum fitting method was employed to obtain the energy gap. In this method, only the measurement of the absorbance spectrum of the glass is needed. For each sample, the width of the band tail was determined. Also, the density and glass transition temperature values indicate that the rigidity and packing of the samples increase with increase in MoO₃ concentration as a network former.     

Preparation of Silica-Based Ionic Liquid an Efficient and Recyclable Catalyst for One-Pot Synthesis of α-Aminonitriles

Abstract  

Preparation of N-(3-silicapropyl) imidazolium hydrogen sulfate ([Sipim]HSO₄) as a heterogeneous acidic ionic liquid is described. This heterogeneous ionic liquid was used as catalyst for the synthesis of α-aminonitriles by a one-pot condensation of aldehydes, amines, and trimethylsilyl cyanide at room temperature. Catalyst could be recycled for several times without any additional treatment.     

Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using Artificial Neural Network

Drying characteristics of shelled corn (Zea mays L) with an initial moisture content of 26% dry basis (db) was studied in a fluidized bed dryer assisted by microwave heating. Four air temperatures (30, 40, 50 and 60 °C) and five microwave powers (180, 360, 540, 720 and 900 W) were studied. Several experiments were conducted to obtain data for sample moisture content versus drying time. The results showed that increasing the drying air temperature resulted in up to 5% decrease in drying time while in the microwave-assisted fluidized bed system, the drying time decreased dramatically up to 50% at a given and corresponding drying air temperature at each microwave energy level. As a result, addition of microwave energy to the fluidized bed drying is recommended to enhance the drying rate of shelled corn. Furthermore, in the present study, the application of Artificial Neural Network (ANN) for predicting the drying time (output parameter for ANN modeling) was investigated. Microwave power, drying air temperature and grain moisture content were considered as input parameters for the model. An ANN model with 170 neurons was selected for studying the influence of transfer functions and training algorithms. The results revealed that a network with the Tansig (hyperbolic tangent sigmoid) transfer function and trainrp (Resilient back propagation) back propagation algorithm made the most accurate predictions for the shelled corn drying system. The effects of uncertainties in output experimental data and ANN prediction values on root mean square error (RMSE) were studied by introducing small random errors within a range of ±5%.     

Polymeric mixed matrix membranes containing zeolites as a filler for gas separation applications: A review

Polymeric membrane technology has received extensive attention in the field of gas separation, recently. However, the tradeoff between permeability and selectivity is one of the biggest problems faced by pure polymer membranes, which greatly limits their further application in the chemical and petrochemical industries. To enhance gas separation performances, recent works have focused on improving polymeric membranes selectivity and permeability by fabricating mixed matrix membranes (MMMs). Inorganic zeolite materials distributed in the organic polymer matrix enhance the separation performance of the membranes well beyond the intrinsic properties of the polymer matrix. This concept combines the advantages of both components: high selectivity of zeolite molecular sieve, and mechanical integrity as well as economical processability of the polymeric materials. In this paper gas permeation mechanism through polymeric and zeolitic membranes, material selection for MMMs and their interaction with each other were reviewed. Also, interfacial morphology between zeolite and polymer in MMMs and modification methods of this interfacial region were discussed. In addition, the effect of different parameters such as zeolite loading, zeolite pore size, zeolite particle size, etc. on gas permeation tests through MMMs was critically reviewed.     

Study of droplet behaviour along a pulsed liquid–liquid extraction column in the presence of nanoparticles

In this article, droplet size and its distribution along a pulsed liquid–liquid extraction column, is studied where SiO₂ nanoparticles with concentrations of 0.01, 0.05 and 0.1 vol.% and different hydrophobicities are applied to the dispersed phase. Using ultrasonication, nanoparticles were dispersed in kerosene as the base fluid. Nanofluids' stability was ensured using a UV–vis spectrophotometer. Some 22,000 droplets were measured by photographic technique and results were compared with systems containing no‐nanoparticles (Water–Acetic acid–Kerosene). Addition of nanoparticles changed the droplet shape from ellipsoidal to spherical. Also, there was a marked influence on droplet breakage and droplet coalescence at 0.01 vol.%, and 0.05 vol.% or higher volume fractions, respectively. © 2012 Canadian Society for Chemical Engineering     

Energy and Quality Attributes of Combined Hot-Air/Infrared Drying of Paddy

The kinetics of combined hot-air/infrared thin-layer drying of paddy was studied. The mechanical quality aspects of paddy kernels dried at different drying conditions were evaluated in terms of percentage of cracked kernels and also required failure force obtained from bending tests. The well-known Artificial Neural Network (ANN) modeling technique was applied to predict the drying time, variations in paddy moisture content, the percentages of cracked kernels, and the values of required failure force of paddy at different drying conditions. The best ANN topologies, transfer functions, and training algorithms were determined for prediction of the mentioned parameters. In addition to the product quality aspects, the specific energy consumption (SEC) was estimated for all drying conditions. The results indicated that application of a low-intensity IR radiation (2000 W/m²), together with lower values of inlet air temperature (30°C) and moderate values of inlet air velocity (0.15 m/s), can effectively improve the final quality of paddy (as a heat-sensitive product) with a reasonable SEC.     

Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran

One of the most important steps in evaluation and development of hydrocarbon reservoirs is the mapping of their characteristics. Nowadays, Seismic Attribute Technique is used to build parameters of hydrocarbon reservoirs in inter-well spaces. One of these parameters is the Flow Zone Index (FZI) that has a significant effect on different stages of evaluation, completion, primary and secondary production, reservoir modeling and reservoir management. The aim of this study is to introduce an equation using seismic attribute and FZI log in wells and then generalize it to predict FZI throughout the reservoir. For this purpose, acoustic impedance (AI) volume as an external attribute was created while internal attributes were computed from seismic data. After that, The best set of attributes was determined using stepwise regression after which seismic attributes were applied to multi-attribute analysis to predict FZI. Then, the attribute map resulted from multi-attribute analysis was used to interpret the spatial distribution of the gas bearing carbonate layers. Finally, the optimum number of Hydraulic Flow Units (HFU) was determined by analyzing the break point in the plot of cumulative frequency of FZI for wells and was generalized all over the reservoir by using the 3D HFU model. The results demonstrated that multi-attribute analysis was a striking technique for HFU estimation in hydrocarbon reservoirs that reduces cost and increases rate of success in hydrocarbon exploration. Distribution of producible hydrocarbon zones along with the seismic lines around the reservoir was characterized by studying this model which can help us in choosing the location of new wells and more economical drilling operations.     

Jitter and metastability analysis and modeling of multilevel bang-bang phase detector

Bang-bang phase detector (BBPD) is one of the essential blocks in the phase-locked loop and clock and data recovery that are used in transceivers. But BBPD has the metastability problem as data change in timing window. It suffers from not only metastability failure but also quantization noise, which causes output jitter. In this paper, the novel model is presented to evaluate the effect of both metastability and jitter on ML-BBPD, and also, it is shown that multilevel BBPD (ML-BBPD) has the improved quantization noise in comparison with the Alexander BBPD. In this model, it is shown that by increasing the oversampling ratio, the quantization noise is decreased, and with the metastability effect and the increment of quantization steps, the characteristic curve of the ML-BBPD becomes more smoothed. Also, the output jitter of ML-BBPD, in which metastability failure is diminished, is modeled. The error function in the model is simulated at system level and compares with the results achieved from simulation at circuit level to prove the validity of the proposed model. The simulation is done in TSMC 65-nm CMOS technology under 1-V supply voltage to compare the characteristic of ML-BBPD for various number of sampling clocks.     

Effect of the pigment–polymer interfacial interactions on the mechanical performance of automotive topcoat layers

The study of the effect of interfacial interactions on the mechanical performance of a selected automotive topcoat system has been undertaken. The investigation concerned interactions that arise in acrylic‐melamine/pigment assemblies. The pigments were designed to have different surface treatments. Three types of TiO₂ pigment, one type of C. I. Pigment Green 7, and a chrome oxide pigment were used in this research. Information regarding the surface characteristics of the pigments and also of the polymer was obtained by Inverse Gas Chromatography (IGC) technique. The pigments, at different particle volume concentrations, were dispersed into the polymeric matrix with the aid of their related additives. The cured coating samples were subjected to tensile stresses. The maximum stress that each sample withstood before breakage was recorded. Two different types of behavior were observed for coating composites that contained one of the pigment forms, at different particle volume concentrations. Finally, the data obtained from the IGC studies were used to support the results obtained from tensile testing. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2261–2268, 2006     

An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach

Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis–artificial neural network (DEA–ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA–ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA–AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers’ efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.