Direct extraction of Mo(VI) from sulfate solution by synergistic extractants in the rotation column

Direct extraction of molybdenum from sulfate solution with synergistic extractants (mixture of D₂EHPA and TBP) was studied in the rotation column. The influence of extractant concentration and initial pH of aqueous phase was studied in the bench scale experiments. The outcomes demonstrated that the synergistic solvent extraction enhances the constancy of the extracted complexes for transfer into the organic phase. In the continuous experiments, the effect of different operating parameters such as speed of agitation, inlet solvent flow rate and inlet aqueous flow rate on the holdup, mean drop size, drop size distribution, slip and characteristic velocities and extraction percentage were examined. Modified correlations were proposed for prediction of hydrodynamic parameters with consideration of reaction extraction condition in the rotation column. Furthermore, these correlations were compared with the experimental data. According to the results, the direct extraction of Mo (VI) from aqueous solution and sulfuric media with extraction efficiency of 90.4% was obtained at higher rotor speed (240 r·min^-1 rpm) in this column.     

The DC characteristics of GaAs/AlGaAs heterojunction bipolartransistors with application to device modeling

A complete DC model for the heterojunction bipolar transistor (HBT) is presented. The DC characteristics of the HBT are compared with the Ebers-Moll (EM) model used by conventional bipolar junction transistors (BJTs) and implemented in simulation and modeling programs. It is shown that although the details of HBT operation can differ markedly from those of a BJT, a model and a parameter extraction technique can be developed which have physical meaning and are exactly compatible with the EM models widely used for BJTs. Device I- V measurements at 77 and 300 K are used to analyze the HBT physical device performance in the context of an EM model. A technique is developed to extract the device base, emitter, and collector series resistances directly from the measured I-V data without requiring an ideal exp(qV_be/kT) base current as reference. Accuracies of the extracted series resistances are assessed. AC parameters of HBT are calculated numerically from the physical device structure. For modeling purposes, these parameters are shown to be comparable with those of conventional BJTs     

Recent Developments for Prediction of Power of Aromatic and Non‐Aromatic Energetic Materials along with a Novel Computer Code for Prediction of Their Power

The explosive power or strength of an energetic material shows its capacity for doing useful work. This work reviews recent developments for prediction of power of energetic compounds. A new user‐friendly computer code is also introduced to predict the relative power of a desired energetic compound as compared to 2,4,6‐trinitrotoluene (TNT). It is based on the best available methods, which can be used for different types of energetic compounds including nitroaromatics, nitroaliphatics, nitramines, and nitrate esters. The computed relative powers are consistent with the measured data for some new materials containing complex molecular structures.     

Interference and Guiding Effects in the Heating of Ceramic Slabs and Joints with Millimeter-Wave Radiation

This paper presents an analysis of some of the electromagnetic wave effects that occur when millimeter-wave beams are used for processing ceramic slabs and joints. These include interference effects on the couplings of the beam into a dielectric slab, guiding of the millimeter-wave beam through a ceramic joint, and standing-wave effects on the uniformity of energy deposition within the slab or joint. It also calculates the extent of the mitigation of the nonuniform energy deposition by thermal smoothing via heat conduction.     

Novel composite nanofibers based on polyamide 66/graphene oxide- grafted aliphatic- aromatic polyamide: preparation and characterization

New polyamide 66/graphene oxide (GO)-grafted aliphatic-aromatic polyamide (polyamide-imide) (PAI) (PA66/GOF) composites nanofibers were successfully prepared via electrospinning method for the first time. An polyamide imide (PAI) was synthesized using polycondensation reaction from a dicarboxylic acid and a diamine based on 4,4′-(4,4′-isopropylidenediphenyl-1,1′-diyldioxy) dianiline, and characterized by ¹HNMR and FTIR. Morphological, structural, thermal and mechanical characteristics of the nanocomposite fibers were investigated by means of SEM, TEM, WAXD, DMTA and TGA techniques. Composites nanofibers of PA66/GO, PA66/PAI and PA66/GOF with smooth surface, uniform structure as well as with diameter ranging from 195 to 784 nm were obtained. The GO incorporation caused a reduction in the nanofibers diameters. The TEM images showed that the GO was well dispersed in the PA66 nanofibers without significant aggregation. An approximately 10 °C temperature increase in the glass transition temperature of PA66 was achieved by addition of 0.5 wt% of PAI, resulting from aliphatic-aromatic structure of PAI. By the TGA results, an increase about 40 °C was observed in the thermal stability of PA66/PAI composite nanofibers in comparison with that of pure PA66 nanofibers.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.