Para-xylene adsorption separation process using nano-zeolite Ba-X

The liquid phase adsorption process was studied on nano-zeolite Ba-X for separating para-xylene from a feed mixture containing all C₈ aromatics. Nano-zeolite Ba-X with different ratios of SiO₂/Al₂O₃ was synthesized through hydrothermal process and ion-exchanged with barium. The product was characterized by X-ray diffraction, scanning electron microscopy (SEM), nitrogen adsorption and in situ Fourier transform infrared (FTIR) spectroscopy. The adsorption process was carried out in a breakthrough system at temperature range of 120–160 °C under 4–7 atm pressure. The influence of nano-zeolite water content on the separation process was studied. The optimization of adsorption process was also investigated by changing the operation conditions. The adsorption isotherm for all C₈ aromatic isomers and also desorbents indicated the typical Langmuir type. The selectivity factor of adsorbent for para-xylene and the adsorption capacity at saturation of the different adsorbate samples with each component from C₈ aromatic mixture were determined. It was observed that the selectivity of para-xylene increased by barium ion-exchange of cationic sites in nano-zeolite X and the adsorbent selectivity for para-xylene relative to each of meta-xylene, ortho-xylene and ethyl-benzene under the optimum conditions was found to be 7.191, 2.819 and 3.745, in the order given. It was also studied the influence of desorbent type on its selectivity for para-xylene compared to each isomer from the C₈ aromatic mixture.     

Rheology of fluids measured by correlation force spectroscopy

We describe a method, correlation force spectrometry (CFS), which characterizes fluids through measurement of the correlations between the thermally stimulated vibrations of two closely spaced micrometer-scale cantilevers in fluid. We discuss a major application: measurement of the rheological properties of fluids at high frequency and high spatial resolution. Use of CFS as a rheometer is validated by comparison between experimental data and finite element modeling of the deterministic ring-down of cantilevers using the known viscosity of fluids. The data can also be accurately fitted using a harmonic oscillator model, which can be used for rapid rheometric measurements after calibration. The method is non-invasive, uses a very small amount of fluid, and has no actively moving parts. It can also be used to analyze the rheology of complex fluids. We use CFS to show that (non-Newtonian) aqueous polyethylene oxide solution can be modeled approximately by incorporating an elastic spring between the cantilevers.     

Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R² value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.     

Experimental validation of a deep neural network—Sparse representation classification ensemble method

In the current study, a new pattern recognition‐based damage detection technique is developed using the frequency response function of the structure. Principal component analysis is employed as an authoritative feature extraction method for dimensional reduction of the measured frequency response function data and constructing distinct feature patterns. Subsequently, as a novel approach, an ensemble of 2 powerful classifiers containing deep neural networks and couple sparse coding classification is utilized for damage prediction of the structure because there is no individual optimal classifier for all the problems. Verification of the proposed method is evaluated by an aluminum beam experimental setup besides a numerical 3D finite element model of a truss bridge. Damage detection results elucidate that the ensemble method decisions are much more accurate compared with the individual classifier decision. The proposed ensemble method verifies to be a novel, robust, and powerful damage detection process.     

Unit commitment-based load uncertainties based on improved particle swarm optimisation

This paper presents an intelligent method based on Improved Particle Swarm Optimisation to solve a unit commitment problem that takes into account the uncertainty in the demand. This uncertainty is included in the optimisation problem as a joint chance constraint that bounds the minimum value of the probability to jointly meet the deterministic power balance constraints. The demand is modelled as a multivariate, normally distributed, random variable and the correlation among different time periods is also considered. To demonstrate the effectiveness and robustness of the proposed algorithm, a system with 10 thermal and wind units with various conditions is simulated. The results and numerical experiments are compared with other methods to provide valuable information for both operational and planning problems.     

Real-time separation of aerosolized Staphylococcus epidermidis and polystyrene latex particles with similar size distributions

For rapid and effective detection of airborne microorganisms, it is preferable to remove dust particles during the air sampling process because they can reduce the detection accuracy of measurements. In this study, a methodology of real-time separation ofaerosolized Staphylococcus epidermidis (S. epidermidis) andpolystyrene latex (PSL) particles of similar size was investigated. These two species represent biological and non-biological particles, respectively. Due to their different relative permittivities, they grasp different numbers of air ions under corona discharge. After these charged particles enter a mobility analyzer with airflow, in which an electric field is applied perpendicular to the airflow, the S. epidermidis and PSL particles separate, due to the difference in their electric mobilities, and exit through two different outlets. Purities and recoveries for S. epidermidis and PSLat their respective outlets were determined with measurements of aerosol number concentrations and ATP bioluminescence intensities at the inlet and two outlets. The results were that purities for PSL and S. epidermidis were 70% and 80%, respectively. This methodology provides a rapid and simple way to increase the detection accuracy of bacterial agents in air.

Copyright © 2017 American Association for Aerosol Research     

Effect of chemical solvents on the technological characteristics of hemp fibre/polypropylene composites

The effects of different chemical solvents on the technological properties of hemp fibre-reinforced polypropylene composites were evaluated in this experimental work. Composite profiles consisting of hemp fibre and polypropylene at 50% weight ratios, with 2% of coupling agent were fabricated using melt compounding followed by injection moulding. The composite specimens were then immersed in CH₃COOH, HNO₃, H₂SO₄, NaOH, NH₃, and C₆H₅NH₂ for different time intervals. Then, the weight loss and mechanical strength of samples were measured. Results indicated that the chemical reagents had significant effect on the weight loss of the composites. The weight loss ratio of the control samples was lower than that of those samples exposure to the chemical solvents. The tensile strength and modulus, and impact strength of composite specimens decreased after exposure to the chemical solvents. The highest mechanical reduction was observed in the case of NaOH. SEM micrographs showed that the extent of degradation increased in composites when they are exposed to chemical solvents.     

Deformation-mechanism map for Ti-6wt% Al alloy

A micro-computer program was developed to plot deformation mechanism maps for metals and substitutional solid solutions such as Ti-6 wt% Al. The package written in Turbo Basic is versatile and can be run to print out maps, using the related stored data files. In modelling the maps, the rate equations governing different deformation mechanisms were employed to find the dominant mechanisms and strain rate contours. In particular, the map for Ti-6 wt% Al was generated and it was found that the power law creep dominated over a wider range of stresses and temperatures than that of pure titanium. It is concluded that the strengthening effect of aluminium has shifted the iso-strain rate contours to higher stresses and temperatures.     

Conservative laparoscopic management of a large cornual ectopic pregnancy

Cornual pregnancy traditionally has been treated with laparotomy and either cornual resection or hysterectomy. Recently, more conservative operations have been developed, and operative laparoscopy has provided yet another management option. This report describes the conservative management of a large cornual ectopic pregnancy and reviews the techniques and outcomes of conservative repair that have been described in the literature.     

A Thienothiophene-Based Cation Treatment Allows Semitransparent Perovskite Solar Cells with Improved Efficiency and Stability

Perovskite surface treatment with additives has been reported to improve charge extraction, stability, and/or surface passivation. In this study, treatment of a 3D perovskite ((FAPbI₃)_1−x(MAPbBr₃)_x) layer with a thienothiophene-based organic cation (TTMAI), synthesized in this work, is investigated. Detailed analyses reveal that a 2D (n = 1) or quasi-2D layer does not form on the PbI₂-rich surface 3D perovskite. TTMAI-treated 3D perovskite solar cells (PSCs) fabricated in this study show improved fill factors, providing an increase in their power conversion efficiencies (PCEs) from 17% to over 20%. It is demonstrated that the enhancement is due to better hole extraction by drift-diffusion simulations. Furthermore, thanks to the hydrophobic nature of the TTMAI, PSC maintains 82% of its initial PCE under 15% humidity for over 380 h (the reference retains 38%). Additionally, semitransparent cells are demonstrated reaching 17.9% PCE with treated 3D perovskite, which is one of the highest reported efficiencies for double cationic 3D perovskites. Moreover, the semitransparent 3D PSC (TTMAI-treated) maintains 87% of its initial efficiency for six weeks (>1000 h) when kept in the dark at room temperature. These results clearly show that this study fills a critical void in perovskite research where highly efficient and stable semitransparent perovskite solar cells are scarce.