The Acute Toxicity of SiO2 and Fe3O4 Nano-particles on Daphnia magna

Silicon - SiO2 nano-particles are applied in different industries such as ceramic producing, glass making, cosmetic products, medicines, magnetic mixtures, heat and electric insulators and glazing...     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

A crossover‐UNIQUAC model for critical and noncritical LLE calculations

A new model, named the crossover‐UNIQUAC model, has been proposed based on the crossover procedure for predicting constant‐pressure liquid–liquid equilibria (LLE). In this manner, critical fluctuations were incorporated into the classical UNIQUAC equation. Coexistence curves were estimated for systems having a diverse range of asymmetries. These systems included the LLE of five different mixtures, composed of nitrobenzene with one of the members of the alkane homologous family (either pentane, octane, decane, dodecane, or tetradecane), as well as an extra system having a different chemical nature, namely the mixture of n‐perfluorohexane and hexane, to further check the validity of the proposed approach. Using these nonideal mixtures, the validity of the new model was investigated within wide ranges, covering near‐critical to regions falling far away from the critical point. The graphical trends, as well as the quantitative comparison with experimental data indicated the good agreement of the proposed model results with the experimental data. A maximum AARD% value of 3.97% was obtained in calculating molar compositions by the proposed model for such challenging systems covering noncritical, as well as critical regions. In addition, to show the strength of the proposed crossover approach to describe properties other than LLE, molar heat capacities were investigated for the system of nitrobenzene + dodecane. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3094–3103, 2015     

Statistical analysis and optimization of simultaneous biological nutrients removal process in an intermittently aerated SBR

Simultaneous removal of carbon and nutrients from a synthetic wastewater in aerobic/anoxic sequence batch reactor (SBR) was investigated. The experiments were conducted based on a central composite design (CCD) and analyzed using response surface methodology (RSM). Two significant independent variables, cycle time and aeration time, were studied to analyze the process. Five dependent parameters—total COD (TCOD) removal, total nitrogen removal, total phosphorus removal, total Kjeldahl nitrogen removal and effluent nitrate concentration—were monitored as the process responses. The region of exploration for the process was taken as the area enclosed by cycle times (2, 4.25 and 6.5 h) and aeration times (30, 40 and 50 min/h) boundaries. The maximum COD (87.18%) and TKN (78.94%) removal efficiencies were obtained at the cycle time and aeration time of 6.5 h and 50 min/h, respectively. While the maximum TN (71.15%) and phosphorus (68.91%) removal efficiencies were obtained at cycle time of 6.5 h and aeration time of 40min/h. As a result, high cycle time (6.5 h) and moderate aeration time (40min/h) were found to be the optimal region for maximum carbon and nitrogen removal efficiencies.     

Two-Dimensional Modeling and Simulation of Mass Transport in Microfabricated Preconcentrators

The adsorption and desorption behavior of a planar microfabricated preconcentrator (PC) has been modeled and simulated using the computational fluid dynamics (CFD) package CFDRC-ACE+trade. By comparison with the results of a designed experiment, model parameters were determined. Assuming a first-order reaction for the adsorption of a light hydrocarbon chemical analyte onto the PC adsorbent and a unity-value sticking coefficient, a rate constant of 36 500 s^-1 was obtained. This compares favorably with the value of 25 300 s^-1 obtained by application of the Modified-Wheeler equation. The modeled rate constant depends on the concentration of adsorbent sites, estimated to be 6.94 ldr 10^-8 kmol/m² for the Carboxen 1000 adsorbent used. Using the integral method, desorption was found to be first order with an Arrhenius temperature dependence and an activation energy of 30.1 kj/mol. Validation of this model is reported herein, including the use of Aris-Taylor dispersion to predict the influence of fluidics surrounding the PC. A maximum in desorption peak area with flow rate, predicted from a quadratic fit to the results of the designed experiment, was not observed in the 2-D simulation. Either approximations in the simulated model or the nonphysical nature of the quadratic fit are responsible. Despite the apparent simplicity of the model, the simulation is internally self consistent and capable of predicting performance of new device designs. To apply the method to other analytes and other adsorbent materials, only a limited number of comparisons to experiment are required to obtain the necessary rate constants.     

A comparison of co-current and counter-current modes of operation for a novel hydrogen-permselective membrane dual-type FTS reactor in GTL technology

In this work, a comparison of co-current and counter-current modes of operation for a novel hydrogen-permselective membrane reactor for Fischer-Tropsch Synthesis (FTS) has been carried out. In both modes of operations, a system with two-catalyst bed instead of one single catalyst bed is developed for FTS reactions. In the first catalytic reactor, the synthesis gas is partly converted to products in a conventional water-cooled fixed-bed reactor, while in the second reactor which is a membrane fixed-bed reactor, the FTS reactions are completed and heat of reaction is used to preheat the feed synthesis gas to the first reactor. In the co-current mode, feed gas is entered into the tubes of the second reactor in the same direction with the reacting gas stream in shell side while in the counter-current mode the gas streams are in the opposite direction. Simulation results for both co-current and counter-current modes have been compared in terms of temperature, gasoline and CO₂ yields, H₂ and CO conversion, selectivity of components as well as permeation rate of hydrogen through the membrane. The results showed that the reactor in the co-current configuration operates with lower conversion and lower permeation rate of hydrogen, but it has more favorable profile of temperature. The counter-current mode of operation decreases undesired products such as CO₂ and CH₄ and also produces more gasoline.     

Comparison of essential oils compositions of Nepeta persica obtained by supercritical carbon dioxide extraction and steam distillation methods

Essential oil of Nepeta persica cultivated in Iran was obtained by steam distillation and supercritical (carbon dioxide) extraction methods. The oils were analysed by capillary gas chromatography using flame ionization and mass spectrometric detections. The compounds were identified according to their retention indices and mass spectra (EI, 70 eV). The effects of different parameters such as pressure, temperature, modifier volume and extraction times (dynamic and static) on the supercritical fluid extraction (SFE) of N. persica oil were investigated. The results showed that under the pressure of 20.3 MPa, temperature of 45 °C, methanol of 1.5% v/v), dynamic extraction time of 50 min and static extraction time of 25 min extraction was more selective for the 4αβ,7α,7aα-nepetalactone. Twelve compounds were identified in the steam-distilled oil. The major components of N. persica were 4αβ,7α,7aα-nepetalactone (26.5%), cis-β-farnesene (4.4%) and 3,4α-dihydro-4aα,7α,7aα-nepetalactone (3.5%). However, by using supercritical carbon dioxide under optimum conditions, only two components have more than 90.0% of the oil. The extraction yield based on steam distillation was 0.08% (v/w). On the other hand, using SFE extraction yield in the range of 0.22–8.90% (w/w) were obtained at different conditions. The results show that, in Iranian N. persica oil, 4αβ,7α,7aα-nepetalactone is a major component.     

Highly efficient dye-sensitized solar cell prepared by electrophoretic deposition method: the effect of TiO<Subscript>2</Subscript> films thickness on the performance of cells

In this work, nanocrystalline P25 TiO₂ films with different thicknesses were deposited on FTO coated glass substrates by an electrophoretic deposition technique (EPD) and applied as the work electrode for dye-sensitized solar cells (DSSC) using cis-bis(isothiocyanato)(2,2'-bipyridyl-4,4'-dicarboxylato)(4,4'di-nonyl-2'-bipyridyl) ruthenium(II) (Z907, Dyesol) as sensitizing dye.The results showed that the increasing the thickness of TiO₂ films lead to increase the adsorption of the dye on the TiO₂ layers which in turns improved the short-circuit photocurrent (J_sc) and open-circuit voltage (V_oc), respectively. Furthermore, it was found that the effects of the surface states on the recombination of the photo-injected electrons (electron–hole pairs) in the TiO₂ films strongly depend on theTiO₂ electrode annealing temperature. Finally, a DSSC with a 32.82 μm thickness for TiO₂ film annealed at 600°C produced the highest conversion efficiency with an incident solar energy of 100 mW/cm² (η = 8.23%, J_sc = 15.98, V_oc = 0.73, FF = 0.7).