Removal of Ni (II) from aqueous solution by an electric arc furnace slag using artificial neural network approach

An artificial neural network (ANN) was built to model the adsorption of nickel on electric arc furnace slag (EAFS). The effect of operating parameters such as pH, the initial metal ion concentration, particle size, and adsorbent dosage were investigated to optimize the sorption process. The operating variables were used as the input for a neural network, which predicted the nickel (II) ion uptake at any time point as the output. The adsorbent was characterized by SEM and BET measurements. From the experimental results the adsorption capacity of 45% was obtained at pH of 8, also as when the adsorbent dosage increases from 0.1 to 1 g/l there is an increase in the percentage removal of Ni(II) ion from 25% to 37% respectively. Further more from the particle size analysis result, it revealed that as the particle size increases from 0.5µm to 3mm the percentage removal of Ni(II) ion decrease from 52% to 33%. Finally by increasing the initial concentration of Ni(II) ion from 50 to 1000 mg L-1, the adsorption capacity also increase from 24% to 43%. The ANN models present high correlation coefficient (R²=1) was found to perform excellently in predicting the adsorption behaviour of nickel in aqueous solutions onto EAFS

Grafting Carbon Nanotubes on Glass Fiber by Dip Coating Technique to Enhance Tensile and Interfacial Shear Strength

The effects of noncovalent bonding and mechanical interlocking of carbon nanotubes (CNT) coating on tensile and interfacial strength of glass fiber were investigated. CNT were coated over glass fiber by a simple dip coating method. Acid treated CNT were suspended in isopropanol solution containing Nafion as binding agent. To achieve uniform distribution of CNT over the glass fiber, an optimized dispersion process was developed by two parameters: CNT concentration and soaking time. CNT concentration was varied from 0.4 to 2 mg/mL and soaking time was varied from 1 to 180 min. The provided micrographs demonstrated appropriate coating of CNT on glass fiber by use of CNT-Nafion mixture. The effects of CNT concentration and soaking time on coating layer were studied by performing single fiber tensile test and pull-out test. The obtained results showed that the optimum CNT concentration and soaking time were 1 mg/mL and 60 min, respectively, which led to significant improvement of tensile strength and interfacial shear stress. It was found that, at other concentrations and soaking times, CNT agglomeration or acutely curly tubes appeared over the fiber surface which caused a reduction of nanotubes interaction on the glass fiber

Effect of intermediate layer in photocurrent improvement of three-layer photoanodes using WO3 and Fe2O3

Sol–gel method was applied to synthesize WO3/Fe2O3 three-layer films in order to improve the generated photocurrent under UV–vis light irradiation. The films were deposited on FTO glass substrates through doctor bladding method. The samples were then calcined at 500 °C. The photocurrents of the synthesized photoanodes were evaluated by measuring the electric current and voltage under UV–vis light at room temperature. Scanning electron microscopy (SEM) revealed unique surface morphologies owing to the presence of the intermediate layers. At an applied potential of 1300 mV, the WO3\Fe2O3\WO3 and Fe2O3\WO3\Fe2O3 photoanodes exhibited photocurrent densities up to 0.1 mA/cm2 and 0.6 mA/cm2, respectively. It was found that porous films with easy accessibility to the inner surface reveal high photocurrents. The intermediate layer of WO3 demonstrated higher values of photocurrent due to roughness enhancement on the upper surface with columnar tree-growth particles. However, a compact state was observed on the cross section of Fe2O3 growth. A comparison was also drawn between the two and three-layer photoanodes using Fe2O3 andWO3. The films were characterized by XRD, SEM/EDX, and UV–vis irradiation to determine the photocurrent densities

Thermal evaluation and simulation of glass wool/maerogel blanket

Aerogel blankets are composites of silica aerogel particles dispersed in a reinforcing fiber matrix that turns the brittle aerogel into a durable and flexible insulating mat. While aerogel blanket manufacture from either organic or inorganic material, they are still some concerns over current environmental issues which are common worldwide are global warming, greenhouse effect, and climate change. Awareness of this environmental concern has led to the rise in an effort to renew agricultural waste like RHA (rice husk ash) which is cheaper precursor or a simple method in ambient pressure. As part of this study, to produce an insulator; glass wool was modified by ambient pressure drying methods to fabricate the flexible aerogel blanket. In order to evaluate thermal resistance of aerogel blanket, a hot plate is used. The microstructure of these aerogel blankets are also investigated for better understanding of the production process. Knowledge of the thermo-mechanical properties is important for the optimization of the design for these heterogeneous materials. In order to assess the aerogel blanket, some technics such as thermal gravimetric analysis (TGA), scanning electron microscopic (SEM) and Fourier Transform Infrared spectrum (FTIR) was done. Moreover a simple numerical micro model have been developed to predict the effective thermal conductivity of flexible aerogel blankets, which consist of fibers, aerogel particles and air-pockets. This simulation has two parts. In the first part of simulation, the effective thermal conductivity of the aerogel composites is computed with different aerogel particles and different volume ratios using the finite element method. The numerical analysis of thermal conductivity is conducted by generating 3D models of the microstructure of the aerogel blanket. In the second part of model, the extracted result from the micro model is inputted to the real sized model to predict top surface temperature. Finally all experiment data are validated by a numerical real sized model. In this study, a flexible aerogel blanket shows very good thermal resistance compare to original glass wool which is around 35% improvement. In addition TGA reveals that Maerogel® can retard material decomposition of blanket from 270°C to 287°C. Moreover SEM and FTIR clearly show that there is a good bonding between SiO2 particles that make a strong network to tolerate high temperature and to be flexible blanket. Furthermore Maerogel® blanket structurally was simulated then was validated by experiment result that showed good agreement; there is a well matching between the data that were extracted from simulation and experiment

Thermochemical Properties of Glass Wool/Maerogel Composites

Aerogel blankets are composites of silica aerogel particles dispersed in a reinforcing fiber matrix that turns the brittle aerogel into durable and flexible insulating materials. In this study, silica aerogel was loaded on glass wool with different concentrations (0-18.6%) and morphological and thermal characteristics of the aerogel blankets were studied. Rate of modified blanket decomposition was slower at temperatures between 250°C and 650°C due to the retardant effect of the silica aerogel. The average diameter of the fiber for either original glass wool or modified glass wool materials was approximately 20 μm and samples had porous, interconnected particles with dendritic-like structure. © 2016 Bahador Dastorian Jamnani et al.info:eu-repo/semantics/publishe

ORIGINAL ARTICLE Corresponding Author Removal of Ni (II) from aqueous solution by an electric arc furnace slag using artificial neural network approach

; Removal of Ni (II) from aqueous solution by an electric arc furnace slag using artificial neural network approach ABSTRACT An artificial neural network (ANN) was built to model the adsorption of nickel on electric arc furnace slag (EAFS). The effect of operating parameters such as pH, the initial metal ion concentration, particle size, and adsorbent dosage were investigated to optimize the sorption process. The operating variables were used as the input for a neural network, which predicted the nickel (II) ion uptake at any time point as the output. The adsorbent was characterized by SEM and BET measurements. From the experimental results the adsorption capacity of 45% was obtained at pH of 8, also as when the adsorbent dosage increases from 0.1 to 1 g/l there is an increase in the percentage removal of Ni(II) ion from 25% to 37% respectively. Further more from the particle size analysis result, it revealed that as the particle size increases from 0.5µm to 3mm the percentage removal of Ni(II) ion decrease from 52% to 33%. Finally by increasing the initial concentration of Ni(II) ion from 50 to 1000 mg L -1 , the adsorption capacity also increase from 24% to 43%. The ANN models present high correlation coefficient (R 2 =1) was found to perform excellently in predicting the adsorption behaviour of nickel in aqueous solutions onto EAFS