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

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