Determination of arsenic removal efficiency by ferric ions using response surface methodology

Arsenic contamination of drinking water is a serious problem in many parts of the world. The precipitation/coprecipitation method was used for arsenic removal from drinking water by ferric chloride, ferric sulfate and ferrous sulfate as coagulant. A Box-Behnken statistical experiment design method was used to investigate the effects of major operating variables such as initial arsenate concentration (10-1000 mu g L-1). coagulant dose (0.5-60 mg L-1) and pH (4-9) were investigated. Experimental data were used for determination of the response functions coefficients. Predicted values of arsenate removal obtained using the response functions were in good agreement with the experimental data. Fe(III) ions were more effective and economic than Fe(II) ion due to required lower coagulant dose and pH. In the low initial arsenate concentrations, the highest arsenate removal efficiency was required high ferric chloride and ferric sulfate dose of 50 and 40 mg L-1, while in the high initial arsenate concentrations, the highest arsenate removal efficiency was provided at low ferric chloride and ferric sulfate dose of 37 and 32 mg L-1, respectively. This study showed that Box-Behnken design and response surface methodology was reliable and effective in determining the optimum conditions for arsenic removal by coagulation and flocculation. (C) 2008 Elsevier B.V. All rights reserved

Synthesis and characterization of novel activated carbon from Medlar seed for chromium removal: Experimental analysis and modeling with artificial neural network and support vector regression

In this study, for the first time the activated carbon has been produced from medlar seed (Mespilus germanica) via chemical activation with KOH. The carbonization process was carried out at different temperatures of 450, 550, 650 and 750 °C. The Nitrogen adsorption-desorption, Fourier transform infrared spectroscopy (FTIR) and Field Emission Scanning Electron Microscope (FESEM) analyses were carried out on the adsorbents. The effect of operating parameters, such as pH, initial concentration of Cr(VI), adsorbent dosage and contact time were investigated. The experimental data showed better agreement with the Langmuir model and the maximum adsorption capacity was evaluated to be 200 mg/g. Kinetic studies indicated that the adsorption process follows the pseudo second-order model and the chemical reaction is the rate-limiting step. Thermodynamic parameters showed that the adsorption process could be considered a spontaneous (ΔG < 0), endothermic (ΔH > 0) process which leads to an increase in entropy (ΔS > 0). The application of support vector machine combined with genetic algorithm (SVM-GA) and artificial neural network (ANN) was investigated to predict the percentage of chromium removal from aqueous solution using synthesized activated carbon. The comparison of correlation coefficient (R2) related to ANN and the SVR-GA models with experimental data proved that both models were able to predict the percentage of chromium removal, by synthetic activated carbon while the SVR-GA model prediction was more accurate

Application of colloidal gas aphron suspensions produced from Sapindus mukorossi for arsenic removal from contaminated soil

Colloidal gas aphron dispersions (CGAs) can be described as a system of microbubbles suspended homogenously in a liquid matrix. This work examines the performance of CGAs in comparison to surfactant solutions for washing low levels of arsenic from an iron rich soil. Sodium Dodecyl Sulfate (SDS) and saponin, a biodegradable surfactant, obtained from Sapindus mukorossi or soapnut fruit were used for generating CGAs and solutions for soil washing. Column washing experiments were performed in down-flow and up flow modes at a soil pH of 5 and 6 using varying concentration of SDS and soapnut solutions as well as CGAs. Soapnut CGAs removed more than 70% arsenic while SDS CGAs removed up to 55% arsenic from the soil columns in the soil pH range of 5–6. CGAs and solutions showed comparable performances in all the cases. CGAs were more economical since it contains 35% of air by volume, thereby requiring less surfactant. Micellar solubilization and low pH of soapnut facilitated arsenic desorption from soil column. FT-IR analysis of effluent suggested that soapnut solution did not interact chemically with arsenic thereby facilitating the recovery of soapnut solution by precipitating the arsenic. Damage to soil was minimal arsenic confirmed by metal dissolution from soil surface and SEM micrograph