Adsorption mechanisms of hexavalent chromium from aqueous solutions on modified activated carbons.

Functional groups of the activated carbon play the major role in metals removal from aqueous solutions and, for this reason, different treatments can be used to modify the adsorbent surface improving the adsorption capacity for a particular pollutant. In this research, oxidation with nitric acid, heating under an inert atmosphere, and ammonia treatment were applied to modify the activated carbon surface. The modified adsorbents were used for the removal of hexavalent chromium (Cr(VI)) from aqueous solutions at different concentrations (10-500 mg L-1), pH 6, and 25 °C. Adsorption mechanisms of Cr(VI) on the activated carbon were proposed based on the surface chemistry, adsorption/reduction, and desorption experiments. Findings demonstrate that acid functional groups of the activated carbon had an important effect on the hexavalent chromium removal. For instance, a high reduction of Cr(VI) to Cr(III) (50%) was obtained by the oxidized adsorbents, whereas the heat treated adsorbents achieved a low reduction (35%), but the ammonia-treated activated carbon achieved the lowest reduction (20%). The heat-treated adsorbent showed the best Cr(VI) adsorption capacity (48 mg g-1), especially at equilibrium Cr(VI) concentration lower than 200 mg L-1, and the fastest adsorption kinetics among the studied adsorbents. Furthermore, the highest Cr(VI) desorption (90%) was achieved with 0.1 N NaOH-NaCl solutions. In summary, an anionic/reduction coupled adsorption mechanism of Cr(VI) seems to be feasible, and the heat-treated activated carbon is an interesting option for sequestering Cr(VI) species from aqueous effluents.

Determining optimal conditions to produce activated carbon from barley husks using single or dual optimization.

When producing activated carbons from agricultural by-products, certain properties, such as yield and specific surface area, are very important for obtaining an economical and promising adsorbent material. Nevertheless, many researchers have not simultaneously optimized these properties and have obtained different optimal conditions for the production of activated carbon that either increases specific surface area but decreases yield or vice versa. In this research, the production of activated carbon from barley husks (BH) by chemical activation with zinc chloride was optimized by using a 2(3) factorial design with replicates at the central point, followed by a central composite design with two responses (the yield and iodine number) and three factors (the activation temperature, activation time, and impregnation ratio). Both responses were simultaneously optimized by using the desirability functions approach to determine the optimal conditions of this process. The findings reveal that after the simultaneous dual optimization, the maximal response values were obtained at an activation temperature of 436 °C, an activation time of 20 min, and an impregnation ratio of 1.1 g ZnCl2/g BH, although the results after the single optimization of each response were quite different. At these conditions, the predicted values for the iodine number and yield were 829.58 ± 78.30 mg/g and 46.82 ± 2.64%, respectively, whereas experimental tests produced values of 901.86 mg/g and 48.48%, respectively. Moreover, activated carbons from BH obtained at the optimal conditions primarily developed a porous structure (mesopores > 71% and micropores > 28%), achieving a high surface area (811.44 m(2)/g) that is similar to commercial activated carbons and lignocellulosic-based activated carbons. These results imply that the pore width and surface area are large enough to allow the diffusion and adsorption of pollutants inside the adsorbent particles. In summary, two responses were optimized to determine the optimal conditions for the production of activated carbons because it is possible to increase both the specific surface area and yield.

Experimental design for the optimization of copper biosorption from aqueous solution by Aspergillus terreus.

An experimental design methodology was applied to study the effects of temperature, pH, biomass dose, and stirring speed on copper removal from aqueous solutions by Aspergillus terreus in a biosorption batch system. To identify the effects of the main factors and their interactions on copper removal efficiency and to optimize the process, a full 2(4) factorial design with central points was performed. Four factors were studied at two levels, including stirring speed (50-150 min(-1)), temperature (30-50°C), pH (4-6) and biosorbent dose (0.01-0.175 g). The main factors observed were pH and biomass dose, along with the interactions between pH and biomass, and stirring speed. The optimal operational conditions were obtained using a response surface methodology. The adequacy of the proposed model at 99% confidence level was confirmed by its high adjusted linear coefficient of determination (R(Adj)(2)=0.9452). The best conditions for copper biosorption in the present study were: pH 6, biosorbent dose of 0.175 g, stirring speed of 50 min(-1) and temperature of 50°C. Under these conditions, the maximum predicted copper removal efficiency was 68.52% (adsorption capacity of 15.24 mg/g). The difference between the experimental and predicted copper removal efficiency at the optimal conditions was 4.8%, which implies that the model represented very well the experimental data.

Artificial Neural Network for predicting biosorption of methylene blue by Spirulina sp.

An artificial neural network (ANN) was used to predict the biosorption of methylene blue on Spirulina sp. biomass. Genetic and anneal algorithms were tested with different quantity of neurons at the hidden layers to determine the optimal neurons in the ANN architecture. In addition, sensitivity analyses were conducted with the optimised ANN architecture for establishing which input variables (temperature, pH, and biomass dose) significantly affect the predicted data (removal efficiency or biosorption capacity). A number of isotherm models were also compared with the optimised ANN architecture. The removal efficiency or the biosorption capacity of MB on Spirulina sp. biomass was adequately predicted with the optimised ANN architecture by using the genetic algorithm with three input neurons, and 20 neurons in each one of the two hidden layers. Sensitivity analyses demonstrated that initial pH and biomass dose show a strong influence on the predicted removal efficiency or biosorption capacity, respectively. When supplying two variables to the genetic algorithm, initial pH and biomass dose improved the prediction of the output neuron (biosorption capacity or removal efficiency). The optimised ANN architecture predicted the equilibrium data 5,000 times better than the best isotherm model. These results demonstrate that ANN can be an effective way of predicting the experimental biosorption data of MB on Spirulina sp. biomass.