Eduardo Soto-Regalado

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.

Methylene blue biosorption by pericarp of corn, alfalfa, and agave bagasse wastes

The presence of dyes in effluent is a matter of concern due to their toxicologic and aesthetical effects. In this research, locally available agro-industrial wastes (Zea mays pericarp, ZMP; Agave tequilana bagasse, ATB; and Medicago sativa waste, MSW) were used as alternative low-cost adsorbents for the removal of methylene blue (MB) from aqueous solutions. The adsorbents were characterized physically and chemically by Fourier transform infrared, scanning electron microscopy, potentiometric titrations, and N2 physisorption. MB adsorption experiments were carried out in batch systems and experimental data were used to calculate the adsorption isotherm model parameters (Langmuir, Freundlich, and Temkin) and the adsorption kinetic model parameters (pseudo-first- and pseudo-second-order models). MB-loaded biosorbents were desorbed with deionized water, ethanol (10% and 50% v/v), hydrochloric acid (0.01 and 0.05 N), and sodium hydroxide (0.1 N) at room temperature, and the best eluent was used in various adsorption–desorption cycles. The selected agricultural wastes can be considered as promising adsorbents for dye uptake from water since they exhibit considerable MB adsorption capacity (MSW 202.6 mg g−1, ATB 156.2 mg g−1, and ZMP 110.9 mg g−1), but it is lower than that reported for activated carbon; however, the biosorbents show higher adsorption rate than powdered activated carbon. Furthermore, the adsorbents can be economically regenerated with HCl solutions and reused for seven adsorption–desorption cycles.

Studies of Adsorption of Heavy Metals onto Spent Coffee Ground: Equilibrium, Regeneration, and Dynamic Performance in a Fixed-Bed Column

Equilibrium and dynamic adsorption of heavy metals onto spent coffee ground (SCG) were studied. The equilibrium adsorption of Cd2

Phenol and methylene blue adsorption on heat-treated activated carbon: Characterization, kinetics, and equilibrium studies:

A comprehensive study was performed for a thermally treated activated carbon to evaluate the influence of this treatment on the physical and chemical properties of the mineral activated carbon, as well as the adsorption toward phenol and methylene blue. After the heat treatment, surface area decreased and total pore volume diminished about 8.5%, and the total basic groups decreased 18% while the total acid groups increased 8% in comparison with the raw activated carbon. Equilibrium adsorption of phenol and methylene blue was described well with the Freundlich and Langmuir isotherm models, respectively. Adsorption kinetics of phenol and methylene blue was predicted adequately with the empirical pseudo-second-order model, the intraparticle diffusion model, and the homogeneous solid diffusion model, but mass transfer coefficients of the diffusion models help to better understand the adsorption phenomenon. Intraparticle diffusion seems to be the rate-controlling step in the adsorption process, and heat-treated activated carbon in an inert atmosphere was a better adsorbent for both phenol and methylene blue than raw activated carbon.

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.

Artificial neural networks for modeling the reverse osmosis unit in a wastewater pilot treatment plant

AbstractThis paper presents experimental and modeling data from a membrane-based wastewater treatment (WWT) pilot plant. The effluents from various upstream steps of a can-manufacturing plant were combined and subjected to a pretreatment process, which consisted of coalescing filters, coagulation and gravity settling, and sand activated carbon and polishing filtration, and a pressure-driven membrane process, such as reverse osmosis (RO). The performance of the RO membrane was evaluated and experiments were conducted using continuous wastewater flow. The complete membrane separation scheme was validated with a closed loop cell through several experiments, in which the concentration of the antiscaling agent and the pH were varied to determine the optimal operational conditions. Detailed parametric studies for these continuous flow experiments were conducted, and the permeate flow rates in the RO membrane system were experimentally measured. The experimental flow data were correlated and analyzed using an ar...

A mass transfer model for the fixed-bed adsorption of ferulic acid onto a polymeric resin: axial dispersion and intraparticle diffusion.

ABSTRACT In this study, amberlite XAD-16 (XAD-16) bed column system was used to remove ferulic acid (FA) from aqueous solutions. Laboratory-scale column experiments were conducted in downflow fixed bed at initial FA concentration of 1 g/L, initial pH 3, and 25°C. The performance of the adsorbent bed under different flow rates (1.3–7.7 mL/min) was studied. The bed utilization efficiency was in the range of 64.64–72.21% at the studied flow rates. A mass transfer model considering both axial dispersion and intraparticle diffusion was developed to predict the breakthrough curves of FA adsorption on XAD-16. This model predicted the experimental data better than Bohart–Adams model and Thomas model, based on the low deviation between predicted and experimental data. The axial dispersion coefficient value varied from 6.45 × 10−6 to 1.10 × 10−6 m2/s at flow rate from 1.3 to 7.7 mL/min, whereas the intraparticle diffusion coefficient was 1.04 × 10−10 m2/s, being this last resistance the rate-limiting step. In conclusion, axial dispersion and intraparticle diffusion phenomena play the major role in predicting the adsorption of FA onto XAD-16 in fixed-bed columns. GRAPHICAL ABSTRACT

Erratum to: Biosorption of Cu(II) and Pb(II) from aqueous solutions by chemically modified spent coffee grains

The original version of this article unfortunately contained mistakes. The presentations of Figs. 1 and 2 were incorrect. The correct versions of the figures are given in following page.