Synthesis of green marine algal-based biochar for remediation of arsenic(V) from contaminated waters in batch and column mode of operation.

The sorption behavior of biochar derived from green seaweed (Ulva reticulata) toward arsenic(V) ions was explored in both batch and continuous modes. The pH edge experiments indicated optimum arsenic(V) sorption observed at pH 4, with maximum sorptional capacity of 7.67 mg/g through isotherm experiments. The kinetic experimental trials indicated that arsenic(V) sorption onto biochar was a fast electrostatic attraction process, with maximum removal occurred within 30 min. The sorption isotherms were modeled using the Toth, Redlich-Peterson, Langmuir and Freundlich isotherm models while the adsorption kinetics was modeled using the pseudo-first- and pseudo-second-order kinetic equations. The three-parameter models (Redlich-Peterson and Toth) better described the isotherm data, whereas pseudo-first-order model represented kinetic data well with low error and high correlation coefficient values. Among the different alkaline and acidic elutants investigated, the solution of 0.01 M NaOH effectively desorbed arsenic(V) from spent biochar. The feasibility of the biochar in continuous remediation of arsenic(V) from contaminated waters was explored in an up-flow fixed column. The biochar exhibited arsenic(V) removal efficiency and sorptional uptake of 59.5% and 8.12 mg/g, respectively. The biochar-loaded column was effectively desorbed using NaOH (0.01 M), with desorption efficiency of 99.5%.

Green alga-mediated treatment process for removal of zinc from synthetic solution and industrial effluent.

The present work explored biosorption of Zn(II) ions from aqueous and zinc-bearing factory effluent using marine seaweed Ulva lactuca. The batch pH edge experiments using aqueous zinc solution indicated that Zn(II) uptake by U. lactuca was found to be maximum at pH 4.5 and the batch isotherm trials performed at pH 4.5 resulted in maximum uptake capacity of 128.0 mg Zn(II)/g. With 0.1 M CaCl2 (pH 3.5, HCl) as elutant, the elution of Zn(II) ions from Zn(II)-laden U. lactuca biosorbent was effective with possible regeneration and reuse for three cycles. The zinc industrial effluent was found to comprise of 87.8 mg/L of zinc ions along with excess co-ions and high total dissolved solids (838.1 mg/L). Owing to this, Zn(II) uptake from electroplating effluent by U. lactuca was suppressed due to competition from other ions. Continuous-flow sorption trials were conducted at flow rate of 5 mL/min in an up-flow fixed column. The existence of surplus competing ions in zinc wastewater influenced the Zn(II) biosorption by U. lactuca. U. lactuca-loaded packed column exhibited uptakes of 78.3 and 70.8 mg Zn(II)/g for aqueous solution and effluent, respectively. The results of three continuous sorption-desorption cycles demonstrated that reuse of U. lactuca biosorbent in remediation of zinc-containing wastewaters was practical and economical.

Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks.

Various low-cost adsorbents have been used for removing Cu(II) ions from aqueous solutions for the treatment of copper containing wastewaters to remove organic compounds and color. Sawdust is an impressive adsorbent in terms of adsorption efficiency, cost and availability; hence the use of sawdust as biosorbent has been widely studied. Many earlier investigations tried to correlate the experimental data with available models or some modified empirical equations, but these results were unable to predict the values of parameters from a single equation. Artificial neural networks (ANN) are effective in modeling and simulation of highly non-liner multivariable relationships. A well-designed and very well trained network can converge even on multiple number of variables at a time without any complex modeling and empirical calculations. In this present work ANN is applied for the prediction of percentage adsorption efficiency for the removal of Cu(II) ions from aqueous solutions by sawdust. Artificial neural network model, based on multilayered partial recurrent back-propagation algorithm has been used. The performance of the network for predicting the sorption efficiency of sawdust for copper is found to be very impressive.