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Rice polish", an agrowaste from rice milling industries, was utilized as potential biosorbent for removal of arsenic from water in a continuous up-flow fixed bed column system. The experiments were conducted to study the effect of important design parameters such as bed height, flow rate, and initial metal ion concentration. At a bed height of 25 cm, flow rate 1.66 mL/min, and initial metal ion concentration 1000 µg/L, the metal uptake capacity of rice polish for As(III) and As(V) was found to be 66.95 and 78.95 µg/g, respectively. The bed depth service time (BDST) model was used to analyze the experimental data. The computed sorption capacity (N o ) was 28776 and 28248 µg/L for As(III) and As(V), respectively. The rate constant (K a ) was recorded as 0.117 × 10 -3 and 0.26 × 10 -4 (L/µg)/min for As(III) and As(V), respectively. The column regeneration studies were carried out using 10% NaOH as eluant for three sorption-desorption cycles. The high arsenic removal ability and regeneration efficiency of this biosorbent suggest its applicability in industrial processes and data generated would help in further upscaling of the adsorption process.

In the present study, continuous up-flow fixed-bed column study was carried out using immobilized dead biomass of Aeromonas hydrophila for the removal of Cr(VI) from aqueous solution. Different polymeric matrices were used to immobilized biomass and polysulfone-immobilized biomass has shown to give maximum removal. The sorption capacity of immobilized biomass for the removal of Cr(VI) evaluating the breakthrough curves obtained at different flow rate and bed height. A maximum of 78.58% Cr(VI) removal was obtained at bed height of 19 cm and flow rate of 2 mL/min. Bed depth service time model provides a good description of experimental results with high correlation coefficient (> 0.996). An attempt has been made to investigate the individual as well as cumulative effect of the process variables and to optimize the process conditions for the maximum removal of chromium from water by two-level two-factor full-factorial central composite design with the help of Minitab version 15 statistical software. The predicted results are having a good agreement (R (2) = 98.19%) with the result obtained. Sorption-desorption studies revealed that polysulfone-immobilized biomass could be reused up to 11 cycles and bed was completely exhausted after 28 cycles.

Phanerochaete chrysosporium, a white rot basidiomycete, was immobilized over Luffa cylindrica sponge discs, treated with 0.1 N HCl and its potentiality for the removal of hexavalent chromium [Cr(VI)] from water was investigated in both batch and in up-flow fixed-bed bioreactor. The acid treatment of biomass increased the uptake capacity and percentage removal of Cr(VI) from 33.5 to 46.5 mg g -1 and 67 to 92 %, respectively. Maximum uptake of Cr(VI) was achieved at pH 2, temperature 40°C after 100 min of contact time. The Cr(VI) sorption on the biomass was better explained by Langmuir isotherm. Thermodynamic studies indicated that the process was spontaneous and endothermic. Sorption kinetic study showed that pseudo-second-order model best correlates the Cr(VI) sorption on the biomass as compare to pseudo-firstorder kinetic model. The performance of fixed-bed bioreactor was evaluated at different bed heights (5, 15 and 25 cm) and flow rates (1.66, 4.98 and 8.33 mL min -1 ) by using bed depth service time model. Response surface methodology statistical method was applied for optimizing the process parameters. FTIR analysis showed that amino groups were mainly involved in adsorption of Cr(VI).

The potential use of biomass of Aeromonas hydrophila for biosorption of chromium from aqueous solution was investigated. The variables (pH, initial Cr(VI) concentration, biomass dose, and temperature) affecting process were optimized by performing minimum number of experimental runs with the help of central composite design. The results predicted by design were found to be in good agreement (R2 = 99.1%) with those obtained by performing experiments. Multiple regression analysis shows that uptake decreases with increase in pH and biomass dose, whereas it increases with increase in temperature and concentration. The maximum removal of Cr(VI) predicted by contour and optimization plots was 184.943 mg/g at pH 1.5, initial Cr(VI) concentration 311.97 mg/L, temperature 60 degrees C, and biomass dose 1.0 g. The removal of Cr(VI) was governed by adsorption of Cr(VI) as well as its reduction into Cr(III), which further gets adsorbed. The sorption capacity of biomass was calculated from experimental data using Langmuir sorption model and was found to be 151.50 mg/g at 40 degrees C and pH 1.5, which is comparable to other biosorbents. In addition to this, Dubinin-Radushkevich model was applied, and it was found that nature of sorption was chemisorption.

For remediation purposes, initially, the screening and selection of potent biosorbent, among three agro-industrial wastes (i.e., wheat bran, maize bran, and rice bran) was done. Wheat bran was found to show maximum uptake in the case of both Se(IV) and Se(VI) ions. Effect of various parameters (pH, temperature, initial metal ion concentration, and biomass dose) was extensively investigated on the uptake of these metal ions by potent biosorbent using batch mode. Langmuir, Freundlich, and Dubinin−Radushkevich (D-R) isotherm models were applied and all three isotherms fitted well to sorption data. The maximum sorption capacity of wheat bran was 89.28 μg/g for Se(VI) and 80.65 μg/g for Se(VI) at 20 °C and pH 2.0. Values of mean sorption energy indicated sorption to be chemisorption. Thermodynamic study revealed that sorption was feasible, spontaneous and exothermic. The sorption reaction was determined to be pseudo-second-order. Fourier transform infrared (FTIR) analysis of raw and metal-loaded biosorbent was done to determine changes on the surface of the sorbent after sorption.

The estimation capacities of two optimization methodologies, response surface methodology (RSM) and artificial neural network (ANN) were evaluated for prediction of biosorptive remediation of As(III) and As(V) species in batch as well as column mode. The independent parameters (viz. pH, initial arsenic concentration, temperature, and biomass dose in the case of batch mode and bed height, flow rate, and initial arsenic concentration in the case of column mode) were fed as input to the central composite design (CCD) of RSM and the ANN techniques, and the output was the uptake capacity of the sorbent. The CCD was used to evaluate the simple and combined effects of the independent parameters and to derive a second-order regression equation for predicting optimization of the process. The sets of input–output patterns were also used to train the multilayer feed-forward networks employing the backpropagation algorithm with MATLAB. The application of the RSM and ANN techniques to the available experimental data showed that ANN outperforms RSM indicating the superiority of a properly trained ANN over RSM in capturing the nonlinear behavior of the system and the simultaneous prediction of the output.