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Bioremediation of the Cr complex dye (Acid black 52) was performed in batch and continuous modes using growingAspergillus tamarii. The removal of Cu which may be present as an impurity was 100% at 100 mg/L initial dye concentration. The removal of color and Cr decreased from 87% to 4% and from 92% to 8%, respectively, by increasing dye concentration from 100 to 5000 mg/L in batch mode. The removal of color and Cr increased from 27% to 67.8% and from 32% to 72%, respectively, with increasing hydraulic retention time from 28 to 220 h at 100 mg/L dye concentration in continuous mode. For optimization of color removal using response surface methodology (RSM) the ranges of parameters were kept at dye concentration: 200–500 mg/L; pH: 4–6 and time: 35–50 hours. Maximum color removal suggested by the model was 85.6809% at initial dye concentration 200 mg/L, pH 5.25 and time 50 h. The validation experiments in batch and continuous modes were conducted at the optimum conditions as suggested by the RSM model. The theoretical and experimental responses of color removal were in close agreement in batch mode. The scanning electron microscopy, energy dispersive X-ray analysis, transmission electron microscopy and gas chromatography–mass spectroscopy analyses indicated biosorption and biodegradation of dye. [ABSTRACT FROM PUBLISHER]

Copper resistant bacteria, Stenotrophomonas species was isolated from a soil from Dhapa, a solid waste disposal site at Kolkata, India. This copper-resistant bacteria Stenotrophomonas maltophilia strain PD2 which can reduce copper from aqueous solution, was isolated and reported for the first time. Dead bacterial biomass prepared and used to remove Cu (II) from aqueous solution was investigated using batch techniques. The influence of different parameters on copper uptake by dried biomass, such as initial copper concentration and initial pH of the solution was studied at different intervals of time. A maximum of 83% copper removal efficiency was obtained using 0.5g biomass and 50 mg L-1 copper concentration. Optimization of process parameters was performed using Response Surface Methodology (RSM). The multiple correlation coefficient (R²) was found to be 0.9990. The deviation between theoretical and experimental results was 0.016%. Computational simulated artificial neural network (ANN) was developed to get a good correlation between the input variables responsible for copper removal and the output parameter (% removal) of the process. The correlation coefficient (R) of ANN is 0.958. The optimization procedure of biosorption showed a close interaction between the experimental and simulated values of copper removal. [ABSTRACT FROM AUTHOR]

Copper is one of the heavy metals, which are essential but toxic at higher concentrations. High uptakes of copper may cause liver and kidney damage and even death. This study was performed to investigate copper reduction using soil as an adsorbent. A multi-step response surface methodology was applied for the maximum reduction of Cu(II) from aqueous solution to optimize the parameters that had an effect on the adsorption studies. A two-level, three-factor (23) full factorial central composite design (CCD) using Design Expert Version 7.0.0 (Stat Ease, USA) was used for the optimization of the adsorption process parameters. From the CCD design it was observed that the optimum reduction of copper was 96.22% at pH 5.50, 31.59 g l−1of adsorbent and 90 min contact time. The deviation between the experimental and theoretical results was 0.42%. [ABSTRACT FROM PUBLISHER]

Abstract: This study investigates bioremediation of copper using copper-resistant bacteria, Stenotrophomonas maltophilia PD2. Living micro-organism was used to remove copper from aqueous solution. The influence of different parameters on bioremediation by S. maltophilia PD2, such as initial copper concentration and initial pH, contact time of the solution was studied. Response surface methodology (RSM) was applied for the optimization of the process parameters responsible for the bioremediation of copper ion effect and to evaluate the effects and interactions of the process variables. Based on the statistics analysis the optimum condition was found at pH 5.50, copper concentration 50.00mgL−1, contact time 26.00h and the copper removal was 90%. The optimization procedure of bioremediation showed a close interaction between the experimental and simulated values of copper removal. [Copyright &y& Elsevier]

Though copper is a necessary trace element but it can be toxic in higher concentration. Locally available fly ash was found to be low-cost adsorbent to remove copper from aqueous solution. Calcium oxide-treated fly ash was investigated in the study of scale up of copper removal. Response surface methodology (RSM) was used for the statistical optimization of the copper reduction process. A two-level-three-factor (23) full factorial central composite design with the help of Design Expert Version 7.0.0 (Stat Ease, USA) was used for the optimization of the copper adsorption process variables like: initial solution pH (3.5-5.5), initial copper concentration (15-50 mg/L), and contact time (45-90 min) and to evaluate the effects and interactions of process variables. The optimum reduction of copper was 99.16% at pH 3.98, 77.88 mg/L copper concentration, and 58.82 min of contact time. The percentage deviation between experimental and RSM model equation was 0.03%. [ABSTRACT FROM AUTHOR]

The ability to remove Cu2+ ions from aqueous solution using calcium oxide (Ca(OH)2) treated orange peel was investigated in the present study. Response Surface Methodology (RSM) was applied for the optimization of the process parameters responsible for the reduction of metal ion effect and to evaluate the effects and interactions of the process variables. The optimum reduction of copper was 93.4253% at pH 4.75, 55.5 mg/l copper concentration and 33.91 min of contact time. The deviation between experimental and RSM model equation was very less. Computational simulated artificial neural network (ANN) was formulated to get a good correlation between the input parameters responsible for copper removal and the output parameters (% removal) of the process. The correlation coefficient (R) of ANN is 0.967. The optimization process shows a close interaction between the observational and modeled values of copper removal. [ABSTRACT FROM AUTHOR]