Optimization of Process Parameters in Bioremediation of Cr(VI) Contaminated Aqueous Solution through Response Surface Methodology

摘要

Bioremediation of metals wastes has gained significant attention in recent years. It is used to degrade toxic organic pollutants, heavy metals using living microorganisms (bacteria, fungi, algae etc.). Microorganisms show moderate to high uptake of heavy metals via different processes such as transport through cell-membrane, biosorption on cell walls and subsequent entrapment in extracellular materials, redox reaction, precipitation, complexion etc. (Rai et al., 1981; Macaskie & Dean, 1989; Avery & Tobin, 1993, Brady et al., 1994, Malik, 2004). For the last few decades, separate batch studies were carried out to optimize various process parameters such as pH, initial concentrations, contact time. But, this approach could not determine the interaction between the various process parameters. Combined effect of all the process parameters on bioremediation of heavy metals needs to be analyzed which helps during the scale-up studies. Conventional batch process is time consuming and requires a large number of experiments to be carried out. Concept of statistical methods can be exploited to mitigate the problems. Response surface methodology (RSM) is used to optimize all the process parameters collectively which reduces number of experiments and thereby reducing the overall cost of the process. RSM is a collection of mathematical and statistical methods used for modeling and optimizing the process parameters by considering the complex combined effect. The present study is aimed to optimize the bioremediation of Cr(VI) by using indigenous chromium resistant bacteria isolated from activated sludge of sewage treatment plant (BITS, Pilani, Rajasthan, India). The influence of initial Cr(VI) concentration, pH and contact time of the solution on bioremediation is studied. RSM is applied for the optimization of these process parameters to evaluate their interaction effect on bioremediation process. The optimization procedure through RSM shows a close interaction between the experimental and simulated values of Cr(VI) removal.