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Recently, removal of arsenic from different industrial effluent discharged using simple, efficient and low-cost technique has been widely considered. In this study, removal of arsenic (As) from real wastewater has been studied employing modified bio-oxidation followed by adsorptive filtration method in a novel continuous flow through the reactor. This method includes biological oxidation of ferrous to ferric ions by immobilized Acidothiobacillus ferrooxidans bacteria on granulated activated carbon (GAC) in fixed bed bio-column reactor with the adsorptive filtration unit. Removal efficiency was optimized regarding the initial flow rate of media and ferrous ions concentration. Synthetic wastewater sample having different heavy metal ions such as Arsenic (As), Cobalt (Co), Chromium (Cr), Copper (Cu), Iron (Fe), Lead (Pb) and Manganese (Mn) were also used in the study. The structural and surface changes occurring after the treatment process were scrutinized using FT-IR and Scanning Electron Microscopy (SEM) analysis. The finding showed that not only arsenic can be removed considerably in the bioreactor system, but also removing efficiency was much more (<90%) for other heavy metals in real wastewater sample. The results from TCPL test confirms that solid spent media was non-hazardous and can be safely disposed of. This study verified that combination of bio-oxidation with adsorptive filtration method improves the removal efficiency of arsenic and other heavy metal ions in wastewater sample.

Electronic nose systems when deployed in network mesh can effectively provide a low budget and onsite solution for the industrial obnoxious gaseous measurement. For accurate and identical prediction capability by all the electronic nose systems, a reliable calibration transfer model needs to be implemented in order to overcome the inherent sensor array variability. In this work, robust regression (RR) is used for calibration transfer between two electronic nose systems using a Box-Behnken (BB) design. Out of the two electronic nose systems, one was trained using industrial gas samples by four artificial neural network models, for the measurement of obnoxious odours emitted from pulp and paper industries. The emissions constitute mainly of hydrogen sulphide (H2S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl disulphide (DMDS) in different proportions. A Box-Behnken design consisting of 27 experiment sets based on synthetic gas combinations of H2S, MM, DMS and DMDS, were conducted for calibration transfer between two identical electronic nose systems. Identical sensors on both the systems were mapped and the prediction models developed using ANN were then transferred to the second system using BB-RR methodology. The results showed successful transmission of prediction models developed for one system to other system, with the mean absolute error between the actual and predicted concentration of analytes in mg L(-1) after calibration transfer (on second system) being 0.076, 0.1801, 0.0329, 0.427 for DMS, DMDS, MM, H2S respectively., (Copyright © 2014 Elsevier B.V. All rights reserved.)