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Integral approach of sorption coupled with biodegradation for treatment of azo dye using Pseudomonas sp.: batch, toxicity, and artificial neural network

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Abstract

The present study investigated the removal of azo dye (crystal violet) by adsorption (using a low-cost adsorbent fly ash), biodegradation (using bacterial species, Pseudomonas sp.), and an integrated approach of sorption coupled with biodegradation (using fly ash immobilized with Pseudomonas sp.) on a comparative scale. To ascertain immobilization of bacteria on fly ash, immobilized bacterial cells were characterized by energy-dispersive X-ray spectroscopy, scanning electron microscopy, Fourier-transform-infrared spectroscopy, and fluorescence microscopy. Batch studies were conducted for optimization of the process parameters for ensuring maximum dye removal. The optimum pH, temperature, and initial dye concentration for the highest percentage of dye removal were found to be pH 7, 37 °C, and 50 mg/L in all the three cases. Under optimized conditions, the highest percentage of dye removal was found to be 89.24, 79.64, and 99.04% for biodegradation, sorption, and integrated approach of sorption and biodegradation, respectively. Finally, phytotoxicity studies carried out with the treated water on Cicer arietinum seeds also carried proved that these processes and the adsorbent did not exert any toxic effects on the seeds. Artificial neural network modeling revealed a close interaction between theoretically predicted and experimentally obtained results and with an error of around 1.1%. Thus, this novel, environmentally sustainable and economically viable technique may be applied for effective removal of crystal violet from industrial wastewater.

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Acknowledgements

All authors are thankful to the Department of Biotechnology, West Bengal for the financial support (Research Grant-in Aid).

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Correspondence to Papita Das.

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Roy, U., Sengupta, S., Das, P. et al. Integral approach of sorption coupled with biodegradation for treatment of azo dye using Pseudomonas sp.: batch, toxicity, and artificial neural network. 3 Biotech 8, 192 (2018). https://doi.org/10.1007/s13205-018-1215-1

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