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Prediction of Biosorption of Total Chromium by Bacillus sp. Using Artificial Neural Network

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Abstract

An artificial neural network (ANN) model was developed to predict the biosorption efficiency of Bacillus sp. for the removal of total chromium from aqueous solution based on 360 data sets obtained in a laboratory batch study. Experimental parameters affecting the biosorption process such as pH, contact time and initial concentration of chromium were studied. A contact time of 2 h was generally sufficient to achieve equilibrium. At optimal conditions, metal ion uptake increased with increasing initial metal ion concentration. The Freundlich model was applied to describe the biosorption isotherm. Chromium biosorption was most significantly influenced by pH, followed by the initial metal concentration of the solution. The findings indicated that the ANN model provided reasonable predictive performance (R2 = 0.971) of chromium biosorption.

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Acknowledgments

Financial support from the Council of Scientific and Industrial Research (File no.: 24(0271)/04/EMR-II) Government of India, is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Agricultural Microbiology, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh, 202002, India

    Farhana Masood & Abdul Malik

  2. Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, 202002, India

    Masood Ahmad

  3. Department of Civil Engineering, Zakir Husain College of Engineering and Technology, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, 202002, India

    Mujib Ahmad Ansari

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  1. Farhana Masood
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  2. Masood Ahmad
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  3. Mujib Ahmad Ansari
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  4. Abdul Malik
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Correspondence to Abdul Malik.

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Masood, F., Ahmad, M., Ansari, M.A. et al. Prediction of Biosorption of Total Chromium by Bacillus sp. Using Artificial Neural Network. Bull Environ Contam Toxicol 88, 563–570 (2012). https://doi.org/10.1007/s00128-011-0517-3

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  • DOI: https://doi.org/10.1007/s00128-011-0517-3

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