ANN MODELING ON PREDICTIONS OF BIOSORPTION EFFICIENCY OF ZEA MAYS FOR THE REMOVAL OF Cr (III) AND Cr (VI) FROM WASTE WATER

Abhishek Kardam; Kumar Rohit Raj; Jyoti Kumar Arora; Shalini Srivastava

doi:https://doi.org/10.14445/22315373/IJMTT-V2I1P505

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 2 | Issue 1 | Year 2011 | Article Id. IJMTT-V2I1P505 | DOI : https://doi.org/10.14445/22315373/IJMTT-V2I1P505

ANN MODELING ON PREDICTIONS OF BIOSORPTION EFFICIENCY OF ZEA MAYS FOR THE REMOVAL OF Cr (III) AND Cr (VI) FROM WASTE WATER

Abhishek Kardam, Kumar Rohit Raj, Jyoti Kumar Arora ,Shalini Srivastava

Citation :

Abhishek Kardam, Kumar Rohit Raj, Jyoti Kumar Arora ,Shalini Srivastava, "ANN MODELING ON PREDICTIONS OF BIOSORPTION EFFICIENCY OF ZEA MAYS FOR THE REMOVAL OF Cr (III) AND Cr (VI) FROM WASTE WATER," International Journal of Mathematics Trends and Technology (IJMTT), vol. 2, no. 1, pp. 23-29, 2011. Crossref, https://doi.org/10.14445/22315373/IJMTT-V2I1P505

Abstract

Various low cost sorbents have been used for removal of toxic metals from aqueous solution for the treatment of Cr (III) and Cr (VI) containing waste water using agricultural wastes. Artificial Neural Network (ANN) was applied to sorption batch studies to develop and validate a model that can predict Cr (III) and Cr (VI) removal efficiency. Earlier investigations correlated the experimental data with available models or some modified empirical equations but these results were unable to predict the values of parameters from a single equation. ANN is effective in modeling and simulation of highly non linear multivariable relationships. A well designed network can converge even on multiple numbers of variables at a time without any complex modeling and empirical calculations. The prediction of removal of Cr (III) and Cr (VI) from waste water has been made using variables of metal concentration, biomass dosage, contact time and initial volume. Different types of the ANN architecture have been tested by varying the neuron number of entrance and the hidden layers, resulting into an excellent agreement between the experimental data and the predicted values. The data of one hundred eighty laboratory experimental sets were used for structuring single layer ANN model. Series of experiments resulted into the performance evaluation based on considering 20 % data for testing and 20 % data for cross validation at 3000 Epoch with 0.70 momentums. The Levenberg– Marquardt algorithm (LMA) was applied giving a minimum mean squared error (MSE) for training and cross validation.

Keywords

Artificial Neural Network; Biosorption; Zea mays; Cr (III) and Cr (VI) removal

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