Abstract
The tendency of selenium to interact with heavy metals in presence of naturally occurring species has been exploited for the development of green bioremediation of toxic metals from soil using Artificial Neural Network (ANN) modeling. The cross validation of the data for the reduction in uptake of Hg(II) ions in the plant R. sativus grown in soil and sand culture in presence of selenium has been used for ANN modeling. ANN model based on the combination of back propagation and principal component analysis was able to predict the reduction in Hg uptake with a sigmoid axon transfer function. The data of fifty 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 1,500 Epoch with 0.70 momentums The Levenberg–Marquardt algorithm (LMA) was found as the best of BP algorithms with a minimum mean squared error at the eighth place of the decimal for training (MSE) and cross validation.
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Acknowledgements
The authors gratefully acknowledge Prof. V.G. Dass, Director and Prof. L.D. Khemani, Head, Department of Chemistry, Dayalbagh Educational Institute, Dayalbagh, Agra. Prof. N. Verma, IIT, Kanpur and Dr S. Paul, DEI is acknowledged for fruitful scientific discussions.
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Raj, K.R., Kardam, A., Arora, J.K. et al. Artificial Neural Network (ANN) design for Hg–Se interactions and their effect on reduction of Hg uptake by radish plant. J Radioanal Nucl Chem 283, 797–801 (2010). https://doi.org/10.1007/s10967-009-0415-x
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DOI: https://doi.org/10.1007/s10967-009-0415-x