Vol. 10 Issue 3
Year:2015
Issue:Feb-Apr
Title:Artificial Neural Network (ANN) Approach for Modeling Chromium (VI) Adsorption from Waste Water Using A Custard Apple Peel Powder
Author Name:D. Krishna and R. Padma Sree
Synopsis:
Artificial Neural Network (ANN) was developed by a single layer feed forward back propagation network to the batch experimental data to develop and validate a model that can predict Cr (VI) removal efficiency. 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 or empirical calculations. The prediction of removal of Cr (VI) from wastewater has been made using variables of pH, adsorbent dosage and initial chromium (VI) concentration. Different types of ANN architecture were tested by varying the neuron number of entrance and the hidden layers, resulting in an excellent agreement between the experimental data and the predicted values. The high 2 correlation coefficient (R =0.992) between the ANN model and the experimental data showed that the model was able to find out the percentage removal of chromium (VI) proficiently. Pattern search method in genetic algorithm was used to obtain the optimum values of input parameters for the maximum percentage removal of chromium (VI).
Year:2015
Issue:Feb-Apr
Title:Artificial Neural Network (ANN) Approach for Modeling Chromium (VI) Adsorption from Waste Water Using A Custard Apple Peel Powder
Author Name:D. Krishna and R. Padma Sree
Synopsis:
Artificial Neural Network (ANN) was developed by a single layer feed forward back propagation network to the batch experimental data to develop and validate a model that can predict Cr (VI) removal efficiency. 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 or empirical calculations. The prediction of removal of Cr (VI) from wastewater has been made using variables of pH, adsorbent dosage and initial chromium (VI) concentration. Different types of ANN architecture were tested by varying the neuron number of entrance and the hidden layers, resulting in an excellent agreement between the experimental data and the predicted values. The high 2 correlation coefficient (R =0.992) between the ANN model and the experimental data showed that the model was able to find out the percentage removal of chromium (VI) proficiently. Pattern search method in genetic algorithm was used to obtain the optimum values of input parameters for the maximum percentage removal of chromium (VI).
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