| Autor: |
Hakke, V. S., Gaikwad, R. W., Warade, A. R., Sonawane, S. H., Boczkaj, G., Sonawane, S. S., Sapkal, V. S. |
| Předmět: |
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| Zdroj: |
International Journal of Environmental Science & Technology (IJEST); Dec2023, Vol. 20 Issue 12, p13479-13490, 12p |
| Abstrakt: |
The removal of heavy metal ions from wastewater was found to be significant when the cation exchange procedure was used effectively. The model of the cation exchange process was built using an artificial neural network (ANN). The acid mine drainage waste's Cu(II) ion was removed using Indion 730 cation exchange resin. Experimental data from 252 cycles were recorded. In a column study, 252 experimental observations validated the three-layered ANN module's ion exchange process forecasting. The model design for the ion exchange process focuses on the process's major constraints, such as initial flow rate, initial concentration of Cu (II) ions, and AMDW residence time in the column, to fit the working environment. The maximum metal ion removal efficiency was found at 5 LPH initial flowrate, 5 pH suspension, and 60 cm bed height. With a regression value of 0.99, the proposed model matches experimental values. A hidden layer with 6 neurons and an outer layer with a linear transfer function can predict adsorption efficiency using the three-layer ANN module's backpropagation (BP) technique. A linear method was used to construct the correlation between dependent and independent variables. The BP-ANN module's coefficient of correlation was 0.99 with accurate dependent variable predictions. In a feedforward neural network, the current research's ANN module predicts the best conditions for Cu(II) ion extraction. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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