In this research, the final concentration of cadmium in an electrochemical removal process is estimated by an artificial neural network (ANN) model. The ANN model based on experimental data obtained by a removal process of cadmium from dilute aqueous solutions under galvanostatic mode in a flow-through cell. The pH, current density, and electrolysis time were considered as input variables. An analysis of the hyperbolic tangential-sigmoidal (TANSIG) and logarithmic-sigmoidal (LOGSIG) transfer function was developed to obtain the best accuracy model. To validate the accuracy and the adaptability of the model proposed, statistical and linearity tests (slope-intercept) were performed. The best model with architecture 3:3:1 was validated with a R2 value of 0.9850 and a MSE value of 0.00166, besides approved linearity tests with 99% confidence.
|Number of pages||10|
|Journal||International Journal of Environmental Science and Technology|
|State||Published - Aug 2022|
Bibliographical noteFunding Information:
The authors are grateful to the National Council for Science and Technology (CONACyT) of Mexico and the Cátedras CONACyT program 2017 under project No. 404 for the grant given to D.E.M-O and the support provided to A.P-B, respectively.
© 2021, Islamic Azad University (IAU).
- Artificial neural network
- Electrochemical removal