TY - JOUR
T1 - Modeling of electrochemical removal of cadmium under galvanostatic mode using an artificial neural network
AU - Millán-Ocampo, D. E.
AU - Parrales-Bahena, A.
AU - de Lourdes Llovera-Hernández, Ma
AU - Silva-Martínez, S.
AU - Porcayo-Calderón, J.
AU - Hernández, J. A.
N1 - Publisher Copyright:
© 2021, Islamic Azad University (IAU).
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Cadmium
KW - Electrochemical removal
UR - http://www.scopus.com/inward/record.url?scp=85115184562&partnerID=8YFLogxK
U2 - 10.1007/s13762-021-03656-w
DO - 10.1007/s13762-021-03656-w
M3 - Artículo
AN - SCOPUS:85115184562
SN - 1735-1472
VL - 19
SP - 7437
EP - 7446
JO - International Journal of Environmental Science and Technology
JF - International Journal of Environmental Science and Technology
IS - 8
ER -