Modeling of electrochemical removal of cadmium under galvanostatic mode using an artificial neural network

D. E. Millán-Ocampo; A. Parrales-Bahena; Ma de Lourdes Llovera-Hernández; S. Silva-Martínez; J. Porcayo-Calderón; J. A. Hernández

doi:10.1007/s13762-021-03656-w

Modeling of electrochemical removal of cadmium under galvanostatic mode using an artificial neural network

D. E. Millán-Ocampo, A. Parrales-Bahena, Ma de Lourdes Llovera-Hernández, S. Silva-Martínez, J. Porcayo-Calderón, J. A. Hernández^*

^*Corresponding author for this work

Departamento de Ingenería Química y Metalurgia

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	7437-7446
Number of pages	10
Journal	International Journal of Environmental Science and Technology
Volume	19
Issue number	8
DOIs	https://doi.org/10.1007/s13762-021-03656-w
State	Published - Aug 2022

Bibliographical note

Funding 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.

Publisher Copyright:
© 2021, Islamic Azad University (IAU).

Keywords

Artificial neural network
Cadmium
Electrochemical removal

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10.1007/s13762-021-03656-w

Cite this

Millán-Ocampo, D. E., Parrales-Bahena, A., de Lourdes Llovera-Hernández, M., Silva-Martínez, S., Porcayo-Calderón, J., & Hernández, J. A. (2022). Modeling of electrochemical removal of cadmium under galvanostatic mode using an artificial neural network. International Journal of Environmental Science and Technology, 19(8), 7437-7446. https://doi.org/10.1007/s13762-021-03656-w

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abstract = "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.",

keywords = "Artificial neural network, Cadmium, Electrochemical removal",

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Millán-Ocampo, DE, Parrales-Bahena, A, de Lourdes Llovera-Hernández, M, Silva-Martínez, S, Porcayo-Calderón, J & Hernández, JA 2022, 'Modeling of electrochemical removal of cadmium under galvanostatic mode using an artificial neural network', International Journal of Environmental Science and Technology, vol. 19, no. 8, pp. 7437-7446. https://doi.org/10.1007/s13762-021-03656-w

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.

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

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KW - Electrochemical removal

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JO - International Journal of Environmental Science and Technology

JF - International Journal of Environmental Science and Technology

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ER -

Modeling of electrochemical removal of cadmium under galvanostatic mode using an artificial neural network

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