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

• Published in: International journal of environmental science and technology (Tehran) 2022-08, Vol.19 (8), p.7437-7446
• Main Authors: Millán-Ocampo, D. E., Parrales-Bahena, A., de Lourdes Llovera-Hernández, Ma, Silva-Martínez, S., Porcayo-Calderón, J., Hernández, J. A.
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Science and Engineering; Original Paper; Soil Science & Conservation; Waste Water Technology; Water Management; Water Pollution Control
• ISSN: 1735-1472; 1735-2630
• DOI: 10.1007/s13762-021-03656-w

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 R 2 value of 0.9850 and a MSE value of 0.00166, besides approved linearity tests with 99% confidence.