TY - JOUR
T1 - Electrochemical evaluation of an Acanthocereus tetragonus aqueous extract on aluminum in NaCl (0.6 M) and HCl (1 M) and its modelling using forward and inverse artificial neural networks
AU - Méndez-Figueroa, Henevith G.
AU - Ossandón, Sebastián
AU - Ramírez Fernández, José Arturo
AU - Galván Martínez, Ricardo
AU - Espinoza Vázquez, Araceli
AU - Orozco-Cruz, Ricardo
N1 - Funding Information:
H. G. Méndez-Figueroa acknowledges support from the master’s program of corrosion engineering at the ”Instituto de Ingeniería” of the Universidad Veracruzana, which is part of the PNPC at CONACyT.
Funding Information:
S. Ossandón acknowledges support from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 777778: MATHROCKS PROJECT and from the project DI INVESTIGACIÓN INNOVADORA INTERDISCIPLINARIA PUCV 2021 N° 039.409/2021. Nanoiónica: Un enfoque interdisciplinario.
Funding Information:
H. G. Méndez-Figueroa acknowledges support from the master's program of corrosion engineering at the ”Instituto de Ingeniería” of the Universidad Veracruzana, which is part of the PNPC at CONACyT. S. Ossandón acknowledges support from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 777778: MATHROCKS PROJECT and from the project DI INVESTIGACIÓN INNOVADORA INTERDISCIPLINARIA PUCV 2021 N° 039.409/2021. Nanoiónica: Un enfoque interdisciplinario.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - An innovative numerical method based on a machine learning approach is presented in order to model the electrochemical behaviour of an Acanthocereus tetragonus aqueous extract on Aluminum in acidic and neutral media. Experimental data of an electrochemical evaluation of Aluminum in HCl (1 M) and NaCl (0.6 M) were used to generate the training set for forward Artificial Neural Networks (ANN). Later, this nonlinear relationship is inverted and refined with the purpose of design and train an inverse ANN that solves the following inverse problem: to find the concentration of a green corrosion inhibitor and the exposure time as a function of pH values, real and imaginary impedance values, and a frequency range of measure between 10,000 and 0.01 Hz.
AB - An innovative numerical method based on a machine learning approach is presented in order to model the electrochemical behaviour of an Acanthocereus tetragonus aqueous extract on Aluminum in acidic and neutral media. Experimental data of an electrochemical evaluation of Aluminum in HCl (1 M) and NaCl (0.6 M) were used to generate the training set for forward Artificial Neural Networks (ANN). Later, this nonlinear relationship is inverted and refined with the purpose of design and train an inverse ANN that solves the following inverse problem: to find the concentration of a green corrosion inhibitor and the exposure time as a function of pH values, real and imaginary impedance values, and a frequency range of measure between 10,000 and 0.01 Hz.
KW - Aluminum
KW - Artificial Neural Networks
KW - Forward problems
KW - Green corrosion inhibitor
KW - Inverse problems
KW - pH
UR - http://www.scopus.com/inward/record.url?scp=85131085288&partnerID=8YFLogxK
U2 - 10.1016/j.jelechem.2022.116444
DO - 10.1016/j.jelechem.2022.116444
M3 - Article
AN - SCOPUS:85131085288
VL - 918
JO - Journal of Electroanalytical Chemistry
JF - Journal of Electroanalytical Chemistry
SN - 1572-6657
M1 - 116444
ER -