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

Henevith G. Méndez-Figueroa, Sebastián Ossandón, José Arturo Ramírez Fernández, Ricardo Galván Martínez, Araceli Espinoza Vázquez, Ricardo Orozco-Cruz

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number116444
JournalJournal of Electroanalytical Chemistry
Volume918
DOIs
StatePublished - 1 Aug 2022
Externally publishedYes

Keywords

  • Aluminum
  • Artificial Neural Networks
  • Forward problems
  • Green corrosion inhibitor
  • Inverse problems
  • pH

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this