Electrochemical Evaluation of an Acanthocereus Tetragonus Aqueous Extract on Aluminum in Nacl (0.6m) and Hcl (1 M) and its Modelling Using Forward and Inverse Artificial Neural Networks

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 (1M) 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 − 0.01 Hz