TY  - EJOU
AU  - Ramírez-Fernández, José Arturo 
AU  - Méndez-Figueroa, Henevith G. 
AU  - Ossandón, Sebastián 
AU  - Galván-Martínez, Ricardo 
AU  - Hernández-Pérez, Miguel Ángel 
AU  - Orozco-Cruz, Ricardo 

TI  - An Improved PID Controller Based on Artificial Neural Networks for Cathodic Protection of Steel in Chlorinated Media
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 3
SN  - 1546-2226

AB  - In this study, artificial neural networks (ANNs) were implemented to determine design parameters for an impressed current cathodic protection (ICCP) prototype. An ASTM A36 steel plate was tested in 3.5% NaCl solution, seawater, and NS4 using electrochemical impedance spectroscopy (EIS) to monitor the evolution of the substrate surface, which affects the current required to reach the protection potential (<mml:math id="mml-ieqn-1"><mml:msub><mml:mtext>E</mml:mtext><mml:mrow><mml:mi>p</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:math>). Experimental data were collected as training datasets and analyzed using statistical methods, including box plots and correlation matrices. Subsequently, ANNs were applied to predict the current demand at different exposure times, enabling the estimation of electrochemical parameters (limiting voltage values) that can be used to optimize a self-regulating ICCP system. The obtained electrochemical parameters were then used, through Particle Swarm Optimization (PSO), to fine-tune an ANN-based proportional-integral-derivative (PID) controller for the ICCP system.
KW  - Artificial neural networks (ANNs); corrosion; impressed current cathodic protection (ICCP); proportional integral derivative (PID) corrosion control; particle swarm optimization (PSO); statistical analysis

DO  - 10.32604/cmc.2025.072707