Optimized Cathode Protection Model for Best Anode Parameter Selection Using Machine Learning Approach: Iraq—Case Study

Cathodic protection is a significant approach utilized to avoid the electrochemical corrosion of pipelines.This is accomplished by supplying an electric current to the structure that requires protection, such as a pipeline, from an external source.study aims to enhance the cathodic protection system by minimizing potential fluctuations along the pipeline hence preventing corrosion.It also aims to achieve economic feasibility by decreasing the number of anodes utilized.These objectives were accomplished by employing meta-heuristic optimization techniques.The present study involves formulating a mathematical model for a pipeline that provides fuel to the Al-Hilla 2 power plant in Iraq to assess the effectiveness of cathodic protection.Utilizing numerical simulation techniques, Multiphysics COMSOL, diverse scenarios are examined, resulting in the acquisition of substantial data.Subsequently, a neural network model is constructed using MATLAB.The primary factors influencing the distribution of cathodic protection potential are the numbers and positioning of the anodes and the output current.Subsequently, the optimization objectives involve determining the optimal anode number, position, and output current value by utilizing the Particle swarm organization (PSO) algorithm.The obtained results provide evidence that the proposed method holds a certain level of significance in guiding the design of cathodic protection systems.