In this paper the identification and control for the impressed current cathodic protection (ICCP) system are present. Firstly, an identification model using an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was implemented. The identification model consists of four inputs which are the aeration flow rates, the temperature, conductivity, and protection current, and one output that represented by the structure-to-electrolyte potential. The used data taken from an experimental CP system model, type impressed current submerged sample pipe carbon steel. Secondly, two control techniques are used. The first control technique use a conventional Proportional-Integral-Derivative (PID) controller, while the second is the fuzzy controller. The PID controller can be applied to control ICCP system and quite easy to implement. But, it required very fine tuning of its parameters based on the desired value. Furthermore, it needed time response more than fuzzy controller to track reference voltage. So the fuzzy controller has a faster and better response.
The corrosion of metallic structures buried in soil or submerged in water which became a problem of worldwide significance and causes most of the deterioration in petroleum industry can be controlled by cathodic protection (CP).CP is a popular technique used to minimize the corrosion of metals in a variety of large structures. To prevent corrosion, voltage between the protection metal and the auxiliary anode has to be controlled on a desired level. In this study two types of controllers will be used to set a pipeline potential at required protection level. The first one is a conventional Proportional-Integral-Derivative (PID) controller and the second are intelligent controllers (fuzzy and neural controllers).The results were simulated and implemented using MATLAB R 2010a program which offers predefined functions to develop PID, fuzzy and neural control systems.