Corrosion of the piping system is a genuine problem in the oil and gas industry. Most oil and gas industries used a carbon steel pipeline for the transportation of crude oil, which is affected by CO 2 corrosion. Now a day, the computational approach and artificial neural network approach will be used to study the corrosion rate. Therefore, in this work, Computational Fluid Dynamics (CFD) and Artificial Neural Network (ANN) studies on piping systems were made to determine the corrosion rate induced by CO 2 saturated aqueous solutions on carbon steel pipeline. In CFD study, corrosion rates were computed by modeling the electrochemical processes occurring at the metal substrate from cathodic reductions of the carbonic acid and hydrogen ions, and the anodic oxidation of the metal component. Also, an artificial neural network study was made using a multilayer perceptron neural network method; and, computational fluid dynamics and artificial neural network simulations were validated with in-house built experiment set-up. The experimental study had been carried out for more than 200-h to find the corrosion rate on the pipeline, and satisfactory trends were observed between computational fluid dynamics, artificial neural network, and experimental values. In the end, corroded pipes were observed under a scanning electron microscope and x-ray spectroscopy, and the corroded zones were viewed as against the non-corroded pipe.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.