The Corrosion Prediction from the Corrosion Product Performance

Subhiksha Gopinath, CSIR-Central Electrochemical Research Institute

Introduction

Carbon dioxide (CO₂) Corrosion of carbon steel is a major threat to the gas and oil pipelines and industries. It is necessary to predict the corrosion rate occurring in the pipelines to avoid the destruction of material and develop the coatings that prevent corrosion. This summary is about preventing Co2 corrosion using corrosion products. Several Mechanistic models were developed ages ago to compare the corrosion product performance in protecting the material.

The basic types of models are Empirical, Semiempirical and Mechanistic models. A Mechanistic model (Co2 corrosion prediction model) was developed recently to study the rate of corrosion and protection offered by the corrosion product FeCo3 scales. This Mechanistic model deals with chemistry, electrochemistry and mass transfer processes.

These protection mechanisms include Hinderance (Barrier effect), Blocking (Coverage effect) and Transportation. The elementary mechanistic model is developed with transportation and hindrance effects and a comprehensive mechanistic model is developed with Blocking effect additional to it.

The main parameters to study the protecting mechanism of corrosion product scales is its thickness and porosity. The parameters mentioned above are studied using the µCT technique. Surface blocking is the main cause of corrosion reduction rates.

*Schema depicting the CO₂ corrosion mechanistic model by Wang et al. 2023*

Development of Corrosion prevention model

Experimental Procedure

Material Preparation: The material chosen for the model is X65 carbon steel and it had taken in the cylinder form for the µCT analysis with the required dimensions welded with copper wire, mounted to the resin. The final material is wet polished with silicon carbide paper and dried with cold air after cleaning with distilled water, ethanol and acetone.

Electrochemical measurements: The material is kept in an in situ- high temperature and pressure environment and autoclave equipped with 3 electrode systems. Polarization curves are obtained using Ivium Potentiostat. The CV measurements are done by sweeping potential at the scan rate 0.5mV/s. The P^H calculations are done by the PHREEQC software. The test solution was NaCl with NaHCo3 and it is deaerated with Co2, 12 hours before the experiment.

Surface Analysis: The morphology of the corrosion product scales had been seen through the Carl Zeiss EVO MA15 scanning electron microscope and XRD was used to study the composition of the product scales.

µCT measurements: The thickness and porosity of the FeCo3 scales are analyzed by the µCT measurement using X Radio520 versa scan machine and 3D visualization was made using AVISO software. Calculations were done with the help of MATLAB software. Ferrous ion measurements are also done by monitoring the Fe contents in bulk solution using ICP-OES (Inductively Coupled Plasma Optical Emission Spectroscopy.

Results and Discussions:

The inference from the experiments performed above was about the morphology, composition, 3D structures, thickness distribution, potentiodynamic polarization after immersion periods. The CO2 corrosion prediction model was compared with the experimental procedure to study the different corrosion rate at different scale thickness with constant porosity or vice versa. The corrosion highly depends on the scale thickness of the corrosion product and porosity of scales. The thicker the scales, the lesser corrosion rate of the material. The more the porosity of the scale, the greater the corrosion rate.

Conclusions:

Thus, we infer that corrosion product protection ability is studied by establishing a model and comparing it with the experiment where thickness and porosity of FeCO3 scales. The coupling effect of thickness and porosity is very important in the analysis of the corrosion prediction model. The pore distribution of the scales is also one of the critical factors as its loose outer layer and inner dense layers contribute to the protection capability.

Also read: Nitrogen Resilience in Waterlogged Soybean plants

Reference:

Cailin Wang , Xiusai Xu , Cuiwei Liu , Xiaoming Luo , Qihui Hu , Rui Zhang , Hongda Guo , Xia Luo , Yong Hua , Yuxing Li (April 2023). Improvement on the CO₂ corrosion prediction via considering the corrosion product performance. Corrosion Science (vol 217). Elsevier. https://doi.org/10.1016/j.corsci.2023.111127