ABSTRACT
The corrosion process of carbon steel in the presence of silica sand deposit in chloride- containing solution at 30 °C was monitored by use of electrochemical noise (EN). Noise resistance calculated from EN was compared with the polarization resistance obtained from conventional corrosion monitoring techniques, such as linear polarization resistance (LPR) and electrochemical impedance spectroscopy (EIS). Recurrence quantification analysis (RQA) was employed to characterize the current noise data associated with different corrosion types and the extracted variables were used for UDC process monitoring. After tests, the corroded steel surfaces were examined using a 3D profilometry to gather information about localized defects. The results demonstrated that electrochemical noise associated with recurrence quantification analysis is a useful tool for monitoring localized corrosion of under deposit corrosion.
INTRODUCTION
It is well know that the presence of accumulated sand, debris, biofilm and carbonate species on the bottom of oil and gas pipelines can cause severe localized corrosion, viz. under deposit corrosion (UDC). Monitoring of corrosion processes under deposits could provide valuable information for the development of mitigation programs. However, since UDC often occurs in the form of pitting 1-3, the conventional electrochemical methods such as linear polarization resistance (LPR), electrochemical impedance spectroscopy (EIS) and electrical resistance (ER) may not be particularly useful for early detection and continuous monitoring of localized corrosion, due to the negligible change in resistances associated with pitting. In contrast, methods based on electrochemical noise have been reported as promising indicators of localized corrosion. 4-7
Electrochemical noise (EN) can be ascribed to the formation of micro-cells on the surfaces of metals subject to corrosion. These microcells give rise to oscillating current and potentials that contain important information on the dynamics of the corrosion process. 8 The analysis of EN data relies on two main aspects - determination of electrochemical noise resistance (Rn) and identification of various corrosion types. 9 The electrochemical noise resistance Rn is commonly determined as the standard deviation of potential divided by the standard deviation of current, and it is considered to be equivalent to the polarization resistance. Therefore, it can be used for estimating the general corrosion rate. The derivation of information regarding the corrosion types from EN measurement has attracted even more attention. A number of analytical approaches have been proposed, including fast Fourier transforms, maximum entropy methods based power spectral density analysis 10, wavelet transforms 11, transient analysis 5,12,13, chaos analysis 14-17 and recurrence quantification analysis 18-22
