Monitoring of carbon steel corrosion by use of electrochemical noise and recurrence quantification analysis (original) (raw)
Related papers
Universiti Teknologi MARA, Sabah, 2017
In this study, the electrochemical noise (ECN) measurement technique was utilized to identify the uniform corrosion of carbon steels in the solutions of hydrochloric (HCl), sulphuric (H2SO4), sodium chloride (NaCl), sodium hydroxide (NaOH), potassium hydroxide (KOH) and calcium hydroxide (Ca(OH)2). ECN is the fluctuations of current and potential of a corroding system. The potential of electrochemical noise was measured between a working electrode and a reference electrode whereas the current of electrochemical noise was measured between two working electrodes in an electrochemical cell. The data from the measurements were statistically analyzed using time and frequency domains. The time domain analysis reveals the characteristics of a particular corrosion type. The frequency domain analysis estimates the power spectra at various frequencies and the statistical analysis calculates electrochemical noise parameters such as the mean, standard deviation, noise resistance (RN), coefficient of variation (CoV), characteristic charge (q) and corrosion current (Icorr). The results of the time domain analysis show that uniform corrosion only occurred in acidic solutions of HCl and H2SO4. The frequency domain analysis was found to be an unsuitable method to identify uniform corrosion in the corrosion system used. The statistical analysis shows that the corrosion rate was greater when using the solutions of HCl and H2SO4 compared to NaCl, NaOH, KOH and Ca (OH)2.
Artificial neural network for the evaluation of CO2 corrosion in a pipeline steel
Journal of Solid State Electrochemistry, 2009
This paper presents a predictive model for the determination of different types of corrosion by using electrochemical impedance spectroscopy curves and artificial neural network. This proposed model obtains predictions for three different types of corrosion by using Nyquist impedance curves from four input variables: inhibitor concentration, time of exposure, and the real and imaginary experimental component of these curves. The model takes into account the variations of inhibitor concentration over steel to decrease the corrosion rate. For the network, the Levenberg-Marquardt learning algorithm, the hyperbolic tangent sigmoid transfer function and the linear transfer function were used. The best fitting training data set was obtained with five neurons in the hidden layer, which made possible to predict satisfactory efficiency (R>0.99). On the validation of the data set, simulations and theoretical data tests were in good agreement (R>0.9905). The developed model can be used for the determination of the type of curves related to the nature phenomena and rate of corrosion at the metal surface.
Monitoring Pitting Corrosion of Carbon Steel Using the Combined WBE-Noise Signatures Method
2006
An electrochemically integrated multi-electrode array namely the wire beam electrode (WBE) in combination with noise signatures analysis has been applied to study localized corrosion, especially pitting. The classic pitting corrosion of carbon steel in Evans solution was carried out by the correlation of electrochemical potential noise signatures and WBE current distribution maps. During carbon steel pitting corrosion process, the characteristic ‘peak’ of rapid potential transient, towards less negative direction, followed by recovery was found to correlate with the disappearance of unstable anodes leading to formation and propagation of stable anodes in WBE current distribution maps. Localized corrosion was the result of the anodic dissolution of the remaining anodic sites after disappearance of unstable anodes. This result suggests that the combined WBE-noise signatures method could be applied as a means of early detection and prediction of localized corrosion.
Measures for the detection of localized corrosion with electrochemical noise
Electrochimica Acta, 2001
A simulation of electrochemical noise data has been produced using a shot noise model, and this has been used to examine the properties of several of the parameters that have been proposed as indicative of the type of corrosion. The model produces an electrochemical noise impedance that is the same as the expected impedance, despite that fact that the model does not incorporate a charge transfer resistance term, supporting the observed and predicted equivalence between noise impedance and conventional electrochemical impedance. Of the various parameters that have been examined, the characteristic charge and characteristic frequency are proposed as useful general indicators of the nature of the corrosion process. Skew and kurtosis statistics may be indicative of the localized corrosion, but the results will be system dependent, particularly with respect to whether uni-or bidirectional transients are observed, and whether the current measuring electrodes are symmetrical or asymmetrical.
Electrochemical Noise Technique for Corrosion Assessment - a Review
Corrosion Reviews, 2005
This review details the electrochemical noise technique as applicable to corrosion assessment with emphasis on the fundamental principles, theoretical aspects, data analysis and applications. The use of this technique in understanding the mechanistic aspects of corrosion and corrosion monitoring has been discussed. The important applications of this technique in corrosion processes like uniform corrosion, localized corrosion (pitting, crevice corrosion and stress corrosion cracking), and evaluation of coatings are highlighted. Miscellaneous applications are also explained.
Neural network methods for corrosion data reduction
Materials & Design, 1999
Neural network methods have been used by a number of workers to model corrosion behaviour. These attempts have generally been regarded as successful, although few have really been able to demonstrate that the neural network model describes the underlying corrosion problem accurately. The application of neural network methods to model the pitting corrosion behaviour of a stainless steel as a function of solution composition and temperature is presented. The predictions of the neural network exhibit reasonable correlation with data for simple one-and two-component solutions, although the problem of testing the generality of the neural network solution remains unsolved. In addition to the basic testing of the neural network performance, there are problems of variability of corrosion behaviour and the unpredictable behaviour of the neural network that may occur in regions of the problem domain where no data are available. The use of simulated data to test the neural network method in conditions similar to those being modelled is suggested as one method of obtaining a better assurance of the applicability and performance of the method. ᮊ
International Journal of Modeling and Optimization, 2012
Abstract-There are many methods for inspecting corrosion interaction in objects. Some of these methods are known as the Non-Destructive Test (NDT) which uses a successful approach to monitor materials without harming the objects. In this paper, corrosion degradation is modeled and simulated by using equivalent electrical circuit. The components of the designed equivalent circuit contain resistances and coils related to the condition of the wire used in the Pre-stressed Concrete Cylinder Pipe (PCCP). The change of the resistance value, self and mutual inductance due to the certain wire loop is related to the change of the wire diameter which changes due to the corrosion. These changed factors change the pattern and the value of the exciter current. The fault resulted by simulated model is inspected using Artificial Neural Networks (ANN) through monitoring of the exciter current changes. PCCP is modeled as equivalent electrical circuit based on Dave's model which was developed in Queen University. Different techniques of calculations of the model components were used in the work which gives better results and different view than the one given by Dave. The simulation of proposed approach of this work gave a good solution to the corrosion degradation that related to the number of broken wires in PCCP. That can be modeled and can be detected by using ANN system for monitoring of the exciter current. The system is able to detect the severity of the fault by finding the approximate number of broken wires and their location in the tube. In this work, MATLAB as a technical scientific language were used to build the simulated system and to monitor it using a proper designed ANN system. The study shows that a simple method for modeling the data of corrosion the wires of PCCP and the possibility of monitoring these changing by monitoring the change of the exciter parameters values changes. The method shown in this study makes it possible to simulate the PCCP for different condition ranging from perfect case to sever defected cases. Many patterns were generated by simulating the model at different conditions of the pipe, then recognized as perfect case (no
Electrochemical noise measurement technique in corrosion research
Electrochemical noise measurement is one of the novel techniques currently being used in corrosion monitoring. Two major methods of analysis in use are the Fast Fourier Transform (FFT) and the Maximum Entropy Method (MEM). This paper reviews the techniques fundamental backgroundtypes of noise, physical data; description, classification and characteristics; mathematical background of random data and spectral analysis. Recent progress made in its application to corrosion monitoring and other electrochemical reaction phenomena are also examined.
Trend-removal in Corrosion Processes using Neural Networks
2006
Lifetime evaluation of metallic components is one of the main subjects for many industries due to its importance for development of metal protection and conservation methods. Looking forward this information, several electrochemical techniques have been applied by practitioners. One of the most important is the so called Electrochemical Noise Study, which makes possible to measure potential or real fluctuations produced by kinetic variations along the corrosion process. This technique makes necessary to apply signal processing methods, including low frequency trend removal. Many statistical methods have been proposed to do so. In order to assess each method performance, it is a must to know exactly both trend and noise for a particular signal. Then, a statistical comparison can be carried out between noise extracted with different methods and real noise. With this purpose, sometimes signal is simulated by computer data generation. In this paper, a new approach is proposed for trend removal, using artificial intelligence techniques instead of statistical methods. With this purpose, we combined an interval signal processing with backpropagation neural networks. Data pre-processing, used topologies, optimizations and training process are exposed in detail. Results are analysed and conclusions drawn.