The Application Of Electrochemical Noise Analysis In Microbial-induced Corrosion

The suitable temperature and a variety of organic and inorganic substances inindustrial cooling water system are ideal for the growth and reproduction ofmicroorganism. The microbial life activities may directly or indirectly impact theelectrochemical process of the heat exchanger in the cooling water system, eventuallyleading to microbiologically influenced corrosion. Microbiologically influencedcorrosion can cause corrosion and sticky mud deposition of heat transfer equipment,thereby reduce cross section and heat transfer efficiency of the device. In seriouscases, it can lead to localized corrosion of equipment and perforation of pipes. Finally,the factory was forced to suspend production, resulting in considerable economiclosses.Electrochemical noise refers to the spontaneous fluctuations of the current (orelectrode potential) through the metal electrode/electrolyte interface in electrolyteunder the control of constant potential (or constant current). The noise arises from theelectrochemical system itself, and it can not affect the microbial growth andreproduction. So, electrochemical noise is a very beneficial technique on microbialcorrosion measurement.In this paper, electrochemical noise technology is used for monitoring thecorrosion of304stainless steel induced by sulfate-reducing bacteria. The304stainless steel was immersed in sterile medium and the culture medium containingSRB for29days, and the process of corrosion was tested four times a day. So, theoriginal data of electrochemical noise was got. The electrochemical noise signalcontains DC component, and it has a great impact on the analysis of the signal. So,the polynomial fitting was used for removing the DC drift. The noise data wereanalyzed by time domain, frequency domain and wavelet analysis combined with theobservations of optical microscope. And the corrosion was divided into four stages:passivation, pitting induction period, pitting and uniform corrosion. The traditional method for electrochemical noise analysis has lag shortcomings, so the feasibilitystudy on Hilbert-huang Transform and Error-Backpropagation Network on intelligentrecognition method for the different stages of microbiologically influenced corrosionwas conducted. The results showed that the use of Hilbert-huang Transform forfeature extraction can characterize the different stages of corrosion;Error-Backpropagation Network could identify passivation, pitting induction periodand pitting correctly, and recognition effect for uniform corrosion would be improved.A feasible way of analyzing electrochemical noise data real-time and intelligent wasprovided on this paper, and it was hoped that the analyzing method could providetheoretical basis in the identification of the different stages of corrosion in practice totake preventive measures timely.