摘要
工业循环冷却水系统中温度适宜、含有各种有机物和无机物,非常适合微生物的生长和繁殖。而微生物的生命活动可直接或间接地对冷却水系统中换热设备的电化学过程产生影响,最终将导致微生物腐蚀。微生物腐蚀可引起冷却水系统中换热设备的腐蚀和粘泥沉积,进而使设备的过水断面减少、传热效率降低,情况严重时可导致设备局部腐蚀、管道穿孔,致使工厂被迫停产,造成相当大的经济损失。
电化学噪声是指在恒电位(或恒电流)的控制下,在电解液中通过金属电极/电解质界面的电流(或电极电位)的自发波动。这种噪声来自于电化学系统本身,不会对微生物的生长和繁殖产生干扰,是一种非常有利于研究微生物腐蚀的测量技术。
本文采用电化学噪声技术监测硫酸盐还原菌诱导304不锈钢腐蚀的过程。将304不锈钢分别浸泡在无菌和有菌培养基中29天,每天对其进行四次电化学噪声测试,得到电化学噪声原始数据。因电化学噪声测量得到的信号包含有直流分量,其对信号的分析有很大的影响,所以选取多项式拟合法去除其直流漂移。通过对去除直流漂移后的噪声数据进行时域、频域和小波分析结合金相显微镜观察结果将腐蚀过程分为四个阶段:钝化、点蚀诱导期、点蚀和均匀腐蚀。针对电化学噪声的传统分析方法滞后性的缺点,本文对基于希尔伯特黄变换和误差后向传播网络智能识别微生物腐蚀过程中的不同阶段的方法可行性进行了研究。研究结果表明:利用希尔伯特黄变换进行特征提取,能很好表征腐蚀过程中的不同阶段,误差后向传播网络智能识别对不锈钢钝化、点蚀诱导期和点蚀这三种腐蚀阶段具有很好的识别效果,对均匀腐蚀这个腐蚀阶段的识别效果还有待提高。本文对在线监测的电化学噪声数据的实时智能分析提供了一种可行性方法,希望对实际中智能识别腐蚀过程的不同阶段以便及时采取防治措施提供理论依据。
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.
引文
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