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Corrosion associated with microorganisms has been recognized for over 50 years and yet the study of microbiologically influenced corrosion (MIC) is relatively new. MIC can occur in diverse environments. Industrial cooling water from rivers, lakes and sea water contain lots of microorganisms which are able to grow and multiply under certain conditions when pH, water temperature and sunlight etc are suitable. MIC is one of key cause of heat exchanger faults. MIC of heat exchanger materials in cooling waters has caused expensive unplanned outages, the need for local repairs and, in some cases, completes system replacement. Sulfate-reducing bacteria (SRB) are the main harmful bacteria in circulating cooling water. Under anaerobic conditions, SRB reproduce a lot to produce mucus, which speed up the formation of corrosion, erode the metal equipment, plug the pipeline, affect the efficiency of heat transfer, and bring a lot of inconvenience to the production. The corrosion behaviors of 304 stainless steel induced by SRB were studied by measuring the polarization curves, electrochemical impedance Spectrum, weight loss measurements of fore-and-aft biocorrosion, and electrochemical noise method. The electrochemical noise signal of 304 stainless steel corrosion were de-noised by using a wavelet threshold de-noising method, which made the quadratic biorthogonal spline wavelet as the mother wavelet and adopted an soft threshold processing function. The result showed that the slope of cathodic polarization curves measured included with SRB is lower than the one obtained without SRB, while the slope of anodic polarization curves is higher than it. It is concluded that the process of anode polarization was repressed at the presence of SRB. With the growth of the culture time, the value of electrochemical impedance without bacteria reduced at first, then rose, while with bacteria fell at all times. It indicated that SRB accelerated the corrosion of stainless steel. With the dipping time, a biofilm, under which corrosion products congregate to form local battery corrosion, was formed on the surface of stainless steel, so that the serious pitting corrosion is induced. The results from electrochemical noise method showed that the quadratic biorthogonal spline wavelet much smoother and it can remove the noise from the electrochemical noise effectively, and can effectively identify the location of the sudden changes in the signal and accurately reflect the useful information of the signal. The more useful information and data about biocorrosion induced by SRB are also gotten.

The application of wavelet neural network based on Levenberg-Marquardt Optimization to predict heat exchanger fouling is reported in this paper. We construct a 6-6-1 network according to the fouling monitor principle and parameters, the modeling of the wavelet neural network programmed with MATLAB, and trained with Levenberg-Marquarde Optimization algorithm, all training data came from the Automatic Dynamic Simulator of Fouling and input the network after normalized processing and reclassification. Simulations show that the relative error of fouling prediction is less than 0.71 percent, and wavelet neural network can be used to predict heat exchanger fouling, and has the rapid convergence rate and perfect prediction precision, the Levenberg-Marquarde Optimization algorithm can also improve convergence rate of wavelet neural network to a certain extent.

To adopt protective measure and guarantee safe and economic operation for heat exchanger, it's necessary to accurate monitor and evaluate fouling and corrosion of heat exchanger. Point at the imperfection of present method to monitor fouling and corrosion of heat exchanger, A new practical on-line equipment was developed based on the soft-measure technology for fouling with easily measured parameters and the mental corrosion rate monitoring technology with the weak polarization method, and introduces mainly the measuring principle, the structure, the performance and the application of the monitoring equipment based on the above principle.