Thesis (M.S.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains xiii, 93 p. : ill. (some col.), map (part col.). Includes abstract. Includes bibliographical references (p. 91-93).

Thesis (M. Phil.)--University of Hong Kong, 1982.

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

Microbiologically Influenced Corrosion (MIC) is a process influenced by microbial presence and their metabolic activities. This study examined the microbial effects on metal corrosion under different environmental conditions with nutrient supplements. Experiments were conducted by inserting stainless steel 304 and mild steel coupons (2.5 x 2.5 cm²) into loam soil and a simulated seawater/sediment system with various nutrient conditions (sterilized, without supplement, 5 mM NaNO₃ or NH₄NO₃). Two mild and stainless steel coupons were removed monthly and the corrosion rate was evaluated based on the weight loss. Bacterial populations were enumerated using the most probable number (MPN) technique. The presence and adhesion of microbes on mild steel coupons were examined using Scanning Electron Microscopy (SEM). The extent of the corrosion process on the surface of the metal coupons were visualized by using the Stereo Microscope. The elemental composition of the corrosion products formed on the coupon surface were determined by Energy Dispersive X-Ray analyses. Isolation and identification of aerobic microorganisms were conducted and examined for its potential in either accelerating or inhibiting corrosion. The bacterial populations present on the mild steel surface were analyzed by fluorescent in situ hybridization. Denaturing gradient gel electrophoresis (DGGE) analyses of PCR-amplified 16S rDNA fragments were conducted to determine the microbial community complexity of the biofilm. Greater weight losses of mild steel in loam soil and the seawater/sediment system with NaNO₃ (48.86 mg/g and 19.96 mg/g of weight loss, respectively after 20 weeks) were observed with total heterotrophic bacterial population presented (106.695 MPN/ml and 0.11187 MPN/ml respectively) compared to the autoclaved control (7.17845 mg/g and 0.12082 mg/g of weight loss respectively). Supplementation of 5 mM NH₄NO₃ increased the total heterotrophic bacterial population and resulted in a decrease in weight loss measurements on the stainless steel coupons (211.4 MPN/ml with a 0.01 mg/g weight loss) after 20 weeks compared to the non-autoclaved loam soil and loam soil supplemented with NaNO₃ (139.2 MPN/ml and 134.9 MPN/ml respectively with no weight loss). SEM images of the mild steel coupons confirmed the presence and adherence of bacteria on the metal surface. Stereo microscopic images displayed reddish-brown deposits and pitting on the coupon surface. Isolation, identification and sequence analysis revealed that most microorganisms were the Bacillus species. This group of microorganisms are iron-oxidizing bacteria that could also promote the corrosion process. After 20 weeks of incubation, the total SRB cell counts were lower in samples supplemented with NaNO₃ in both loam soil and the seawater/sediment system. This study also indicated that the isolated aerobic microorganisms do play a role in the corrosion process in both stainless and mild steel. DGGE analysis revealed microbial diversity in the corrosion products especially those affiliated to the bacterial phyla Firmicutes and Gamma-Proteobacteria. Fluorescent in situ hybridization analysis allowed for an overall estimation of Eubacteria and sulphate-reducing bacteria present in the biofilm formed on the surface of mild steel. The current study indicates that the addition of nitrates did not significantly reduce the rates of corrosion of both mild and stainless steel. However, it does seem that environmental conditions did pose as an important factor in the corrosion process. Therefore, further studies need to be implemented to analyze the environmental type, microbial composition and optimization of the concentration of nitrates for possible mitigation of metal corrosion. To optimize MIC prevention and control, collaboration between engineers and microbiologists proves advantageous to develop an environmentally sound and potentially cost-effective approach to control corrosion. / Thesis (Ph.D.)-University of KwaZulu-Natal, Westville, 2012.

Neural networks were applied to the analysis of electrochemical noise data. Electrochemical noise is defined as the fluctuations in either current or potential with time for a metal which is immersed in a conductive solution. This data is of interest because of its relationship to particular corrosion processes. Specifically, a system which is experiencing uniform corrosion will produce a different noise signal than one which is experiencing localized (perforation) corrosion. The economic effects of corrosion are significant and methods which improve the ability to detect, measure and predict corrosion would be extremely valuable.Two series of experiments were conducted. The data for both series were collected from aluminum samples immersed in various aqueous solutions. The series differed from each other in the configuration and programming of the potentiostat which collected the data. The first series only dealt with potential noise while the second series dealt with both potential and current noise. Auxiliary parameters, such as the pH and chloride concentration of the solutions were used in the second series. The first series studied data from only two solutions, while the second series included six solutions.It was possible for neural networks to correctly categorize systems in Series 1 according to the class of corrosion being observed (uniform or perforating). Appropriate data transformation steps were required to effect these classifications and it was also observed that many of these data transformations would lead directly to categorization without the use of a neural network.The additional data collected in Series 2 allowed a more complex analysis. Neural networks were able to simultaneously predict both the propensity towards localized corrosion and the metal dissolution rate. This application demonstrated the power of neural networks.Several types of neural networks and learning algorithms were included in this study. The two systems used most were a backpropagation (multi-layer perceptron) and a radial basis system. Comparisons of the various network systems with regard to speed and accuracy were made. / Department of Mathematical Sciences

Thesis (PhD) - Swinburne University of Technology, School of Engineering and Science, 2008. / A thesis submitted for the degree of Doctor of Philosophy, School of Engineering and Science, Swinburne University of Technology, 2008. Typescript. Includes bibliographical references (p. 189-204).

Thesis (M. Eng.)--Carleton University, 1999. / Includes bibliographical references (p. 164-166). Also available in electronic format on the Internet.

Thesis (M. Eng.)--Carleton University, 2000. / Includes bibliographical references (p. 152). Also available in electronic format on the Internet.

Thesis (M. Eng.)--Carleton University, 2004. / Includes bibliographical references (p. 177-183). Also available in electronic format on the Internet.

Thesis (M.S.)--Ohio University, August, 1995. / Title from PDF t.p.