Currently, sulfate-reducing bacteria (SRB) is regarded as the main culprit of microbiologically influenced corrosion (MIC), mainly due to the low reported corrosion rates of other microorganisms. For example, the highest reported corrosion rate for methanogens is 0.065 mm/yr. However, by investigating methanogen-induced microbiologically influenced corrosion (Mi-MIC) using an in-house developed versatile multiport flow test column, extremely high corrosion rates were observed. We analyzed a large set of carbon steel beads, which were sectionally embedded into the test columns as substrates for iron-utilizing methanogen Methanobacterium IM1. After 14 days of operation using glass beads as fillers for section separation, the highest average corrosion rate of Methanobacterium IM1 was 0.2 mm/yr, which doubled that of Desulfovibrio ferrophilus IS5 and Desulfovibrio alaskensis 16109 investigated at the same conditions. At the most corroded region, nearly 80% of the beads lost 1% of their initial weight (fast-corrosion), resulting in an average corrosion rate of 0.2 mm/yr for Methanobacterium IM1-treated columns. When sand was used as filler material to mimic sediment conditions, average corrosion rates for Methanobacterium IM1 increased to 0.3 mm/yr (maximum 0.52 mm/yr) with over 83% of the beads having corrosion rates above 0.3 mm/yr. Scanning electron images of metal coupons extracted from the column showed methanogenic cells were clustered close to the metal surface. Methanobacterium IM1 is a hydrogenotrophic methanogen with higher affinity to metal than H2. Unlike SRB, Methanobacterium IM1 is not restricted to the availability of sulfate concentration in the environment. Thus, the use of the multiport flow column provided a new insight on the corrosion potential of methanogens, particularly in dynamic conditions, that offers new opportunities for monitoring and development of mitigation strategies. Overall, this study shows (1) under certain conditions methanogenic archaea can cause higher corrosion than SRB, (2) specific quantifications, i.e., maximum, average, and minimum corrosion rates can be determined, and (3) that spatial statistical evaluations of MIC can be carried out.
Keywords: biocorrosion; flow system; methane; methanogen; microbiologically influenced corrosion; modeling; multiport.
Copyright © 2020 An, Kleinbub, Ozcan and Koerdt.