Semantic Scholar Open Access 2026

Microbially Induced Corrosion: A Hidden Risk in Industrial Accidents

T. Vítěz Luboš Kotek M. Černý N. Hanišáková M. Vítězová +1 lainnya

Abstrak

The phenomenon of microbiologically induced corrosion (MIC) has been known for more than 100 years, but its role as a cause of accidents in the process industry is still somewhat underestimated. Microbiologically induced corrosion is often associated with anaerobic environments, where microorganisms such as sulfate-reducing bacteria or methanogenic archaea are significantly involved in accelerating corrosion of metallic materials. This study aims to assess the significance of microbiologically induced corrosion in industrial accidents, identify facilities and conditions most at risk, and propose effective prevention strategies. Analysis of publicly available accident databases, together with expert assessment of operating conditions, suggests that MIC might be associated with roughly 10–20% of corrosion-related incidents; however, this range should be interpreted cautiously due to substantial limitations in reporting and confirmation practices. The presence of anaerobic microorganisms and biofilms substantially increases the likelihood of MIC, underscoring the need for systematic monitoring of microbial communities and environmental conditions to improve preventive measures and enhance plant safety. However, the analysis is limited by the incomplete and heterogeneous reporting of corrosion mechanisms in publicly accessible accident databases. Consequently, the estimated contribution of MIC should be interpreted with caution, as many events lack sufficient microbiological or environmental detail to allow definitive classification.

Penulis (6)

T

T. Vítěz

L

Luboš Kotek

M

M. Černý

N

N. Hanišáková

M

M. Vítězová

P

P. Trávníček

Format Sitasi

Vítěz, T., Kotek, L., Černý, M., Hanišáková, N., Vítězová, M., Trávníček, P. (2026). Microbially Induced Corrosion: A Hidden Risk in Industrial Accidents. https://doi.org/10.1021/acsomega.5c09790

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Informasi Jurnal
Tahun Terbit
2026
Bahasa
en
Sumber Database
Semantic Scholar
DOI
10.1021/acsomega.5c09790
Akses
Open Access ✓