Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis
In this study, the corrosion of carbon steel immersed in CO 2 saturated aqueous solutions, in the presence and absence of sand deposits, were investigated by electrochemical noise measurement and recurrence quantification analysis. Uniform corrosion occurred at samples without sand deposit, while lo...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Pergamon
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/67641 |
| _version_ | 1848761619549519872 |
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| author | Hou, Y. Aldrich, Chris Lepkova, Katerina Kinsella, Brian |
| author_facet | Hou, Y. Aldrich, Chris Lepkova, Katerina Kinsella, Brian |
| author_sort | Hou, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this study, the corrosion of carbon steel immersed in CO 2 saturated aqueous solutions, in the presence and absence of sand deposits, were investigated by electrochemical noise measurement and recurrence quantification analysis. Uniform corrosion occurred at samples without sand deposit, while localized corrosion took place at the sand-covered steel samples. These two different corrosion types can be accurately predicted by random forest and principal component models based on recurrence quantification analysis of either electrochemical potential or current noise data, regardless of threshold values. The study provides a potential automated online corrosion monitoring scheme to ensure the integrity of pipelines. |
| first_indexed | 2025-11-14T10:34:33Z |
| format | Journal Article |
| id | curtin-20.500.11937-67641 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:34:33Z |
| publishDate | 2018 |
| publisher | Pergamon |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-676412018-10-11T00:54:45Z Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis Hou, Y. Aldrich, Chris Lepkova, Katerina Kinsella, Brian In this study, the corrosion of carbon steel immersed in CO 2 saturated aqueous solutions, in the presence and absence of sand deposits, were investigated by electrochemical noise measurement and recurrence quantification analysis. Uniform corrosion occurred at samples without sand deposit, while localized corrosion took place at the sand-covered steel samples. These two different corrosion types can be accurately predicted by random forest and principal component models based on recurrence quantification analysis of either electrochemical potential or current noise data, regardless of threshold values. The study provides a potential automated online corrosion monitoring scheme to ensure the integrity of pipelines. 2018 Journal Article http://hdl.handle.net/20.500.11937/67641 10.1016/j.electacta.2018.04.037 Pergamon restricted |
| spellingShingle | Hou, Y. Aldrich, Chris Lepkova, Katerina Kinsella, Brian Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis |
| title | Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis |
| title_full | Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis |
| title_fullStr | Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis |
| title_full_unstemmed | Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis |
| title_short | Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis |
| title_sort | detection of under deposit corrosion in a co2 environment by using electrochemical noise and recurrence quantification analysis |
| url | http://hdl.handle.net/20.500.11937/67641 |