Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors
This study established a circulating system to control the concentration of substances and temperature in the aqueous solution. Simultaneously, sensors were used to continuously monitor the corrosion of three pipe materials: ductile iron (DI), surface-treated ductile iron (SDI), and carbon steel (CS...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785424020817 |
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| _version_ | 1846107845277253632 |
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| author | Bingqin Wang Long Zhao Yongfeng Chen Lingsheng Zhu Chao Liu Xuequn Cheng Xiaogang Li |
| author_facet | Bingqin Wang Long Zhao Yongfeng Chen Lingsheng Zhu Chao Liu Xuequn Cheng Xiaogang Li |
| author_sort | Bingqin Wang |
| collection | DOAJ |
| description | This study established a circulating system to control the concentration of substances and temperature in the aqueous solution. Simultaneously, sensors were used to continuously monitor the corrosion of three pipe materials: ductile iron (DI), surface-treated ductile iron (SDI), and carbon steel (CS). A corrosion decision model based on a machine learning framework was developed for data mining. The results show that the developed model provides accurate corrosion prediction strategies. Analysis revealed that high temperature is the primary factor accelerating corrosion in water systems. SDI accelerates at 60 °C, reaching its peak at 90 °C, while DI and CS peak at 80 °C. The superior corrosion resistance of SDI is attributed to its ability to withstand accelerated corrosion under high temperatures and environmental coupling, making it more stable when immersed in water. |
| format | Article |
| id | doaj-art-4183697ebbf441c6ab550d4bce562f98 |
| institution | Kabale University |
| issn | 2238-7854 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-4183697ebbf441c6ab550d4bce562f982024-12-26T08:53:38ZengElsevierJournal of Materials Research and Technology2238-78542024-11-0133725741Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensorsBingqin Wang0Long Zhao1Yongfeng Chen2Lingsheng Zhu3Chao Liu4Xuequn Cheng5Xiaogang Li6Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China; Key Laboratory for Corrosion and Protection, Ministry of Education, University of Science and Technology Beijing, Beijing, ChinaInstitute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China; Key Laboratory for Corrosion and Protection, Ministry of Education, University of Science and Technology Beijing, Beijing, ChinaR&D Center, National, Xinxing Ductile Iron Pipes Co., Ltd., Handan, HeBei, ChinaR&D Center, National, Xinxing Ductile Iron Pipes Co., Ltd., Handan, HeBei, ChinaInstitute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China; Key Laboratory for Corrosion and Protection, Ministry of Education, University of Science and Technology Beijing, Beijing, China; Corresponding author. Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, ChinaInstitute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China; Key Laboratory for Corrosion and Protection, Ministry of Education, University of Science and Technology Beijing, Beijing, China; Institute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang, 110004, ChinaInstitute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China; Key Laboratory for Corrosion and Protection, Ministry of Education, University of Science and Technology Beijing, Beijing, ChinaThis study established a circulating system to control the concentration of substances and temperature in the aqueous solution. Simultaneously, sensors were used to continuously monitor the corrosion of three pipe materials: ductile iron (DI), surface-treated ductile iron (SDI), and carbon steel (CS). A corrosion decision model based on a machine learning framework was developed for data mining. The results show that the developed model provides accurate corrosion prediction strategies. Analysis revealed that high temperature is the primary factor accelerating corrosion in water systems. SDI accelerates at 60 °C, reaching its peak at 90 °C, while DI and CS peak at 80 °C. The superior corrosion resistance of SDI is attributed to its ability to withstand accelerated corrosion under high temperatures and environmental coupling, making it more stable when immersed in water.http://www.sciencedirect.com/science/article/pii/S2238785424020817Water pipelineMonitoringMachine learningCorrosionBig-data |
| spellingShingle | Bingqin Wang Long Zhao Yongfeng Chen Lingsheng Zhu Chao Liu Xuequn Cheng Xiaogang Li Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors Journal of Materials Research and Technology Water pipeline Monitoring Machine learning Corrosion Big-data |
| title | Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors |
| title_full | Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors |
| title_fullStr | Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors |
| title_full_unstemmed | Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors |
| title_short | Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors |
| title_sort | intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors |
| topic | Water pipeline Monitoring Machine learning Corrosion Big-data |
| url | http://www.sciencedirect.com/science/article/pii/S2238785424020817 |
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