{"title":"利用机器学习和电化学传感器对球墨铸铁管系统进行智能腐蚀分析和寿命预测","authors":"","doi":"10.1016/j.jmrt.2024.09.076","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":54332,"journal":{"name":"Journal of Materials Research and Technology-Jmr&t","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2238785424020817/pdfft?md5=94298bacde3db3992e78479e3255f07f&pid=1-s2.0-S2238785424020817-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors\",\"authors\":\"\",\"doi\":\"10.1016/j.jmrt.2024.09.076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":54332,\"journal\":{\"name\":\"Journal of Materials Research and Technology-Jmr&t\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2238785424020817/pdfft?md5=94298bacde3db3992e78479e3255f07f&pid=1-s2.0-S2238785424020817-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Materials Research and Technology-Jmr&t\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2238785424020817\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Materials Research and Technology-Jmr&t","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2238785424020817","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
这项研究建立了一个循环系统来控制水溶液中的物质浓度和温度。同时,使用传感器连续监测三种管道材料的腐蚀情况:球墨铸铁(DI)、表面处理球墨铸铁(SDI)和碳钢(CS)。为进行数据挖掘,开发了一个基于机器学习框架的腐蚀决策模型。结果表明,所开发的模型提供了准确的腐蚀预测策略。分析表明,高温是加速水系统腐蚀的主要因素。SDI 在 60 °C 时加速腐蚀,在 90 °C 时达到峰值,而 DI 和 CS 在 80 °C 时达到峰值。SDI 的优异耐腐蚀性归功于其在高温和环境耦合条件下承受加速腐蚀的能力,使其在浸入水中时更加稳定。
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). 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.
期刊介绍:
The Journal of Materials Research and Technology is a publication of ABM - Brazilian Metallurgical, Materials and Mining Association - and publishes four issues per year also with a free version online (www.jmrt.com.br). The journal provides an international medium for the publication of theoretical and experimental studies related to Metallurgy, Materials and Minerals research and technology. Appropriate submissions to the Journal of Materials Research and Technology should include scientific and/or engineering factors which affect processes and products in the Metallurgy, Materials and Mining areas.