{"title":"利用多元数据分析和机器学习方法预测高炉炼铁过程中的铁水温度","authors":"Arun Kumar, Ashish Agrawal, Ashok Kumar, Sunil Kumar","doi":"10.1051/metal/2023073","DOIUrl":null,"url":null,"abstract":"The feed-forward back propagation neural (FFBPN) network method and multivariate data analysis are used to present a new approach for predicting the health of a blast furnace in the form of hot metal temperature (HMT), which is a crucial parameter to control the stable flow of hot metal production while avoiding major danger incidents during the ironmaking process. The health status also appears to predict the performance level of BF at a premature time, allowing the operator to take necessary steps to avoid BF deterioration. The BF’s health status designates the stability or instability of the BF, which may arise during the manufacturing process of hot molten iron, and is used to find the fault. In this paper, the health status of BF was determined with the help of a FFBPN and correlation matrix. This was done with Matlab (Version 2018Rb) software that uses data pre-processing, variable reduction, and a selective attribute of a data set. The FFBPN model has been trained, tested, and validated, and it has got 96% correlation coefficient of HMT prediction of combination of all data sets. The predicted HMT using several actual process data sets has been helpful in identifying the process irregularity in BF.","PeriodicalId":18527,"journal":{"name":"Metallurgical Research & Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.9000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of hot metal temperature in a blast furnace iron making process using multivariate data analysis and machine learning methodology\",\"authors\":\"Arun Kumar, Ashish Agrawal, Ashok Kumar, Sunil Kumar\",\"doi\":\"10.1051/metal/2023073\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The feed-forward back propagation neural (FFBPN) network method and multivariate data analysis are used to present a new approach for predicting the health of a blast furnace in the form of hot metal temperature (HMT), which is a crucial parameter to control the stable flow of hot metal production while avoiding major danger incidents during the ironmaking process. The health status also appears to predict the performance level of BF at a premature time, allowing the operator to take necessary steps to avoid BF deterioration. The BF’s health status designates the stability or instability of the BF, which may arise during the manufacturing process of hot molten iron, and is used to find the fault. In this paper, the health status of BF was determined with the help of a FFBPN and correlation matrix. This was done with Matlab (Version 2018Rb) software that uses data pre-processing, variable reduction, and a selective attribute of a data set. The FFBPN model has been trained, tested, and validated, and it has got 96% correlation coefficient of HMT prediction of combination of all data sets. The predicted HMT using several actual process data sets has been helpful in identifying the process irregularity in BF.\",\"PeriodicalId\":18527,\"journal\":{\"name\":\"Metallurgical Research & Technology\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metallurgical Research & Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1051/metal/2023073\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METALLURGY & METALLURGICAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metallurgical Research & Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/metal/2023073","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
Prediction of hot metal temperature in a blast furnace iron making process using multivariate data analysis and machine learning methodology
The feed-forward back propagation neural (FFBPN) network method and multivariate data analysis are used to present a new approach for predicting the health of a blast furnace in the form of hot metal temperature (HMT), which is a crucial parameter to control the stable flow of hot metal production while avoiding major danger incidents during the ironmaking process. The health status also appears to predict the performance level of BF at a premature time, allowing the operator to take necessary steps to avoid BF deterioration. The BF’s health status designates the stability or instability of the BF, which may arise during the manufacturing process of hot molten iron, and is used to find the fault. In this paper, the health status of BF was determined with the help of a FFBPN and correlation matrix. This was done with Matlab (Version 2018Rb) software that uses data pre-processing, variable reduction, and a selective attribute of a data set. The FFBPN model has been trained, tested, and validated, and it has got 96% correlation coefficient of HMT prediction of combination of all data sets. The predicted HMT using several actual process data sets has been helpful in identifying the process irregularity in BF.
期刊介绍:
Metallurgical Research and Technology (MRT) is a peer-reviewed bi-monthly journal publishing original high-quality research papers in areas ranging from process metallurgy to metal product properties and applications of ferrous and non-ferrous metals and alloys, including light-metals. It covers also the materials involved in the metal processing as ores, refractories and slags.
The journal is listed in the citation index Web of Science and has an Impact Factor.
It is highly concerned by the technological innovation as a support of the metallurgical industry at a time when it has to tackle severe challenges like energy, raw materials, sustainability, environment... Strengthening and enhancing the dialogue between science and industry is at the heart of the scope of MRT. This is why it welcomes manuscripts focusing on industrial practice, as well as basic metallurgical knowledge or review articles.