{"title":"Endpoint Phosphorus Content Prediction in BOF Steelmaking Using PCA-Based Support Vector Regression","authors":"S. Mukherjee, S. Barui, K. Chattopadhyay","doi":"10.33313/tr/0422","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":39956,"journal":{"name":"Iron and Steel Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iron and Steel Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33313/tr/0422","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Materials Science","Score":null,"Total":0}
期刊介绍：
Iron & Steel Technology is the premier technical journal for metallurgical, engineering, operating and maintenance personnel in the North American iron and steel industry. As the official monthly publication of AIST, Iron & Steel Technology is the most comprehensive and widely circulated journal available today, dedicated to providing its readers with the latest information on breakthroughs and trends in equipment, processes and operating practices in the international iron and steelmaking industry.
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