{"title":"为工艺优化开发聚合反应的数字孪生体","authors":"Balazs Bordas, Kutup Kurt, A. Bamberg, S. Engell","doi":"10.1109/asmc54647.2022.9792518","DOIUrl":null,"url":null,"abstract":"The application performance of polymers is heavily influenced by their molecular weight distribution. As the dynamic modeling of the MWD in polymerization reactions is difficult based on first principles, a semi-physical or grey-box modeling approach is proposed for step-growth polymerization processes, utilizing molecular weight distribution measurement data. The method is tested on data from an industrial polymerization reactor.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Developing a digital twin of a polymerization reaction for process optimization\",\"authors\":\"Balazs Bordas, Kutup Kurt, A. Bamberg, S. Engell\",\"doi\":\"10.1109/asmc54647.2022.9792518\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The application performance of polymers is heavily influenced by their molecular weight distribution. As the dynamic modeling of the MWD in polymerization reactions is difficult based on first principles, a semi-physical or grey-box modeling approach is proposed for step-growth polymerization processes, utilizing molecular weight distribution measurement data. The method is tested on data from an industrial polymerization reactor.\",\"PeriodicalId\":436890,\"journal\":{\"name\":\"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/asmc54647.2022.9792518\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/asmc54647.2022.9792518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Developing a digital twin of a polymerization reaction for process optimization
The application performance of polymers is heavily influenced by their molecular weight distribution. As the dynamic modeling of the MWD in polymerization reactions is difficult based on first principles, a semi-physical or grey-box modeling approach is proposed for step-growth polymerization processes, utilizing molecular weight distribution measurement data. The method is tested on data from an industrial polymerization reactor.