{"title":"Machine-learning optimization of 3D-printed flow-reactor geometry","authors":"Jeffrey A. Bennett, Milad Abolhasani","doi":"10.1038/s44286-024-00095-5","DOIUrl":null,"url":null,"abstract":"The geometric design space of continuous flow reactors for optimal process intensification is prohibitively large for a comprehensive search, but incorporation of multi-fidelity optimization techniques using computer simulations and additive manufacturing can rapidly improve reactor performance.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Chemical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44286-024-00095-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The geometric design space of continuous flow reactors for optimal process intensification is prohibitively large for a comprehensive search, but incorporation of multi-fidelity optimization techniques using computer simulations and additive manufacturing can rapidly improve reactor performance.
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