{"title":"释放生物质的价值:机器学习对棉秆还原催化分馏的启示","authors":"","doi":"10.1016/j.jics.2024.101394","DOIUrl":null,"url":null,"abstract":"<div><div>Lignocellulosic biomass valorization has become an intensive area of research due to the importance of renewable nature and availability of biomass. However, biomass fractionation and depolymerization produce numerous datasets, that are difficult to visualise and interpret for the scale-up of the process. Therefore, machine learning algorithms, which can discover hidden patterns in data are applied to these datasets. Reductive Catalytic Fractionation (RCF) of lignocellulosic biomass is an emerging methodology to valorize biomass completely and effectively. Herein, the present work includes the Correlation Analysis and the Principal Component Analysis (PCA) of product distribution obtained from RCF of cotton stalks. Interactions between process variables and delignification (DL), sugar retention (SR), total phenolic monomers (PM), and individual phenolic monomers yield were evaluated. Correlations among DL, SR, and PM yields were also evaluated at different reaction conditions through PCA, which were explained using the reaction mechanism and molecular chemistry of lignin.</div></div>","PeriodicalId":17276,"journal":{"name":"Journal of the Indian Chemical Society","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unlocking biomass valorization: Machine Learning insights for Reductive Catalytic Fractionation of cotton stalks\",\"authors\":\"\",\"doi\":\"10.1016/j.jics.2024.101394\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Lignocellulosic biomass valorization has become an intensive area of research due to the importance of renewable nature and availability of biomass. However, biomass fractionation and depolymerization produce numerous datasets, that are difficult to visualise and interpret for the scale-up of the process. Therefore, machine learning algorithms, which can discover hidden patterns in data are applied to these datasets. Reductive Catalytic Fractionation (RCF) of lignocellulosic biomass is an emerging methodology to valorize biomass completely and effectively. Herein, the present work includes the Correlation Analysis and the Principal Component Analysis (PCA) of product distribution obtained from RCF of cotton stalks. Interactions between process variables and delignification (DL), sugar retention (SR), total phenolic monomers (PM), and individual phenolic monomers yield were evaluated. Correlations among DL, SR, and PM yields were also evaluated at different reaction conditions through PCA, which were explained using the reaction mechanism and molecular chemistry of lignin.</div></div>\",\"PeriodicalId\":17276,\"journal\":{\"name\":\"Journal of the Indian Chemical Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Indian Chemical Society\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0019452224002747\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Indian Chemical Society","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019452224002747","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Unlocking biomass valorization: Machine Learning insights for Reductive Catalytic Fractionation of cotton stalks
Lignocellulosic biomass valorization has become an intensive area of research due to the importance of renewable nature and availability of biomass. However, biomass fractionation and depolymerization produce numerous datasets, that are difficult to visualise and interpret for the scale-up of the process. Therefore, machine learning algorithms, which can discover hidden patterns in data are applied to these datasets. Reductive Catalytic Fractionation (RCF) of lignocellulosic biomass is an emerging methodology to valorize biomass completely and effectively. Herein, the present work includes the Correlation Analysis and the Principal Component Analysis (PCA) of product distribution obtained from RCF of cotton stalks. Interactions between process variables and delignification (DL), sugar retention (SR), total phenolic monomers (PM), and individual phenolic monomers yield were evaluated. Correlations among DL, SR, and PM yields were also evaluated at different reaction conditions through PCA, which were explained using the reaction mechanism and molecular chemistry of lignin.
期刊介绍:
The Journal of the Indian Chemical Society publishes original, fundamental, theorical, experimental research work of highest quality in all areas of chemistry, biochemistry, medicinal chemistry, electrochemistry, agrochemistry, chemical engineering and technology, food chemistry, environmental chemistry, etc.