{"title":"使用铜基催化剂预测 CO2 加氢反应中甲醇产率的硅学模型","authors":"Vanjari Pallavi, Reddi Kamesh, K. Yamuna Rani","doi":"10.1007/s10562-024-04800-0","DOIUrl":null,"url":null,"abstract":"<p>CO<sub>2</sub> hydrogenation to methanol is instrumental in mitigating carbon emissions and providing a renewable source of clean fuel, methanol. Though Cu-based catalysts proved to be economical and efficient catalysts for this reaction, it has the disadvantage of low catalyst efficiency and sintering. In this study, we developed different six machine learning (ML) models for the prediction of methanol yield (%) from CO<sub>2</sub> hydrogenation for Cu-based catalysts. The gradient boost random trees model outperformed other ML models with accuracy R<sup>2</sup> and RMSE of 0.96, 0.71 on train data and 0.75, 1.75 on test data. Pressure, metal:support ratio, active metal composition, GHSV and reaction temperature were found to be influential parameters for optimization of methanol yield. The prediction capability of this model is also validated based on unseen experimental data with varied input parameters and the predictions are good enough with R<sup>2</sup> and RMSE of 0.9 and 1.14. Therefore, this model can be regarded as a valuable solution to guide experimental design without actual experimentation for Cu-based catalysts.</p>","PeriodicalId":508,"journal":{"name":"Catalysis Letters","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"In Silico Models for Prediction of Methanol Yield in CO2 Hydrogenation Reaction Using Cu-Based Catalysts\",\"authors\":\"Vanjari Pallavi, Reddi Kamesh, K. Yamuna Rani\",\"doi\":\"10.1007/s10562-024-04800-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>CO<sub>2</sub> hydrogenation to methanol is instrumental in mitigating carbon emissions and providing a renewable source of clean fuel, methanol. Though Cu-based catalysts proved to be economical and efficient catalysts for this reaction, it has the disadvantage of low catalyst efficiency and sintering. In this study, we developed different six machine learning (ML) models for the prediction of methanol yield (%) from CO<sub>2</sub> hydrogenation for Cu-based catalysts. The gradient boost random trees model outperformed other ML models with accuracy R<sup>2</sup> and RMSE of 0.96, 0.71 on train data and 0.75, 1.75 on test data. Pressure, metal:support ratio, active metal composition, GHSV and reaction temperature were found to be influential parameters for optimization of methanol yield. The prediction capability of this model is also validated based on unseen experimental data with varied input parameters and the predictions are good enough with R<sup>2</sup> and RMSE of 0.9 and 1.14. Therefore, this model can be regarded as a valuable solution to guide experimental design without actual experimentation for Cu-based catalysts.</p>\",\"PeriodicalId\":508,\"journal\":{\"name\":\"Catalysis Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Catalysis Letters\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10562-024-04800-0\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Catalysis Letters","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s10562-024-04800-0","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
In Silico Models for Prediction of Methanol Yield in CO2 Hydrogenation Reaction Using Cu-Based Catalysts
CO2 hydrogenation to methanol is instrumental in mitigating carbon emissions and providing a renewable source of clean fuel, methanol. Though Cu-based catalysts proved to be economical and efficient catalysts for this reaction, it has the disadvantage of low catalyst efficiency and sintering. In this study, we developed different six machine learning (ML) models for the prediction of methanol yield (%) from CO2 hydrogenation for Cu-based catalysts. The gradient boost random trees model outperformed other ML models with accuracy R2 and RMSE of 0.96, 0.71 on train data and 0.75, 1.75 on test data. Pressure, metal:support ratio, active metal composition, GHSV and reaction temperature were found to be influential parameters for optimization of methanol yield. The prediction capability of this model is also validated based on unseen experimental data with varied input parameters and the predictions are good enough with R2 and RMSE of 0.9 and 1.14. Therefore, this model can be regarded as a valuable solution to guide experimental design without actual experimentation for Cu-based catalysts.
期刊介绍:
Catalysis Letters aim is the rapid publication of outstanding and high-impact original research articles in catalysis. The scope of the journal covers a broad range of topics in all fields of both applied and theoretical catalysis, including heterogeneous, homogeneous and biocatalysis.
The high-quality original research articles published in Catalysis Letters are subject to rigorous peer review. Accepted papers are published online first and subsequently in print issues. All contributions must include a graphical abstract. Manuscripts should be written in English and the responsibility lies with the authors to ensure that they are grammatically and linguistically correct. Authors for whom English is not the working language are encouraged to consider using a professional language-editing service before submitting their manuscripts.