{"title":"采用贝叶斯蒙特卡罗方法,使用柔性金属-有机框架对CO2吸附过程进行建模、参数估计和不确定性量化","authors":"Saeki Sugimoto, Yuya Takakura, Hiroshi Kajiro, Junpei Fujiki, Hossein Dashti, Tomoyuki Yajima, Yoshiaki Kawajiri","doi":"10.1002/amp2.10165","DOIUrl":null,"url":null,"abstract":"<p>Flexible metal<b>–</b>organic frameworks (flexible MOFs) are considered promising adsorbents for CO<sub>2</sub> capture, some of which have sigmoidal isotherm shapes that allow adsorption and desorption operations within a narrow partial pressure range. Nevertheless, modeling of adsorption processes employing flexible MOFs remains a challenge due to the unique isotherm shapes and kinetics. In this work, a Bayesian estimation framework is applied sequentially to handle two experimental data sets: isotherm and breakthrough measurements. The computational challenge for estimating the isotherm and kinetic parameters from the isotherm measurements and breakthrough experiments is resolved by Markov chain and sequential Monte Carlo methods. The uncertainties of the model parameters are obtained as probability distributions.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling, parameter estimation, and uncertainty quantification for CO2 adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods\",\"authors\":\"Saeki Sugimoto, Yuya Takakura, Hiroshi Kajiro, Junpei Fujiki, Hossein Dashti, Tomoyuki Yajima, Yoshiaki Kawajiri\",\"doi\":\"10.1002/amp2.10165\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Flexible metal<b>–</b>organic frameworks (flexible MOFs) are considered promising adsorbents for CO<sub>2</sub> capture, some of which have sigmoidal isotherm shapes that allow adsorption and desorption operations within a narrow partial pressure range. Nevertheless, modeling of adsorption processes employing flexible MOFs remains a challenge due to the unique isotherm shapes and kinetics. In this work, a Bayesian estimation framework is applied sequentially to handle two experimental data sets: isotherm and breakthrough measurements. The computational challenge for estimating the isotherm and kinetic parameters from the isotherm measurements and breakthrough experiments is resolved by Markov chain and sequential Monte Carlo methods. The uncertainties of the model parameters are obtained as probability distributions.</p>\",\"PeriodicalId\":87290,\"journal\":{\"name\":\"Journal of advanced manufacturing and processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of advanced manufacturing and processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/amp2.10165\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of advanced manufacturing and processing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/amp2.10165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling, parameter estimation, and uncertainty quantification for CO2 adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods
Flexible metal–organic frameworks (flexible MOFs) are considered promising adsorbents for CO2 capture, some of which have sigmoidal isotherm shapes that allow adsorption and desorption operations within a narrow partial pressure range. Nevertheless, modeling of adsorption processes employing flexible MOFs remains a challenge due to the unique isotherm shapes and kinetics. In this work, a Bayesian estimation framework is applied sequentially to handle two experimental data sets: isotherm and breakthrough measurements. The computational challenge for estimating the isotherm and kinetic parameters from the isotherm measurements and breakthrough experiments is resolved by Markov chain and sequential Monte Carlo methods. The uncertainties of the model parameters are obtained as probability distributions.