Yildiz, Merve G.Davran-Candan, TugbaGunay, M. ErdemYildirim, Ramazan2024-07-182024-07-1820192212-98202212-9839https://doi.org/10.1016/j.jcou.2019.02.010https://hdl.handle.net/11411/7415This study aims to extract knowledge for CO2 capture by amine-functionalized mesoporous silica (MCM-41 and SBA-15) through exploratory analysis and decision tree classification of the data reported in over 100 papers published between the years 2002 and 2017. A database containing 1039 data points showing the effects of 15 input variables (grouped in four as support properties, preparation method, amine properties and operational variables) over two performance variables as CO2 adsorption capacity and amine efficiency (CO2 captured/amino groups involved) was constructed. Box and whisker plots were applied (as a part of exploratory data analysis) to determine how various input variables influence the performance variables. Moreover, decision tree classification was used to determine the relative significance of the input variables and the possible combinations of these variables leading to high performance (to deduce heuristics for high CO2 uptake). It was found from the exploratory data analysis that amine density was the most significant variable affecting the adsorption capacity whereas remaining pore volume and adsorption temperature were the most influential variables in case of amine efficiency. Furthermore, various combinations of input variables leading to high CO2 capture performance were revealed through the decision tree analysis, all of which may be used as guidelines for future studies in this area.eninfo:eu-repo/semantics/closedAccessCo2 AdsorptionAmine-Functionalized SorbentsMesoporous SilicaData MiningDecision TreesBox And Whisker PlotPore-Expanded Mcm-41Ordered Mesoporous SilicaCarbon-DioxideAdsorption PerformancePromising AdsorbentMolecular-SieveHigh-CapacitySorbentsRemovalEquilibriumArticle2-s2.0-8506273237410.1016/j.jcou.2019.02.01042Q12731Q1WOS:000464979200004