Separating monomeric cycloalkanes from naphtha obtained from direct coal liquefaction not only facilitates the valuable utilization of naphtha but also holds potential for addressing China’s domestic chemical feedsto...
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Separating monomeric cycloalkanes from naphtha obtained from direct coal liquefaction not only facilitates the valuable utilization of naphtha but also holds potential for addressing China’s domestic chemical feedstock market demand for these *** extractive distillation processes of naphtha,relative volatility serves as a crucial parameter for extractant ***,determining relative volatility through conventional vapor-liquid equilibrium experiments for extractant selection proves challenging due to the complexity of naphtha’s compound *** address this challenge,a prediction model for the relative volatility of n-heptane/methylcyclohexane in various extractants has been developed using machine-learning quantitativestructure-propertyrelationship *** model enables rapid and cost-effective extractant *** statistical analysis of the model revealed favorable performance indicators,including a coefficient of determination of 0.88,cross-validation coefficient of 0.94,and root mean square error of *** such asα,EHOMO,ρ,and logPoct/water collectively influence relative *** of standardized coefficients in the multivariate linear regression equation identified density as the primary factor affecting the relative volatility of n-heptane/methylcyclohexane in the different *** with higher densities,devoid of branched chains,exhibited increased relative volatility compared to their counterparts with branched ***,the process of separating cycloalkane monomers from direct coal liquefaction products via extractive distillation was optimized using Aspen Plus software,achieving purities exceeding 0.99 and yields exceeding 0.90 for cyclohexane and methylcyclohexane ***,energy consumption,and environmental assessments were *** acid emerged as the most suitable extractant for purifying cycloalkanes in direct coal liquefaction naphtha due to its super
This work presents the development of molecular-based mathematical model for the prediction of CO_(2) solubility in deep eutectic solvents(DESs).First,a comprehensive database containing 1011 CO_(2) solubility data in...
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This work presents the development of molecular-based mathematical model for the prediction of CO_(2) solubility in deep eutectic solvents(DESs).First,a comprehensive database containing 1011 CO_(2) solubility data in various DESs at different temperatures and pressures is established,and the COSMO-RS-derived descriptors of involved hydrogen bond acceptors and hydrogen bond donors of DESs are ***,the efficiency of the input variables,i.e.,temperature,pressure,COSMO-RS-derived descriptors of HBA and HBD as well as their molar ratio,is explored by a qualitative analysis of CO_(2) solubility in DESs using a simple multiple linear regression model.A machine learning method namely random forest is then employed to develop more accurate nonlinear quantitativestructure-propertyrelationship(QSPR)*** the QSPR validation and comparisons with literature-reported models(i.e.,COSMO-RS model,traditional thermodynamic models and equations of state methods),the developed QSPR model with COSMO-RS-derived parameters as molecular descriptors is suggested to be able to give reliable predictions of CO_(2) solubility in DESs and could be used as a useful tool in selecting DESs for CO_(2) capture processes.
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