Quantitative Structure-Relative Volatility Relationship Model for Extractive Distillation of Ethylbenzene/ p -Xylene Mixtures: Application to Binary and Ternary Mixtures as Extractive Agents

Bulletin- Korean Chemical Society (B KOREAN CHEM SOC);
ABSTRACT Ethylbenzene (EB) and p-xylene (PX) are important chemicals for the production of industrial materials; accordingly, their efficient separation is desired, even though the difference in their boiling points is very small. This paper describes the efforts toward the identification of high-performance extractive agents for EB and PX separation by distillation. Most high-performance extractive agents contain halogen atoms, which present health hazards and are corrosive to distillation plates. To avoid this disadvantage of extractive agents, we developed a quantitative structure–relative volatility relationship (QSRVR) model for designing safe extractive agents. We have previously developed and reported QSRVR models for single extractive agents. In this study, we introduce extended QSRVR models for binary and ternary extractive agents. The QSRVR models accurately predict the relative volatilities of binary and ternary extractive agents. The service to predict the relative volatility for binary and ternary extractive agents is freely available from the Internet at http://qsrvr.opengsi.org/.

Quantitative Structure Relative Volatility Relationship Model for Extractive Distillation of Ethylbenzene/p-Xylene Mixtures

Industrial & Engineering Chemistry Research (Impact Factor: 2.24). 06/2014;
ABSTRACT Extractive distillation is a highly effective process for the separation of compound pairs having low relative volatility values, such as ethylbenzene (EB) and p-xylene (PX) mixtures. Many solvents or solvent mixtures have been screened experimentally to identify a suitable extraction agent for EB/PX mixtures. Because the number of possible solvent and solvent mixture candidates is high, it is necessary to introduce a computer-aided extraction performance prediction technique. In this study, a knowledge-based quantitative structure relative volatility relationship (QSRVR) model was developed using multiple linear regression (MLR) and artificial neural network (ANN) models, with each model having five descriptors. The root-mean-square errors (RMSE) of the training and test sets for the MLR model were calculated as 0.01486 and 0.00905, while their squared correlation coefficients (R2) were 0.867 and 0.941, respectively. The R2 and RMSE values of the total data set for the MLR model were 0.878 and 0.01408, and for the ANN model the values were 0.949 and 0.00929, respectively. The predictive ability of both models is sufficient for identifying suitable extractive distillation solvents for the separation of EB/PX mixtures.