Influence of water vapor on the separation of volatile organic compound/nitrogen mixture by polydimethylsiloxane membrane

In the industrial treatment of waste volatile organic compound(VOC)streams by membrane technology,a third impurity,generally,water vapor,coexists in the mixture of VOC and nitrogen or air,and can affect membrane performance and the design of the industrial process.This study focused on the investigation of the effect of water vapor on the separation performance of the separation of VOC/water/nitrogen mixtures by a polydimethylsiloxane(PDMS)membrane.Three types of VOCs:water-miscible ethanol,water-semi-miscible butanol,and water-immiscible cyclohexane,were selected for the study.Different operating parameters including,concentration of the feed VOC,feed temperature,and concentration of the feed water were compared for the separation of binary and ternary VOC/nitrogen mixtures.The interaction between the VOC and water was analyzed to explain the transportation mechanism after analyzing the difference in the membrane performance for the separation of binary and ternary mixtures.The results indicated that the interaction between the VOC(or nitrogen)and water is the key factor affecting membrane performance.Water can promote the permeation of hydrophilic VOC but prevent hydrophobic VOC through the membrane for the separation of ternary VOC/water/nitrogen mixtures.These results will provide fundamental insights for the design of the recovery application process for industrial membrane-based VOCs,and also guidance for the investigation of the separation mechanism in vapor permeation.

Understanding the Behavioral Differences Between American and German Users: A Data-Driven Study

Given that the USA and Germany are the most populous countries in North America and Western Europe,understanding the behavioral differences between American and German users of online social networks is essential.In this work,we conduct a data-driven study based on the Yelp Open Dataset.We demonstrate the behavioral characteristics of both American and German users from different aspects,i.e.,social connectivity,review styles,and spatiotemporal patterns.In addition,we construct a classification model to accurately recognize American and German users according to the behavioral data.Our model achieves high classification performance with an F1-score of 0.891 and AUC of 0.949.