Efficient conversion of benzene and syngas to toluene and xylene over ZnO-ZrO_(2)&H-ZSM-5 bifunctional catalysts

A series of ZnO-ZrO_(2) solid solutions with different Zn contents were synthesized by the urea coprecipitation method,which were coupled with H-ZSM-5 zeolite to form bifunctional catalysts.As a new benzene alkylation reagent,syngas was used instead of methanol to realize the efficient conversion of syngas and benzene into toluene and xylene.A suitable ratio of ZnO-ZrO_(2) led to the significant improvement in the catalytic performance,and a suitable amount of acid helped to increase the selectivity of toluene/xylene and reduce the selectivity of the by-products ethylbenzene and C^(9+) aromatics.The highest benzene conversion of 89.2%and toluene/xylene selectivity of 88.7%were achieved over 10%ZnO-ZrO_(2)&H-ZSM-5(Si/Al=23)at a pressure of 3 MPa and a temperature of 450℃.In addition,the effect of the zeolite framework structure on product distribution was examined.Similar to the molecular dynamics of aromatic hydrocarbons,H-ZSM-5 zeolites comprise 10-membered-ring pores,which are beneficial to the activation of benzene;hence,the conversion of benzene is higher.H-ZSM-35 and HMOR zeolites exhibited small eight-membered-ring channels,which were not conducive to the passage of benzene;hence,the by-product ethylbenzene exhibits a higher selectivity.The distance between the active centers of the bifunctional catalysts was the main factor affecting the catalytic performance,and the powder mixing method was more conducive to the conversion of syngas and benzene.

Road traffic congestion measurement considering impacts on travelers

The article intends to find a method to quantify traffic congestion＇s impacts on travelers to help transportation planners and policy decision makers well understand congestion situations. Three new congestion indicators, including transportation environment satisfaction （TES）, travel time satisfaction （TTS）, and traffic congestion frequency and feeling （TCFF）, are defined to estimate urban traffic congestion based on travelers＇ feelings. Data of travelers＇ attitude about congestion and trip information were collected from a survey in Shanghai, China. Based on the survey data, we estimated the value of the three indi- cators. Then, the principal components analysis was used to derive a small number of linear combinations of a set of variables to estimate the whole congestion status. A linear regression model was used to find out the significant variables which impact respondents＇ feelings. Two ordered logit models were used to select significant variables of TES and TTS. Attitudinal factor variables were also used in these models. The results show that attitudinal factor variables and cluster category variables are as important as sociodemographic variables in the models. Using the three congestion indicators, the government can collect travelers＇ feeling about traffic congestion and estimate the transportation policy that might be applied to cope with traffic congestion.

Dwell time estimation models for bus rapid transit stations

Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are in the process of planning the construction of BRT systems. To improve the performance of BRT systems, many researchers study BRT operation and control, which include the study of dwell times at bus/BRT stations. To ensure the effectiveness of real-time control which aims to avoid bus/BRT vehicles congestion, accurate dwell time models are needed. We develop our models using data from a BRT vehicle survey conducted in Changzhou, China, where BRT lines are built along passenger corridors, and BRT stations are enclosed like light rails. This means that interactions between passengers traveling on the BRT system are more frequent than those in traditional transit system who use platform stations. We statistically analyze the BRT vehicle survey data, and based on this analysis, we are able to make the following conclusions： （ I ） The delay time per passenger at a BRT station is less than that at a non-BRT station, which implies that BRT stations are efficient in the sense that they are able to move passengers quickly. （II） The dwell time follows a logarithmic normal distribution with a mean of 2.56 and a variance of 0.53. （III） The greater the number of BRT lines serviced by a station, the longer the dwell time is. （IV） Daily travel demands are highest during the morning peak interval where the dwell time, the number of passengers boarding and alighting and the number of passengers on vehicles reach their maximum values. （V） The dwell time is highly positively correlated with the total number of passengers boarding and alighting. （VI） The delay per passenger is negatively correlated with the total number of passengers boarding and alighting. We propose two dwell time models for the BRT station. The first proposed model is a linear model while the second is nonlinear. We introduce the conflict between passengers boarding and alighting into our models. Finally, by comparing our models with the models of Rajbhandari and Chien et al., and TCQSM （Transit Capacity and Quality of Service Manual）, we conclude that the proposed nonlinear model can better predict the dwell time at BRT stations.