Selective conversion of methanol to propylene over highly dealuminated mordenite:Al location and crystal morphology effects

The growing consumption of light olefins has stimulated intensive researches on methanol to olefin(MTO)process which possesses great advantages for coal conversion to value‐added chemicals in an environmentally benign way.The catalysts commonly used for MTO process faces several challenges such as poor selectivity control,low hydrothermal stability and short lifetime.In the present study,we prepared a series of mordenite zeolites with variable Al contents(Si/Al molar ratios of 51−436)by a sequential dealumination treatment of air‐calcination and acid leaching.The textural properties,acidity and Al location before and after the dealumination treatment have been systematically studied and their effect on MTO especially the methanol to propylene(MTP)performance was thoroughly investigated.The mordenite zeolites with the Si/Al ratios over 150 selectively catalyzed methanol conversion in the MTP pathway,providing a high propylene selectivity of 63%and propylene/ethylene ratio of>10.Compared to the low‐silica MOR catalysts,highly dealuminated MOR showed much higher stability and longer lifetime,which can be further enhanced via harsh hydrothermal pretreatment.Furthermore,the lifetime was highly related to the crystal size along c‐axis.The excellent performance of highly dealuminated MOR is likely ascribed to the mesopores formed upon dealumination and the scarce Al sites located in the T sites shared by the 8‐member ring(MR)side pockets and 12‐MR pore channels.

Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

This paper studies a federated edge learning system,in which an edge server coordinates a set of edge devices to train a shared machine learning(ML)model based on their locally distributed data samples.During the distributed training,we exploit the joint communication and computation design for improving the system energy efficiency,in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized.In particular,we consider two transmission protocols for edge devices to upload ML-parameters to edge server,based on the non-orthogonal multiple access(NOMA)and time division multiple access(TDMA),respectively.Under both protocols,we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy,by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit(CPU)frequencies for local update.We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization.Numerical results show that as compared to other benchmark schemes,our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system,by properly balancing the energy tradeoff between communication and computation.