Silicoaluminophosphate zeolite (SAPO‐34) has been attracting increasing attention due to its excellent form selection and controllability in the chemical industry, as well as being one of the best industrial catalysts for methanol‐to‐olefin (MTO) reaction conversion. However, as a microporous molecular sieve, SAPO‐34 easily generates carbon deposition and rapidly becomes inactivated. Therefore, it is necessary to reduce the crystal size of the zeolite or to introduce secondary macropores into the zeolite crystal to form a hierarchical structure in order to improve the catalytic effect. In this review, the synthesis methods of conventional SAPO‐34 molecular sieves, hierarchical SAPO‐34 molecular sieves and nanosized SAPO‐34 molecular sieves are introduced, and the properties of the synthesized SAPO‐34 molecular sieves are described, including the phase, morphology, pore structure, acid source, and catalytic performance, in particular with respect to the synthesis of hierarchical SAPO‐34 molecular sieves. We hope that the review can provide guidance to the preparation of the SAPO‐34 catalysts, and stimulate the future development of high‐performance hierarchical SAPO‐34 catalysts to meet the growing demands of the material and chemical industries.

The methanol‐to‐olefins reaction has received considerable interest owing to its importance in converting abundant resources, such as coal, natural gas and biomass, to widely demanded light olefins. SAPO‐34, with a CHA topological structure, has high methanol conversion and excellent selectivity for light olefins. For more details on the efficient synthesis and the use of the SAPO‐34 molecular sieves, see the Review by X. Wu et al. (DOI: 10.1002/chem.202102787).

The ZrN‐SiAlON composite powder was synthesized using low‐grade zircon and bauxite by carbothermal reduction nitridation at first and then ZrN‐SiAlON‐SiC‐C composite refractory were fabricated with the ZrN‐SiAlON powder, SiC particles, and a small amount of Si powder as raw materials and sucrose as the binder. The slag resistance of these composites in O2, N2 and Ar atmosphere was investigated by X‐ray diffraction, scanning electron microscopy, and energy dispersion spectra. The results show that the pores in the inside of ZrN‐SiAlON‐SiC‐C composite refractory were enlarged in oxygen atmosphere due to oxidation, which leads to the decrease in slag resistance. In argon atmosphere, blast furnace slag destroyed the sintered body of zircon, corundum, and cristobalite with the formation of CaZrO3, then infiltrated into and filled the pores inside the refractory to form a dense layer, which hindered the further erosion of the blast furnace slag. In the reducing atmosphere, the interfacial energy of the gas‐liquid phases became larger due to the reactions between blast furnace slag liquid and the gas, resulting in a larger wetting angle which prevented the erosion.

In this paper, we research on the wind power prediction method to identify a viable and stable way of predictive modeling applications, namely neural network modeling method. After giving the analysis of wind power generation characteristics, we find the most important impact factor of wind power by the grey relational method .With RBF neural network and actual monitoring data as sample data, finally we design and implement of wind power prediction system, which has a certain practical value.

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