In this paper, the key problems of catalyst deactivation caused by the formation of green oil on the surface of acetylene selective hydrogenation Pd⁃Ag catalyst were investigated. XRD and H2⁃TPR were used to characterize the composition and the structure of catalyst. The selective hydrogenation performance of Pd⁃Ag catalyst for acetylene and the formation of green oil precursor C4 olefins during the reaction were investigated by using a 10 ml fixed bed high pressure hydrogenation unit. The results show that there are significant differences in the acetylene hydrogenation activity of Pd⁃Ag/Al2O3 catalysts in several different preparation processes, which mainly depend on the dispersion of Pd on the catalyst surface. The close mixing between the promoter Ag and the active center Pd can enhance the electrons and geometric effect, more Pd⁃Ag alloy phase is generated, which reduces the hydrogenation activity of the catalyst, but increases the stability of the catalyst; while the amount of C4 olefins is positively correlated with the activity of the catalyst. The higher the catalyst activity, the more C4 olefins are generated. The conclusion of this study reveals the deactivation mechanism of Pd⁃Ag catalyst, which has important guiding value for catalyst modification and process condition optimization.

The effect of alkali metal modification on g-C ₃N ₄ photocatalyst was systematically studied. The density functional theory is used to explain the microscopic mechanism of different alkali metal modification on improving photocatalytic activity from the various angles of geometry and electronic structure. The results show that the binding energies of alkali metals and g-C ₃N ₄ become weaker and weaker with the increase of the number of extra-nuclear electronic layers of Li, Na, K and Rb atoms, and the influence on the structure of g-C ₃N ₄ is weaker and weaker. The electronic structure analysis shows that Li, Na, and K atoms have an activation effect on g-C ₃N ₄, and Rb atoms have a passivation effect on the surface of g-C ₃N ₄. At the same time, the adsorption of N ₂ molecules on M-g-C ₃N ₄ (M=Li, Na, K, Rb) is simulated, and the interaction mechanism between N ₂ molecules and alkali metals is understood. By analyzing the adsorption energy, structural parameters and electronic properties of N ₂ adsorption, it is found that the influence of Li and Na atoms on N ₂ adsorption is greater. The N—N bond of N ₂ is elongated, and the K and Rb atoms have little effect on N ₂. Alkali metal modified g-C ₃N ₄ is more beneficial for the adsorption of N ₂ molecules than pure g-C ₃N ₄, but as the atomic radius of the alkali metal increases, the adsorption capacity becomes weaker and weaker, and the electron transfer becomes less and less. That is to say, the ability of activating N ₂ molecule decreases in the order of Li, Na, K and Rb.

 

In order to improve the opacity of data collection, the complexity of each operating segment links and coarse tuning parameters manually in the process of data collection and analysis in the lad, a set of complete data collection and analysis system was designed by using data acquisition card of serial port and two channel, nonlinearity reset PSO algorithm and harmonious human-computer interaction interface. The working principles of frequency response data acquisition and data processing system, the composition structure and operation instructions were introduced. Equipment control and data acquisition were carried out after frequency instruction given by lower machine data acquisition system, the data was transmitted through the serial port communication to upper computer then through the Fourier transform into a data processing system. Test results showed that the system could collect data timely and accurately, display data in real time, process and save data automatically and precisely. The system was stable in testing process which guaranteed the accuracy of the data analysis.

A quantitative analysis model for the determination of the oxide concentration in a doublecomponent containing model oil is established based on the infrared spectroscopy data for cyclohexanone and cyclohexanol components in model oil, the regression of prominent component, and the regression of back propagation neural algorithm and support vector machine. The infrared spectroscopy data were compressed by principal component analysis and used as input information to develop models. The three quantitative models can predict the respective oxide contents, with the predictive ability of support vector machine model superior to the other two models, showing a good stability of support vector machine model. Comparing with traditional methods, the established model provides a simple, fast, nodamage, costeffective and green determination method.