The aniline intercalated MoO₃ organic-inorganic hybrid compound as precursor was prepared. Then it was treated as Oxidative polymerization in air atmosphere at 120 ℃, the polyaniline intercalated MoO₃ composite was constructed. Rhodamine B (RhB) was used as the target degradation material, and the photocatalytic of MoO₃ composite was investigated The effects of light time, catalyst dosage and pH value on photocatalytic degradation efficiency were studied. The results show that the MoO₃/PANI composites have good photocatalytic degradation effect on Rhodamine B under the conditions of pH value of 1~4, catalyst mass concentration of 0.30~0.40 g/L and illumination time of 2.0 h, moreover that the highest degradation rate is up to 98.00%.

    Pyridine-sulfonic acid functionalized ionic liquid([PY-BS] [TsO]) is prepared by the reaction of p-toluenesulfonic acid with sulfonic groups amphoteric salt(PY-BS) that is prepared by the reaction of Pyridine with 1,4butyl sultone. Then, [PY-BS][TsO] ionic liquid can be characterized by IR, NMR. And it is chosen as the catalyst and solvent in the Friedel-Craft acylation of 2-methoxynaphthalene optimizing the reaction conditions by orthogonal experiment of L₉(3⁴). The orthogonal experiment result shows that the conversion of 2-methoxynaphthalene (2-MN) reaches 90.7% and the selectivity of 2-methoxy-1-acetyl naphthalene as the main products is 99.4% in the follwing conditions that the 2methoxynaphthalene is 5 mmol, the mol ratio of 2-methoxynaphthalene/acetic anhydride is 1∶5, ionic liquid is 0.3 g and reaction time is 6.0 h at 120 ℃. The main factors affecting the reaction rate and product selectivity are the reaction time and temperature.

      Based on three-dimensional somatic environment of the real clover type hydrocracking catalyst as the computational entity and the simulated industrial operating temperature as the boundary condition. The Fourier heat transfer equation is solved by the meshless numerical method. Then the influence of external temperature fluctuation on the internal temperature distribution of the catalyst was analyzed by using the calculated results. The analysis results show that the actual reactions in the catalyst is not isothermal reaction. At the same time, the fluctuation of the reaction temperature and the hot spot inside the catalyst have a certain influence on the temperature distribution for the catalyst cluster in the hydrocracking process. Catalyst bulk phase inner average temperature is influenced by heat of reaction, the catalyst particle size, material density, reaction space velocity and catalyst inner hotspot distribution.

        In recent years, the natural gas hydrate as a new type of high efficiency, energy saving, environmental protection energy was praised by the world's energy industry, and its potential value could not be ignored. The study of gas hydrate was integrated in different countries of the world history, the present research situation and prospect analysis. In view of the natural gas hydrate in China started to be late and a series of actual situation, a set of research strategies suitable for the current development of our country was put forward, and some problems in the process of the actual mining were introduced.

      Long-term natural gas load forecasting can solve the problem of the imbalance between supply and demand of city gas and provide assistance for the city gas company's management and running. In order to improve the accuracy of predicting the longterm natural gas load，a forecasting model of natural gas longterm load was built based on SVM-GA(Support Vector MachinesGenetic Algorithm). The relevant factors influencing natural gas consumption was analyzed and determined. In order to improve prediction accuracy， the penalty factor c and the kernel parameter g of support vector machines were optimized using genetic algorithm and cross validation methods. Optimized parameters were inputted support vector machines model and long-term natural gas load forecasting was made. In a case study from a certain city，a comparative analysis was made of the forecasting results among SVM-GA，SVM and crossvalidation method combined prediction model and BP(Back Propagation) neural networks. The forecasting model based on SVM-GA was validated with a high prediction accuracy and the resulted relative mean square error，normalization mean square error，normalization absolute square error，normalization rootmean square error， maximum absolute error resulted from the SVM-GA were lower than those from SVM and crossvalidation method combined prediction model or BP neural networks by 0.58%，3.98%，2.99%，4.58%，8.64% and 6.13%，26.28%，19.71%，21.09%，31.48%. Therefore，the support vector machine and genetic algorithm combined model can accurately predict the long-term natural gas load.

      There is usually a starting pressure gradient in the tight oil. This article considers the seepage law of the crude oil flowing from the matrix to the fractures in the formation under the premise of starting the pressure gradient and some assumptions. Through the establishment of numerical model, the relationship between the pressure, permeability, core size and other factors and the degree of failure recovery in the onedimensional space is analyzed, as well as the threedimensional plate is drawn. The results show that when the core scale is very small, the main factors affecting the degree of recovery are the failure pressure difference. At the same time when the scale increases, the relationship between the recovery degree and the core length and permeability is more and more significant. Then, based on the conclusion of the previous the paper put forward the empirical formula considering the degree of calculation and recovery under various factors, and extend the space to three dimensions. On the basis of one dimensional revise the formula, the empirical formula of the failure rate of three dimensional matrix failure is obtained. It provides some reference for production practice.

       WC-10Co4Cr coatings were prepared by high velocity oxy-fuel with different spray distances. The internal structure and wear/corrosion resistance of the coating were studied. The internal structure and phase composition of the coating were analyzed by Metallographic microscope, SEM and XRD. The microscopic structure, microhardness, porosity, density, wear/ corrosion resistance properties were tested. The results showed that the particle stayed in the jet for longer time and in the process of spraying it enlarged the melting degree as the distance increased. As a result, complete flattening of particles was gained which increased the density of coating and the property of wear resistance and corrosion resistance. The coating showed the best property of wear resistance and corrosion resistance at the spray distance of 380 mm.

      Power grid is diagnosed after a failure to prevent the fault occurred by inferring the information that the fault generated.The method of model prediction (MP) and abductive reasoning network(ARN) is proposed to forecast the power system fault. MP predicted the troublefree operation data of the power grid by using historical data, and compared with the actual grid runtime data, the difference was calculated and used as the input of fault diagnosis system. ARN was used to bulid the fault diagnosis system and solve the complicated relationships between data processing and the corresponding candidate fault section. The fault location can be found before protection device and circuit breaker by combining the method of MP and ARN. The test results showed that the model prediction method can quickly and accurately diagnose the fault compared with BP neural network method.

      E-commerce enterprise performance was one of the important issues to be considered when they developed to a certain stage, and it was realistic to research enterprise performance. SUNING was taken as an example, the typical E-commerce enterprise, 10 key factors were proposed that affected the performance and converted them into 10 key indicators to study the performance of the E-commerce enterprises. By SPSS19.0 software, the correlation test, descriptive statistics and regression analysis were made based on the 10 indicators, and the conclusion showed that the key factors influenced the E-business enterprise performance. The research conclusion would play a guiding role in the future development of E-commerce enterprises.