In view of the irregularity of the bottom floor of working face and the diversity of the shape of the flying gangue in steeply dipping coal seam, based on the geographic information system data such as contour line of bottom floor of working face, the 3d grid model of bottom floor is established, combined with the energy tracking method(ETM) C + + programs, four typical shapes of flying gangue with the same mass and different shapes are simulated to obtain the motion trajectories of the migration of flying gangue in the actual working face, as well as the velocity, angular velocity and energy change curves at any time. The influence of the shapes on the motion of flying gangue is analyzed. In order to verify the accuracy and feasibility of the method in this paper, the trajectory simulated by Rockyfor3D software is compared. The results show that the transport capacity of ellipsoidal flying gangue is much higher than that of polyhedral flying gangue. Compared with common polyhedral flying gangue, the regular polyhedral flying gangue has farther migration distance and less energy loss due to collision. The number of edges of flying gangue of regular polyhedron is inversely proportional to the energy loss of flying gangue in collision, which indicates that flying gangue of regular polyhedron with multiple edges is most likely to cause danger．

Taking a heavy oil in Shengli Oil Field as the research object,the core was filled with porous medium in the core displacement instrument to simulate the formation conditions,and the aquathermolysis of heavy oil under different reaction conditions was studied.The results shown that the viscosity reduction rate of heavy oil can reach 20.8% only in porous medium by direct displacement,and the viscosity reduction effect is more obvious in porous medium system after the addition of amphiphilic catalyst for reaction.The displacement viscosity reduction rate after reaction in a low?temperature environment of 65 ℃ is 57.9%.However,the reaction in a low?temperature environment leads to an increase in the molecular weight of the asphaltene component,with the relative molecular weight of the asphaltene increasing from 5 244 g/mol to 6 690 g/mol.After the reaction is completed and maintained in a high?temperature environment of 265 ℃,the displacement has a better viscosity reduction effect,with a maximum comprehensive viscosity reduction rate of 96.0%.It provides an important guiding significance for the optimization of operating conditions of heavy oil aquathermolysis in the field application process.

In order to solve the problem of strong subjectivity of the prior data of the dynamic Bayesian network (DBN) obtained when analyzing the reliability of the system, BP neural network was used to optimize the prior data of DBN, taking the burner system of gasifier as the research object. According to the empirical formula of the number of neurons in the hidden layer, the DBN of the gasifier burner system was divided into three subsystems, which were transformed into BP neural network respectively, and the estimated prior distribution of DBN was corresponding to the input function and output function of the BP neural network respectively, the performance of the system is studied and the DBN parameters are optimized by using the characteristics of information is transmitted forward and error backward of BP neural network. The two?way reasoning of the gasifier burner system DBN is carried out to realize the dynamic reliability analysis of the gasifier burner system. The results show that the forward reasoning of the gasifier burner system DBN can obtain the optimized system reliability trend; the reverse reasoning is performed to obtain that the results of key events and weak links remain the same before or after optimization and the weak links are the high value and fluctuation of oxygen coal ratio.

In view of the unstable and nonlinear characteristics of the rolling bearing vibration signal of rotating electrical machines, the traditional time?frequency analysis method and wavelet packet decomposition method have energy leakage and poor adaptive ability in the signal decomposition process, and the EMD decomposition method has modal aliasing and other problems. In order to improve the fault diagnosis accuracy of rolling bearings, CEEMDAN combined with energy moment method is proposed to extract the original vibration signal features. The weight and offset of ELM hidden layer are optimized by SPSO algorithm, and the CEEMDAN?SPSO?ELM method is used to analyze and diagnose single and multiple damage faults of rolling bearings.The effectiveness of the algorithm and the improvement of diagnosis accuracy are verified by comparative experiments.