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At present, the optimization of the optimal thermal recovery temperature in the process of thermal natural gas hydrate is considered from the aspects of reducing the heat injection and increasing the heat of combustion of the gas, that is, the combustion of methane gas under the unit heat injection. The thermal recovery temperature corresponding to the maximum heat is the optimal thermal recovery temperature. Based on the consideration of reducing the heat loss in the thermal recovery process, this paper considers the reduction of the duration of the thermal recovery process, introduces the new concept of thermal recovery development efficiency, and defines the thermal recovery development efficiency as the unit heat injection in the natural gas hydrate thermal recovery process. The combustion heat of the average gas production rate is introduced into the concept of an infinitely long cylindrical heat exchange reaction kettle. The 303K, 318K, 333K, 348K and 353K partition wall thermal gas hydrate experiments are carried out for the own equipment. The experimental data show that 348K °C can be obtained. The largest thermal development efficiency. Using the thermal development efficiency evaluation standard of this paper, for the weather hydrate thermal recovery process under different parameters, by determining the optimal heat exchange temperature, the energy utilization efficiency and gas production rate of the thermal recovery process can be significantly improved, and the thermal recovery development can be reduced. Economic costs in natural gas hydrate processes. [ABSTRACT FROM AUTHOR]

To more accurately describe the evolution process of traffic flow, a novel method was proposed on the basis of the polynomial trend model and the Kalman filtering theory in the paper. The state variables were defined as the deviations of the actual OD flow from the historical values. These deviations were presented as a stochastic evolution process with a sliding trend. Furthermore, the dynamic OD matrix was estimated and predicated by establishing a polynomial trend filtering model. A large number of simulation results prove that the performance of the algorithm proposed in this paper is better than the traditional method, and this algorithm can acquire more accurate estimation and prediction information for OD matrix. [ABSTRACT FROM AUTHOR]