Liquid holdup is one of the most critical factors for the formation of pipe effusion, which is closely related to the efficiency of pipe transportation. Nowadays, liquid holdup is mainly estimated according to empirical or semiempirical correlation. Besides, little has been done concerning the accurate prediction of liquid holdup. Therefore, to obtain more precise forecast, this paper proposed a prediction method concerning liquid holdup in horizontal pipe with BP neural network algorithm. Meanwhile, a sensitivity analysis on the key factors impacting liquid holdup was conducted by the combination of the forecast calculation and orthogonal experiment design. The results showed that compared with the empirical calculation (the smallest standard deviation 8.65%), the BP neural network prediction model had achieved more accurate estimation (the average relative error is 7.38%). In addition, the sensitivity analysis indicated that the main indexes including pipe diameter, gas‐ and liquid‐phase superficial velocities, and temperature have significant influence on the liquid holdup. Pipe diameter, liquid‐phase superficial velocity, temperature, and viscosity are positively correlated with the liquid holdup, while pressure and gas‐phase superficial velocity are negatively correlated with it.

In order to quantitatively characterize the strength of wax deposition layer in pipeline, it is very important to analyze the primary and secondary influencing factors of wax deposition layer for establishing the strength model of wax deposition layer in pipeline. Firstly, based on the experimental data of simulated wax deposit under a certain wax mixing ratio, the influencing factors of wax deposition layer strength are qualitatively analyzed. Then the principal component analysis method is introduced to quantitatively evaluate the influencing factors of wax deposition layer strength, quantify the weights of each influencing factor, and sort the influencing degree of the influencing factors. By analyzing the experimental data of simulated wax deposits with two wax mixing ratio, the results show that both the wax crystal microparameters and the solid phase wax concentration have a great influence on the strength of the wax deposition layer, and the all wax crystal microscopic parameters have greater influence on the strength of the wax deposition layer than the solid phase wax concentration. Therefore, when establishing the strength model of wax deposition layer in pipeline, it is necessary to comprehensively consider the influence of the solid phase wax concentration and the microscopic parameters of the wax crystal on it.

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In this paper, we focused on the dynamic evaluation of sustainable development capacity for mining cities in China. The indicator system of sustainable development capacity for mining cities was constructed from the aspects of resources, environment, economy and social development. BP neural network method was adopted to dynamically predict the capacity for sustainable development system of Panjin City. The dynamic results indicated that of factors influencing the sustainability in the future, economic level, mineral resources production and degree of coordination between industry and environment are very strong. Insufficient educational investment will dissatisfy the requirements of industrial transformation and socio-economic development. Furthermore, the promotion of optimization and upgrading of industrial structure in mining cities should base on the characteristics of resources and regional economy, meanwhile, mining cities should develop clean energy and improve investment environment. This paper laid an experience for the application of BP neural network model in socio-economic research field.