The Prediction of liquid holdup in horizontal pipe with BP neural network

Abstract

Abstract 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.

Keywords

• BP neural network algorithm
• gas‐liquid two‐phase flow
• influencing factors
• liquid holdup
• orthogonal test