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

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.

Prediction of short‐term photovoltaic power via codec neural network and mode decomposition based deep learning approach

“…The Xaiver method is more applicable when saturated activation function used such as Sigmoid and Tanh. 38 The initial value of the model generated by the Xavier method obeys the normal distribution of N(0, std1), where std 1 can be expressed as (12):…”

“…At present, data‐driven artificial neural network and machine learning have been used in nonlinear fitting filed 10‐13,16,17 . Tian Z proposed LSSVM for wind power prediction 14 .…”

“…In addition, traditional filtering methods such as mean filtering and wavelet domain denoizing used in the existing statistical prediction models have limited effect to eliminate the noise component of PV power, which restrict further improvement of the prediction accuracy. [8][9][10][11][12][13] At present, data-driven artificial neural network and machine learning have been used in nonlinear fitting filed. [10][11][12][13]16,17 Tian Z proposed LSSVM for wind power prediction.…”

“…[8][9][10][11][12][13] At present, data-driven artificial neural network and machine learning have been used in nonlinear fitting filed. [10][11][12][13]16,17 Tian Z proposed LSSVM for wind power prediction. 14 Ahmad et al used GPR to predict solar and wind power.…”

“…However, strong disturbance such as nonstationarity of sequences and outliers of the sequence will reduce the prediction accuracy of traditional statistical model. In addition, traditional filtering methods such as mean filtering and wavelet domain denoizing used in the existing statistical prediction models have limited effect to eliminate the noise component of PV power, which restrict further improvement of the prediction accuracy 8‐13 …”