@Article{hmt.20.13,
AUTHOR = {Qi Zhuang
, Dong Liu, Bo Liu, Mei Liu},
TITLE = {PREDICTION MODEL OF LIQUID HOLDUP BASED ON SOA-BPNN  ALGORITHM},
JOURNAL = {Frontiers in Heat and Mass Transfer},
VOLUME = {20},
YEAR = {2023},
NUMBER = {1},
PAGES = {1--6},
URL = {http://www.techscience.com/fhmt/v20n1/52375},
ISSN = {2151-8629},
ABSTRACT = {In the actual operation of wet gas pipeline, liquid accumulation is easy to form in the low-lying and uphill sections of the pipeline, which leads to a 
series of problems such as reduced pipeline transportation efficiency, increased pipeline pressure drop, hydrate formation, slug flow and intensified 
corrosion in the pipeline. Accurate calculation of liquid holdup is of great significance to the research of flow pattern identification, pipeline corrosion 
evaluation and prediction, and gas pipeline transportation efficiency calculation. Based on the experimental data of liquid holdup in horizontal pipeline, 
a commonly used BP neural network (BPNN) model is established in this paper. In order to improve the accuracy of BPNN model, Genetic Algorithm 
(GA), Particle Swarm Optimization (PSO) and Seeker Optimization Algorithm (SOA) are used to optimize the initial weights and thresholds of BPNN
model, and GA-BPNN model, PSO-BPNN model and SOA-BPNN model are established. By comparing the model accuracy, the average absolute 
error of SOA-BPNN prediction model is 3.7351%, and the root mean square error is 0.0113. This model has high prediction accuracy and wide 
application range, which is obviously superior to other algorithms, and provides a new method for accurate prediction of liquid holdup of wet gas 
pipeline.},
DOI = {10.5098/hmt.20.13}
}