@Article{hmt.18.8,
AUTHOR = {Rongge Xiao
, Qi Zhuang, Shuaishuai Jin
, Wenbo Jin},
TITLE = {PREDICTION MODEL OF WAX DEPOSITION RATE BASED ON WOABPNN ALGORITHM},
JOURNAL = {Frontiers in Heat and Mass Transfer},
VOLUME = {18},
YEAR = {2022},
NUMBER = {1},
PAGES = {1--7},
URL = {http://www.techscience.com/fhmt/v18n1/52439},
ISSN = {2151-8629},
ABSTRACT = {A model for predicting wax deposition rate in pipeline transportation is constructed to predict wax deposition in actual pipeline, which can provide 
decision support for the flow guarantee of waxy crude oil in pipeline transportation. This paper analyzes the working principle of Back Propagation 
Neural Networks (BPNN). Aiming at the problems of BPNN model, such as over learning, long training time, low generalization ability and easy to 
fall into local minimum, the paper proposes an improved scheme of using Whale Optimization Algorithm (WOA) to optimize BPNN model(WOABPNN).Taking 38 groups of crude oil wax deposition experimental data in Huachi operation area as an example, the simulation calculation is carried 
out in MATLAB, and the Genetic Algorithm optimized BPNN(GA-BPNN) and the non Optimized BP neural network are used as comparative models 
for comparative analysis. The results show that the Mean Relative Error (<i>MRE</i>) of WOA-BPNN model in predicting wax deposition rate is 2.72% and 
the coefficient of determination(<i>R
<sup>2</sup></i>
) is 0.9966, which are better than those of BPNN and GA-BPNN models. It is proved that WOA-BPNN model has 
higher accuracy and robustness in predicting wax deposition rate.},
DOI = {10.5098/hmt.18.8}
}