Rongge Xiaoa,*
, Qi Zhuanga, Shuaishuai Jina
, Wenbo Jina
Frontiers in Heat and Mass Transfer, Vol.18, pp. 1-7, 2022, DOI:10.5098/hmt.18.8
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 More >
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