Application of wavelet neural network with chaos theory for enhanced forecasting of pressure drop signals in vapor−liquid−solid fluidized bed evaporator  

作　　者：Xiaoping Xu Ting Zhang Zhimin Mu Yongli Ma Mingyan Liu 

机构地区：[1]College of Basic Science,Tianjin Agricultural University,Tianjin 300392,China [2]School of Chemical Engineering and Technology,Tianjin University,Tianjin 300350,China [3]State Key Laboratory of Chemical Engineering,Tianjin University,Tianjin 300350,China

出　　处：《Chinese Journal of Chemical Engineering》2025年第2期67-81,共15页中国化学工程学报(英文版)

基　　金：supported by the open foundation of State Key Laboratory of Chemical Engineering(SKL-ChE-22B01);the Natural Science Foundation of China(22008169).

摘　　要：The dynamics of vapor−liquid−solid(V−L−S)flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior,hindering accurate prediction of pressure drop signals.To address this challenge,this study proposes an innovative hybrid approach that integrates wavelet neural network(WNN)with chaos analysis.By leveraging the Cross-Correlation(C−C)method,the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN.Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals,advancing our understanding of the intricate dynamic phenomena occurring with V−L−S fluidized bed evaporators.Moreover,this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.

关 键 词：Wavelet neural network forecasting Chaos theory Phase space reconstruction Pressure drop forecasting Fluidized bed evaporator Multi-phase dynamics 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]