Investigations into the flow dynamics of mixed biomass particles in a fluidized bed through Hilbert-Huang transformation and data-driven modelling  

作　　者：Bojian Qi Yong Yan Wenbiao Zhang 

机构地区：[1]School of Computer and Artificial Intelligence,Beijing Technology and Business University,Beijing,100048,China [2]International Research Center for Carbon Neutralization,Hangzhou Beihang International Innovation Institute,Beihang University,Hangzhou,311115,China [3]School of Control and Computer Engineering,North China Electric Power University,Beijing,102206,China

出　　处：《Particuology》2024年第12期115-123,共9页颗粒学报（英文版）

基　　金：the National Natural Science Foundation of China (grant No.62303022)for its funding.

摘　　要：Flow dynamics of binary particles are investigated to realize the monitoring and optimization of fluidized beds.It is a challenge to accurately classify the mass fraction of mixed biomass,considering the limitations of existing techniques.The data collected from an electrostatic sensor array is analyzed.Cross correlation,empirical mode decomposition(EMD),Hilbert-Huang transform(HHT)are applied to process the signals.Under a higher mass fraction of the wood sawdust,the segregation behavior occurs,and the high energy region of HHT spectrum increases.Furthermore,two data-driven models are trained based on a hybrid wavelet scattering transform and bidirectional long short-term memory(ST-BiLSTM)network and a EMD and BiLSTM(EMD-BiLSTM)network to identify the mass fractions of the mixed biomass,with accuracies of 92%and 99%.The electrostatic sensing combined with the EMD-BiLSTM model is effective to classify the mass fraction of the mixed biomass.

关 键 词：Fluidized beds BIOMASS Hilbert-Huang transformation Bidirectional long short-term memory network Electrostatic sensors 

分 类 号：O313[理学—一般力学与力学基础]