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

IF 4.1 2区材料科学 Q2 ENGINEERING, CHEMICAL Particuology Pub Date : 2024-09-24 DOI:10.1016/j.partic.2024.09.010

Bojian Qi , Yong Yan , Wenbiao Zhang

引用次数: 0

Abstract

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.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

通过希尔伯特-黄变换和数据驱动建模研究流化床中混合生物质颗粒的流动动力学

研究二元颗粒的流动动力学是为了实现流化床的监测和优化。考虑到现有技术的局限性，准确划分混合生物质的质量分数是一项挑战。本文分析了从静电传感器阵列收集到的数据。交叉相关、经验模式分解（EMD）和希尔伯特-黄变换（HHT）被用于处理信号。在木锯屑质量分数较高的情况下，会出现偏析行为，HHT 频谱的高能量区域会增大。此外，基于混合小波散射变换和双向长短期记忆（ST-BiLSTM）网络以及 EMD 和 BiLSTM（EMD-BiLSTM）网络训练了两个数据驱动模型，以识别混合生物质的质量分数，准确率分别为 92% 和 99%。静电感应与 EMD-BiLSTM 模型相结合可有效地对混合生物质的质量分数进行分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Particuology 工程技术-材料科学：综合

CiteScore

6.70

自引率

2.90%

发文量

1730

审稿时长

32 days

期刊介绍： The word ‘particuology’ was coined to parallel the discipline for the science and technology of particles. Particuology is an interdisciplinary journal that publishes frontier research articles and critical reviews on the discovery, formulation and engineering of particulate materials, processes and systems. It especially welcomes contributions utilising advanced theoretical, modelling and measurement methods to enable the discovery and creation of new particulate materials, and the manufacturing of functional particulate-based products, such as sensors. Papers are handled by Thematic Editors who oversee contributions from specific subject fields. These fields are classified into: Particle Synthesis and Modification; Particle Characterization and Measurement; Granular Systems and Bulk Solids Technology; Fluidization and Particle-Fluid Systems; Aerosols; and Applications of Particle Technology. Key topics concerning the creation and processing of particulates include: -Modelling and simulation of particle formation, collective behaviour of particles and systems for particle production over a broad spectrum of length scales -Mining of experimental data for particle synthesis and surface properties to facilitate the creation of new materials and processes -Particle design and preparation including controlled response and sensing functionalities in formation, delivery systems and biological systems, etc. -Experimental and computational methods for visualization and analysis of particulate system. These topics are broadly relevant to the production of materials, pharmaceuticals and food, and to the conversion of energy resources to fuels and protection of the environment.