基于声发射信号的集成建模技术及其在颗粒检测中的应用研究
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摘要
在气固流化床反应器中,流化床层的传递特性参数、反应效果与流化状态均受到流化床内颗粒的性质及运动状态的影响,而颗粒的性质又是随着反应过程的进行而变化的,在一些特殊的合成反应过程中,床层内物料的粒度、料层高度的波动会严重影响反应进程,因而研究流化床反应器的流体力学行为并实现物料特征参数的在线检测有着非常重要的意义。类似地,搅拌釜式反应器也是化工过程最常用的设备,很多化学产品如颗粒型过碳酸钠等均是在间歇釜式反应器中合成得到的。这类反应过程的显著特点就是反应器内部的物料特征,如颗粒的粒度或者物料的浓度,均会随着时间的推移而不断地发生变化,而目前的传统方法尚难以对这种动态变化的颗粒粒度与物料浓度进行在线测量。
利用被动声发射信号检测反应器内物料的特征参数具有检测灵敏、安全高效、不侵入流场、实时在线的优点。本文以声发射信号作为测量流化床或搅拌釜内物料特征参数的媒介,通过小波或小波包对声发射信号进行时频多尺度分析,由分解结果构建一种多元自变量的模式,采用一些现代的数据处理方法和建模技术,为化工过程的一些物理量,如物料的粒度、固含率(也称为浓度,下同)建立软测量模型。应用这些模型,可以通过声发射信号,实现对流化床或搅拌釜内物料粒度或浓度的软测量,算法不仅明确,而且精度高,具有重要的学术意义和应用价值。具体开展了以下几个方面的研究工作,并取得了相应的成果。
1)系统回顾了颗粒特征参数的测量方法,声发射技术用于颗粒特征参数检测的现状及其在化工过程中的研究进展,声发射信号的处理方法。
2)介绍了小波分析和小波包分析的基本理论,并应用相关理论实现对声发射信号的多尺度分解与重构。讨论了声发射信号的小波及小波包去噪算法、最优小波包分解等信号处理技术。介绍了现代集成建模技术,并以声发射信号为媒介,用于检测流化床或搅拌釜内颗粒的特征参数。
3)通过小波分析、主成分分析和神经网络,建立了声发射信号与流化床颗粒平均粒度的量化关系。将采集到的声发射信号进行小波分析(waveletanalysis,WLA)或小波包分析(waveletpacket analysis,WLPA),获得低频细节信号与高频细节信号的能量,构建能量模式。主成分分析(Principal ComponentsAnalysis,PCA)用于消除变量之间的复相关性,并可减少变量个数,然后以得到的主成分作为神经网络的输入,以颗粒平均粒度作为网络的输出,建立回归的多层前向神经网络(Multi-Layer Feed Forward Neural Network,MLFN)模型,并讨论了影响模型精度的一些影响因素。实验结果表明,所建立的基于Sym8小波分解与主成分分析的多层前向神经网络(Sym8 WLA-PCA-MLFN)模型能实现对颗粒粒度的软测量,精度很高。
对声发射信号用Haar小波包二尺度分解,得到四维能量模式。进而建立基于Haar小波包分解的径向基神经网络(Radical Base Function Neural Network)模型(Haar WLPA-RBFN)或者与主成分分析集成的径向基神经网络模型(HaarWLPA-PCA-RBFN),二种模型都能取得较好的计算精度。所采用的正则化RBFN模型构造方便,只需要确定一个参数即可。
4)建立了利用声发射信号对搅拌釜内颗粒粒度进行分类的识别模型,首先对声发射信号用sym2小波包二尺度分解,以细节信号的能量构造模式样本,实施标准差标准化后用于判别分析,以逐步判别分析方法和马氏统计量对变量进行检验和筛选。所用判别分析方法有贝叶斯(Bayes)方法和马氏距离(Mahalanobis distance,MDis)方法。在搅拌釜转速与浓度一定的条件下,所建立的Sym2 WLPA-Bayes或Sym2 WLPA-MDis模型可根据声发射信号,对搅拌釜内的物料粒度实现正确的分类。
在转速与物料的粒度组成一定条件下,应用该模型也可以正确地对釜内物料浓度进行分类。
5)提出Sym2 WLPA-PCA-LSSVM的模型,用于多种粒度、多种物料浓度条件下,根据声发射信号对搅拌釜内物料浓度或粒度分类。讨论了预报精度的一些影响因素。所提出的Sym2 WLPA-PCA-LSSVM的分类模型,应用于搅拌釜内物料浓度与粒度分类时,其预报与自检精度均优于Sym2 WLPA-PCA-MDis判别分析法。
Sym2 WLPA-PCA-LSSVM模型也可以用于流化床粒度检测,预报结果也具有很高的精确度。
基于LSSVM的模型适合于个体数量较小的样本,并且不存在神经网络的过拟合与欠学习问题,具有较好的应用前景。
6)对缺少先验知识的数据集,常采用聚类分析,进行前导性研究,本章对不同条件下的声发射信号数据进行了聚类分析,从中得到一些颇有意义的启示。例如,在物料浓度与其它工艺参数一定的条件下,声发射信号能量模式的空间分布结构按平均粒度清晰地聚类,说明可以用一般的统计方法建立声发射信号能量模式与颗粒平均粒度之间的分类模型。
采用Sym2小波包对声发射信号进行二尺度分解,从细节信号求出标准化的能量模式,谱系聚类结果表明,在浓度一定条件下,能量模式与搅拌釜内物料粒度有较好的对应关系。类似地,粒度一定时,聚类结果也能近似地反映出物料的浓度类别。
针对两个相邻粒度范围之间没有清晰的分割边界,以及FCM聚类算法容易收敛到局部极小,提出Sym2 WLPA-GA-FCM聚类算法,应用于搅拌釜声发射信号聚类分析,在粒度组成不变或浓度一定的条件下,基于GA-FCM的聚类分析结果能较好地反映声发射信号个体的类属。
无论是谱系聚类还是GA-FCM算法,在对多种浓度与多种粒度的声发射信号进行聚类分析时,聚类结果难以反映声发射信号个体的实际浓度类属,表明统计分析方法难以由声发射信号能量模式建立釜内物料浓度的分类模型。
Hydrodynamics parameters of fluidization, behaviour of reaction and properties in fluidized bed reactors are strongly affected by the movement and characters of the particles. And the characters of the particles would change with the development of reaction process. Fluctuation of the layer height and the particle size has strongly effect on some buildingup reactions. It is therefore vital important to measure parameters on line and study on the hydromechanical behavior of the fluidized bed. Besides, batch stirrred-tank reactors are also common-used facility for synthesizing chemicals such as granular sodium percarbonate.The marked characteristic of these reaction processes is that the the characteristic parameters can not remain stationary, it would change dynamically according to the reaction conditions. Until now, it is still a challenge task to measure the particle size or concentration of such dynamic system on line by traditional method.
To measure the characteristic parameters by passive acoustic emission (AE) signals is a novel measuring technology due to its sensitive, environmental security, non-contact, non-invasive and real-time online features.This dissertation centered on how to measure the average particle size or solid concentration(also named as concentration) by asessing the AE signals that originated from the fluidized bed or stirring vessel. The AE signals were firstly decomposed by wavelet (or wavelet packet). Energies had been extracted from the details and approximations of AE signals. Soft sensing model employecd with modern data processing and modeling technology had finally been established for determining parameters such as the average particle size in a fluidized bed or concentration in a stirring vessel. These algorithms were not only unambiguous but also achieved high accuracy. There are significant applied cost and technical value to measure the particle size or concentration by AE signals by employing these soft sensing models. The dissertation centered on the following points and achieved corresponding achievements:
1) Particle size measuring technology had been reviewed. The state of the arts of measuring characteristic parameters by AE signals as well as the processing method for AE signals had been discussed.
2) Theory on wavelet or waveletpacket analysis as well as AE signal decomposing, denoising and reconstructing had been introduced. Algorithm on how to denoise AE signals by classic wavelet or wavepacket had also been discussed. State of the ats of modern modeling technique and its application in characteristic parameters determining by AE signals had been reviewed.
3) Average particle size measuring technology in a model fluidized bed by passive AE signals had been proposed by principal component analysis, neural networks and wavelet (wavepacket) decomposing. The relationship between acoustic emission signals and average particle size had been established. The AE signals originated by various particle sizes were decomposed by wavelet analysis (WLA) or wavepacket analysis (WLPA). Energies, the summations of absolute wavelet coefficients of detail signals and approximation signals, were used as recognition pattern. Principal components analysis (PCA) had been used to eliminate the complex relativity and the number of variables. A multi-layer feed forward neural network (MLFN) for regression had been established, in which the principal components were used as inputs of the neural networks and the average particle size was used as output. Factors such as the type of the wavelet or the decomposed level that influence on the prediction accuracy had been investigated. The results showed that the Sym8 WLA-PCA-MLFN model achieved high accuracy on average particle size regression by acoustic emission signals.
Four-dimensional energy pattern had been finally obtained after the original AE signals had been 2-level decomposed by Haar waveletpacket. Both the radical base function neural network based on Haar waveletpacket analysis and principal components analysis (Haar WLPA-PCA- RBFN) and the Haar WLPA-RBFN model achieved high accuracy when they were used to predict the average particle size in a model fluidized bed. The regularization radical base neural networks can be constructed easily due to only one parameter is needed.
4) Models had been propounded for classifying average particle size in a stirring vessel by acoustic emission signals. The AE signals had been 2-level decomposed by Sym2 WLP. Standardized energy pattern had been obtained after the absolute coefficients of the detail signals were summed. The variables had been selected and verified by stepwise discriminate analysis and mahalanobis statistic. The Sym2 WLPA-Bayes model or the Sym2 WLPA-MDis model achieved high accuracy when they were used to classify the average particle size in a stirring vessel according to the AE signals on condition that the concentration and rotating rate remained changeless.
It could also be used to predict the concentration of a stirring vessel on condition that the rotating speed and the average particle size remained changless.
5) A Sym2 WLPA-PCA-LSSVM (least square support vectors machine) model had been proposed for classifying the average particle sizes or concentrations according to the AE signals originated from multi-concentrations and multi-particle sizes of a stirring vessel. Factors that influence on the precision had been discussed. The accuracy of prediction as well as validation of this Sym2 WLPA-PCA-LSSVM model is superior to discriminant analysis model of the Sym2 WLPA-PCA-MDis.
The Sym2 WLPA-PCA-LSSVM model could also achieve high accuracy when it was applied to measure the average particle size of the fluidized bed.
The LSSVM model is suitable even if the number of the individual of a training set is not large enough. It had no bearing up on the problem of "overfitting" or "lack of study".
6) On condition that the relationship between AE signals and concentration or average particle size in a stirring vessel was unclear, Cluster analysis was often needed to the datasets that lack of prior knowledge. Meaningful revelations had been obtained through cluster analysis. For example, there was clear cluster structure between average particle size and energy pattern of AE signals on condition that the concentration and other parameters remain changeless. Statistical method was therefore suitable for relating AE signals and average particle size in a stirring vessel.
AE signals had been firstly 2-level decomposed by waveletpacket of Sym2, standard energies obtained from the detail signals were used for cluster analysis. Results of hierarchical clustering showed there was good relationship between energies and the average particle sizes on condition that the concentration remained changeless. Similarly, Results of hierarchical clustering also indicated that the energies had relationship with the concentrations if the average particle size and other parameters remained changeless.
There wasn't distinguishable boundary between two adjacent average particle sizes, and the FCM algorithm often converged to local minima, A Sym2 WLPA-GA-FCM algorithm had been proposed for clustering AE signals. Clustering results based on GA-FCM algorithm were in agreement with practical class if the average particle size or the concentration remained changeless.
Neither hierarchical clustering nor GA-FCM clustering analysis showed there was clear relationship between the AE signals and the concentrations if the AE signals had been originated from multi-concentration and multi-particle size. This indicated that the relationship between the AE signals and the concentrations could not be classified by statistical methods.
利用被动声发射信号检测反应器内物料的特征参数具有检测灵敏、安全高效、不侵入流场、实时在线的优点。本文以声发射信号作为测量流化床或搅拌釜内物料特征参数的媒介,通过小波或小波包对声发射信号进行时频多尺度分析,由分解结果构建一种多元自变量的模式,采用一些现代的数据处理方法和建模技术,为化工过程的一些物理量,如物料的粒度、固含率(也称为浓度,下同)建立软测量模型。应用这些模型,可以通过声发射信号,实现对流化床或搅拌釜内物料粒度或浓度的软测量,算法不仅明确,而且精度高,具有重要的学术意义和应用价值。具体开展了以下几个方面的研究工作,并取得了相应的成果。
1)系统回顾了颗粒特征参数的测量方法,声发射技术用于颗粒特征参数检测的现状及其在化工过程中的研究进展,声发射信号的处理方法。
2)介绍了小波分析和小波包分析的基本理论,并应用相关理论实现对声发射信号的多尺度分解与重构。讨论了声发射信号的小波及小波包去噪算法、最优小波包分解等信号处理技术。介绍了现代集成建模技术,并以声发射信号为媒介,用于检测流化床或搅拌釜内颗粒的特征参数。
3)通过小波分析、主成分分析和神经网络,建立了声发射信号与流化床颗粒平均粒度的量化关系。将采集到的声发射信号进行小波分析(waveletanalysis,WLA)或小波包分析(waveletpacket analysis,WLPA),获得低频细节信号与高频细节信号的能量,构建能量模式。主成分分析(Principal ComponentsAnalysis,PCA)用于消除变量之间的复相关性,并可减少变量个数,然后以得到的主成分作为神经网络的输入,以颗粒平均粒度作为网络的输出,建立回归的多层前向神经网络(Multi-Layer Feed Forward Neural Network,MLFN)模型,并讨论了影响模型精度的一些影响因素。实验结果表明,所建立的基于Sym8小波分解与主成分分析的多层前向神经网络(Sym8 WLA-PCA-MLFN)模型能实现对颗粒粒度的软测量,精度很高。
对声发射信号用Haar小波包二尺度分解,得到四维能量模式。进而建立基于Haar小波包分解的径向基神经网络(Radical Base Function Neural Network)模型(Haar WLPA-RBFN)或者与主成分分析集成的径向基神经网络模型(HaarWLPA-PCA-RBFN),二种模型都能取得较好的计算精度。所采用的正则化RBFN模型构造方便,只需要确定一个参数即可。
4)建立了利用声发射信号对搅拌釜内颗粒粒度进行分类的识别模型,首先对声发射信号用sym2小波包二尺度分解,以细节信号的能量构造模式样本,实施标准差标准化后用于判别分析,以逐步判别分析方法和马氏统计量对变量进行检验和筛选。所用判别分析方法有贝叶斯(Bayes)方法和马氏距离(Mahalanobis distance,MDis)方法。在搅拌釜转速与浓度一定的条件下,所建立的Sym2 WLPA-Bayes或Sym2 WLPA-MDis模型可根据声发射信号,对搅拌釜内的物料粒度实现正确的分类。
在转速与物料的粒度组成一定条件下,应用该模型也可以正确地对釜内物料浓度进行分类。
5)提出Sym2 WLPA-PCA-LSSVM的模型,用于多种粒度、多种物料浓度条件下,根据声发射信号对搅拌釜内物料浓度或粒度分类。讨论了预报精度的一些影响因素。所提出的Sym2 WLPA-PCA-LSSVM的分类模型,应用于搅拌釜内物料浓度与粒度分类时,其预报与自检精度均优于Sym2 WLPA-PCA-MDis判别分析法。
Sym2 WLPA-PCA-LSSVM模型也可以用于流化床粒度检测,预报结果也具有很高的精确度。
基于LSSVM的模型适合于个体数量较小的样本,并且不存在神经网络的过拟合与欠学习问题,具有较好的应用前景。
6)对缺少先验知识的数据集,常采用聚类分析,进行前导性研究,本章对不同条件下的声发射信号数据进行了聚类分析,从中得到一些颇有意义的启示。例如,在物料浓度与其它工艺参数一定的条件下,声发射信号能量模式的空间分布结构按平均粒度清晰地聚类,说明可以用一般的统计方法建立声发射信号能量模式与颗粒平均粒度之间的分类模型。
采用Sym2小波包对声发射信号进行二尺度分解,从细节信号求出标准化的能量模式,谱系聚类结果表明,在浓度一定条件下,能量模式与搅拌釜内物料粒度有较好的对应关系。类似地,粒度一定时,聚类结果也能近似地反映出物料的浓度类别。
针对两个相邻粒度范围之间没有清晰的分割边界,以及FCM聚类算法容易收敛到局部极小,提出Sym2 WLPA-GA-FCM聚类算法,应用于搅拌釜声发射信号聚类分析,在粒度组成不变或浓度一定的条件下,基于GA-FCM的聚类分析结果能较好地反映声发射信号个体的类属。
无论是谱系聚类还是GA-FCM算法,在对多种浓度与多种粒度的声发射信号进行聚类分析时,聚类结果难以反映声发射信号个体的实际浓度类属,表明统计分析方法难以由声发射信号能量模式建立釜内物料浓度的分类模型。
Hydrodynamics parameters of fluidization, behaviour of reaction and properties in fluidized bed reactors are strongly affected by the movement and characters of the particles. And the characters of the particles would change with the development of reaction process. Fluctuation of the layer height and the particle size has strongly effect on some buildingup reactions. It is therefore vital important to measure parameters on line and study on the hydromechanical behavior of the fluidized bed. Besides, batch stirrred-tank reactors are also common-used facility for synthesizing chemicals such as granular sodium percarbonate.The marked characteristic of these reaction processes is that the the characteristic parameters can not remain stationary, it would change dynamically according to the reaction conditions. Until now, it is still a challenge task to measure the particle size or concentration of such dynamic system on line by traditional method.
To measure the characteristic parameters by passive acoustic emission (AE) signals is a novel measuring technology due to its sensitive, environmental security, non-contact, non-invasive and real-time online features.This dissertation centered on how to measure the average particle size or solid concentration(also named as concentration) by asessing the AE signals that originated from the fluidized bed or stirring vessel. The AE signals were firstly decomposed by wavelet (or wavelet packet). Energies had been extracted from the details and approximations of AE signals. Soft sensing model employecd with modern data processing and modeling technology had finally been established for determining parameters such as the average particle size in a fluidized bed or concentration in a stirring vessel. These algorithms were not only unambiguous but also achieved high accuracy. There are significant applied cost and technical value to measure the particle size or concentration by AE signals by employing these soft sensing models. The dissertation centered on the following points and achieved corresponding achievements:
1) Particle size measuring technology had been reviewed. The state of the arts of measuring characteristic parameters by AE signals as well as the processing method for AE signals had been discussed.
2) Theory on wavelet or waveletpacket analysis as well as AE signal decomposing, denoising and reconstructing had been introduced. Algorithm on how to denoise AE signals by classic wavelet or wavepacket had also been discussed. State of the ats of modern modeling technique and its application in characteristic parameters determining by AE signals had been reviewed.
3) Average particle size measuring technology in a model fluidized bed by passive AE signals had been proposed by principal component analysis, neural networks and wavelet (wavepacket) decomposing. The relationship between acoustic emission signals and average particle size had been established. The AE signals originated by various particle sizes were decomposed by wavelet analysis (WLA) or wavepacket analysis (WLPA). Energies, the summations of absolute wavelet coefficients of detail signals and approximation signals, were used as recognition pattern. Principal components analysis (PCA) had been used to eliminate the complex relativity and the number of variables. A multi-layer feed forward neural network (MLFN) for regression had been established, in which the principal components were used as inputs of the neural networks and the average particle size was used as output. Factors such as the type of the wavelet or the decomposed level that influence on the prediction accuracy had been investigated. The results showed that the Sym8 WLA-PCA-MLFN model achieved high accuracy on average particle size regression by acoustic emission signals.
Four-dimensional energy pattern had been finally obtained after the original AE signals had been 2-level decomposed by Haar waveletpacket. Both the radical base function neural network based on Haar waveletpacket analysis and principal components analysis (Haar WLPA-PCA- RBFN) and the Haar WLPA-RBFN model achieved high accuracy when they were used to predict the average particle size in a model fluidized bed. The regularization radical base neural networks can be constructed easily due to only one parameter is needed.
4) Models had been propounded for classifying average particle size in a stirring vessel by acoustic emission signals. The AE signals had been 2-level decomposed by Sym2 WLP. Standardized energy pattern had been obtained after the absolute coefficients of the detail signals were summed. The variables had been selected and verified by stepwise discriminate analysis and mahalanobis statistic. The Sym2 WLPA-Bayes model or the Sym2 WLPA-MDis model achieved high accuracy when they were used to classify the average particle size in a stirring vessel according to the AE signals on condition that the concentration and rotating rate remained changeless.
It could also be used to predict the concentration of a stirring vessel on condition that the rotating speed and the average particle size remained changless.
5) A Sym2 WLPA-PCA-LSSVM (least square support vectors machine) model had been proposed for classifying the average particle sizes or concentrations according to the AE signals originated from multi-concentrations and multi-particle sizes of a stirring vessel. Factors that influence on the precision had been discussed. The accuracy of prediction as well as validation of this Sym2 WLPA-PCA-LSSVM model is superior to discriminant analysis model of the Sym2 WLPA-PCA-MDis.
The Sym2 WLPA-PCA-LSSVM model could also achieve high accuracy when it was applied to measure the average particle size of the fluidized bed.
The LSSVM model is suitable even if the number of the individual of a training set is not large enough. It had no bearing up on the problem of "overfitting" or "lack of study".
6) On condition that the relationship between AE signals and concentration or average particle size in a stirring vessel was unclear, Cluster analysis was often needed to the datasets that lack of prior knowledge. Meaningful revelations had been obtained through cluster analysis. For example, there was clear cluster structure between average particle size and energy pattern of AE signals on condition that the concentration and other parameters remain changeless. Statistical method was therefore suitable for relating AE signals and average particle size in a stirring vessel.
AE signals had been firstly 2-level decomposed by waveletpacket of Sym2, standard energies obtained from the detail signals were used for cluster analysis. Results of hierarchical clustering showed there was good relationship between energies and the average particle sizes on condition that the concentration remained changeless. Similarly, Results of hierarchical clustering also indicated that the energies had relationship with the concentrations if the average particle size and other parameters remained changeless.
There wasn't distinguishable boundary between two adjacent average particle sizes, and the FCM algorithm often converged to local minima, A Sym2 WLPA-GA-FCM algorithm had been proposed for clustering AE signals. Clustering results based on GA-FCM algorithm were in agreement with practical class if the average particle size or the concentration remained changeless.
Neither hierarchical clustering nor GA-FCM clustering analysis showed there was clear relationship between the AE signals and the concentrations if the AE signals had been originated from multi-concentration and multi-particle size. This indicated that the relationship between the AE signals and the concentrations could not be classified by statistical methods.
引文
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