基于SVM及电容层析成像的两相流流型识别

两相流测量中,流型的准确识别是流动参数准确测量的基础.电容层析成像(ECT)技术是自20世纪80年代发展起来的新型检测技术,可用于两相流/多相流流型识别及固相浓度测量.支持向量机是一种基于统计学习理论的机器学习算法,即使在小样本情况下也能得到很好的分类效果.应用ECT系统测量的包含流型信息的电容测量数据,采用支持向量机算法进行流型识别,对4种典型空气-油两相流流型识别分别进行了仿真和静态实验.结果表明,该方法辨识速度快,可准确地识别典型的流型.     

基于粗糙集理论两相流流型辨识方法研究

本文提出了一种基于粗糙集理论和电容层析成像系统辨识两相流流型的新方法。该法通过对极板电容测量数据进行属性分析，然后根据粗糙集理论化简属性，最后根据分类规则进行流型辨识。本流型辨识方法决策规则简单适合在线测量。仿真结果表明：此方法在两相流流型辨识中具有较高的判别精度。也为两相流流型提供了一种有效的手段。     

基于自组织神经网络的油气两相流管截面流型辨识

为了探索两相流流型辨识方法,本文在12电极电容检测系统的基础上,针对油气两相流,提出了基于神经网络的流型辨识方法,即采用自组织神经网络根据电容采样数据实现相应管截面流型的识别.研究表明,该方法是有效的.     

双层电容层析成像传感器及应用

电容层析成像在气力输送过程中具有十分重要的应用地位，可进行固相浓度分布测量，流型判断。利用双层电容层析成像传感器不仅可以进行浓度测量还可以进行相关测速。基于这种思想，设计了一种双层电容传感器，与电容测量系统结合，用于气力输送过程固相浓度和速度的测量。     

气液两相流液相体积含量的精确测量

两相流多变的流型导致电容传感器测量组分体积含量时精度较低。仿真试验结果显示,管道气—油两相流含油率的测量误差高达１５％ 以上。利用层析成象电容传感器阵列获取的管道内部两相流分布情况的投影值,可确定对组分体积含量的修正方法,使误差显著降低。本文分析了流型对测量的影响,导出了修正公式,介绍两种修正方法及仿真修正结果。     

气液两相流型在线识别系统的开发和应用

在统计理论和小波理论的基础上,把神经网络应用到流型识别中,采用VB语言编程完成了软件的开发.按照信号测量、信号特征提取和流型识别3个实现流型客观自动识别的步骤,建立了一套气液两相流流型在线识别系统,在气液两相流实验系统上采集了180组仿真样本进行仿真实验.结果显示:流型正确识别率为91.7%,此系统应用于流型识别,不仅具有较高的识别率,而且很好地实现了识别结果的实时显示,运行速度快,达到了流型在线识别的目的.     

电容层析成像技术在气固两相流检测中的应用

电容层析成像是一种对绝缘介质内部状态实现可视化的技术.文中介绍了一种应用于气固高压密相输送的小口径电容层析成像系统.电容传感器采用8电极结构,内径为10 mm,管道内压力达2 MPa.基于交流法的电容检测电路可以实现高分辨率测量,交流和直流两级增益可调可以实现宽范围电容检测.系统的成像速度达100帧/s.     

基于ECT投影数据的两相流流型辨识

提出一种基于电容层析成像（ECT）系统的两相流流型辨识新方法。ECT投影数据经特征参数提取前处理环节后馈人事先训练好的神经网络实现流型辩识。文中介绍了特征参数的提取、神经网络的构成以及训练方法。研究结果表明，该方法辨识精度高、辨识速度快，是两相流流型在线辨识的一种有效手段。     

石油气液两相垂直管流流型仿真

本文以奥齐思泽斯基垂直管气液两相压力梯度预测为基础,对石油气液两相垂直管流流型分布进行研究.以Visual C 6.0为开发工具,借助OpenGL编程和计算机可视化技术,开发了动态仿真软件,对各流型存在范围进行可视化仿真,为提高气举采油效率和安装气泡破碎器提供技术支持.     

基于ERT技术的垂直管道两相流流型识别

在两相流测量问题的研究中 ,流型的准确识别是其它流动参数准确测量的基础。基于电学敏感原理的电阻层析成像技术 (ERT) ,由于可视化、无辐射、低成本等优点 ,在两相流动参数的检测中具有很广阔的发展前景。研究中以 ERT系统样机为测量工具 ,以垂直管道气 /液两相管流为研究对象。通过对模拟流动状态实验下几种典型流型的测量数据的采集、处理 ,采用模式识别中的特征提取方法降低数据维数 ,并利用数理统计和神经网络方法识别流型。研究结果表明采用距离判别法和径向基神经网络技术均得到较高的识别率     

用于两相流测量的ECT图像重构技术研究

电容层析成像技术(ECT)具有非侵入、响应速度快、成本低等优点,是用于两相流参数检测非常有发展潜力的技术之一。而图像重构是ECT系统研究的关键技术。该文利用有限元方法对12电极ECT系统进行建模仿真,进行正问题求解,获得了图像重构的样本数据;引入改进的径向基函数神经网络,建立了ECT图像重构算法,并在MATLAB平台上进行了仿真验证。结果表明,改进的径向基神经网络算法在图像重构准确度及速度方面有了明显提高。     

基于加权SVD截断共轭梯度的ECT图像重建算法

针对电容层析成像技术中的“软场”效应和病态问题,基于灵敏度矩阵的奇异值分解理论,提出了一种加权SVD截断共轭梯度的ECT图像重建算法,给出了算法的数学模型,完成了算法的收敛性分析和证明,并将其应用在电容层析成像系统的图像重建中。仿真和实验结果表明,同LBP算法和CG算法相比,该算法有成像效果好,成像速度快,易于实现等特点。     

基于改进Landweber算法的两相流检测的研究

电容层析成像(ECT)具有非侵入式,响应速度快,安全等优点,是用于两相流检测非常有发展潜力的技术之一.该文在介绍了电容层析成像基本原理之后,探讨了ECT应用改进的Landweber算法的可行性,该算法收敛速度较快,且成像精度高.仿真实验表明,改进的Landweber算法同Landweber算法相比较,只需要很少的迭代次...     

Image reconstruction of electrical capacitance tomography based on optimal simulated annealing algorithm using orthogonal test method

Electrical capacitance tomography (ECT) is a visualization measurement method for two-phase flow. Imaging permittivity distributions using electrical capacitance tomography has always been one of the most significant issues studied by scholars, and the algorithm will have a great impact on the accuracy of image reconstruction result. This paper applies simulated annealing (SA) algorithm to image reconstruction in ECT. However, some parameters of SA algorithm need to be optimized in order to obtain better reconstructed images in ECT. The influence of different parameter values in SA algorithm for image reconstruction in ECT is studied, and a set of optimal parameters of the SA algorithm is obtained based on the orthogonal experimental design method in this paper. At the same time, simulation and static experiments are conducted. Reconstructed images by SA algorithm with optimized parameter are compared with the linear back projection (LBP) and Landweber iterative algorithms. The results show that better images can be obtained for typical oil-gas two-phase flow using SA algorithm. The quality and shape fidelity of reconstructed image for the central object are obviously improved.     

Simulation of Image Fusion of Dual Modality (Electrical Capacitance and Optical Tomography) in Solid/Gas Flow

This paper presents a novel method of combining dual modality electrical capacitance and optical tomography for applications in monitoring and investigating solid/gas flow. The objective of this method is to obtain a good quality image of the full-scale concentration distribution of solid/gas flow. A new image reconstruction algorithm fused the dual modality images is developed and evaluated.     

Sectional void fraction measurement of gas-water two-phase flow by using a capacitive array sensor

The sectional void fraction measurement for multiphase flow is usually influenced by flow patterns. Inspired by electrical capacitance tomography (ECT) devices applied to flow imaging (whose measured capacitance data contain both the flow pattern and sectional void fraction information), a capacitive array sensor is developed to realize two functions, flow pattern recognition and void fraction measurement, simultaneously; so that the void fraction measurement can be conducted for a certain flow pattern and the measurement accuracy can be expected to be improved. The main idea of the proposed method can be described as: firstly, the proper feature vectors are extracted from the electrical signal to identify the flow pattern (the BPNN model with GDX learning algorithm is used for flow pattern identification); and then the average of electrical signal is applied to estimates the void fraction by the corresponding calibration curve. An experimental platform of air/water two-phase flow is built (on which 3 flow patterns can be generated stably) to test the performance of the proposed method. The results support the correctness and effectiveness of the proposed method.     

基于加权奇异值分解截断共轭梯度的电容层析图像重建

针对电容层析成像技术(ECT)中的"软场"效应和病态问题,提出了一种基于加权奇异值分解(SVD)截断共轭梯度的电容层析(ECT)图像重建算法。阐述了电容层析成像工作原理,提出了12电极ECT系统的测量方法。在分析灵敏度矩阵的奇异值分解理论的基础上,推导出了加权SVD截断共轭梯度的数学模型,并利用Tikhonov方法进行正则化加权处理。最后,分析了算法的收敛性,并将其应用于电容层析成像系统的图像重建中。实验结果表明,对于层流,截断共轭梯度算法的平均误差能达到27.54%,全部流型平均迭代步数达到13步,与LBP、Landweber和CG算法比较,该算法具有成像效果好,成像速度快,易于实现等特点。     

Data fusion in dual-mode tomography for imaging oil–gas two-phase flow

Process tomography (PT) techniques have been developed rapidly for visualizing the internal behavior of industrial processes, e.g. multi-phase flow measurement. Most of tomography systems employ a single measurement technique, such as computerized tomography (CT), optical tomography (OT), electrical resistance tomography (ERT) or electrical capacitance tomography (ECT). It is now possible to fit two or more tomographic systems to an industrial process. Detailed information from different modalities can be gained by inspection of separate tomographs, and the advantage of the strongest features provided by each unit can be taken. A combined tomogram can be produced of superior quality to any of the separate tomograms. To maximize the information available from the combined tomographic system, data fusion is the better option. In this paper, a dual-mode tomography system based on capacitance sensor and gamma sensor was developed to capture oil–gas two-phase flow. The two modalities can work at the same time. Two fusion methods, namely image fusion method and data fusion method, are proposed. Both simulation and static experiments for oil–gas two-phase flow were conducted. The reconstruction results of different fusion methods and modalities were compared and discussed.     

基于自适应模拟退火及 LM 联合反演算法的 ECT 图像重建

为解决电容层析成像技术(ECT)中图像重建的非线性和病态性问题,提出了一种自适应模拟退火-Levenberg Marquardt (ASA-LM)联合反演算法。 改进了标准模拟退火(SA)算法的新解生成策略、能量函数的定义及退火策略,并结合 LM 的直接局 部搜索方法联合反演 ECT 图像重建问题。 同时,利用 Savitzky-Golay (SG) 滤波对 ECT 图像重建所需电容数据进行平滑处理以 提高其信噪比。 最后,进行仿真及静态实验,并与线性反投影(LBP)、Landweber 迭代及标准 SA 算法进行了比较。 结果表明,与 其他 3 种算法相比,ASA-LM 算法收敛速度快、图像重建质量明显提高,边缘信息保真度高,重建图像的平均相对误差为 0. 331 1,平均相关系数为 0. 933 1。     

Identification of oil–gas two-phase flow pattern based on SVM and electrical capacitance tomography technique

The correct identification of two-phase flow patterns is the basis for the accurate measurement of other flow parameters in two-phase flow measurement. Electrical capacitance tomography (ECT) is a new visualization measurement technique for two-phase/multi-phase flows. The capacitance measurements obtained from the ECT system contain flow pattern information, and then six feature parameters are extracted. The support vector machine (SVM) has a desirable classification ability with fewer training samples. The inputs of the SVM are extracted feature parameters of different flow patterns. Simulation and static experiments were carried out for typical flow patterns. Results showed that this method is fast in speed and can identify these flow patterns correctly.