厚管壁电容层析成像传感器特性研究

电容层析成像技术利用多电极电容传感器实现对多相流电介质分布进行可视化.通过对管道内径为10 mm,管壁厚度为5 mm的8电极电容传感器的数值模拟,研究了管道介电常数、管内介质介电常数以及径向电极对传感器电容绝对值、空满管电容变化值和传感器线性度的影响,分析了厚管壁下电容传感器的灵敏场特性,并给出了在高径极比条件下传感器设计的参数优化方案,为电容层析成像检测电路的设计提供相应的依据.     

基于交流法的ECT成像系统的硬件设计

为了实现多相流流动过程的多参数测量,设计了一套用于气固两相流检测的双截面32电极电容层析成像系统。系统以STM32F4为开发平台,硬件部分设计了信号发生器电路、电容/电压(C/V)转换电路、信号调制解调电路、A/D转换电路、D/A转换电路、ARM与CPLD及其外围电路。系统采用交流法电容检测原理实现具有32个独立电容检测通道的电容测量电路,将采集的2层截面图像做相关计算,来获取截面成像的固相速度分布。最后对ECT成像系统的线性度和数据采集稳定性进行分析,经实验表明,该系统具有较高的一致性和稳定性。     

基于双截面ECT的气/固两相流参数检测系统

为了实现多相流的流动过程在线监测和多参数测量,设计并研发了一套应用于气/固两相流检测的双截面8电极电容层析成像系统.采用交流法电容检测原理实现具有16个独立电容检测通道的电容测量电路,实测系统的成像速度为590帧/秒.将双截面电容层析成像系统用于气/固两相流的实时检测,并将两层截面的图像作相关计算,获取成像截面的固相速度分布.由固相速度和浓度最终算出气/固两相流的固相质量流量,从而实现气/固两相流浓度、速度、流量等多参数的综合检测.将双截面ECT传感器算得的质量流量与发料罐重力传感器获取的质量流量作对比发现,两者具有较好的一致性,流量测量误差小于8％.     

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.     

电容层析成像硬件系统

提出了一种基于电荷放大原理的应用于电容层析成像的电容测量电路,在一个测量周期中,该电路仅对被测电容进行一次充放电即可完成电容测量.设计了基于此电路的数据采集系统.实验表明,该系统具有采样速度高和线性度高的特点.对12电极系统的数据采集速度达到了600幅/秒,线性度达到0.9991.     

循环流化床上ECT传感器灵敏场的有限元分析

电容层析成像传感器的敏感场受多相流介质分布的影响,软场特性给图像重建带来很大困难,为了提高重建图像质量,对敏感场分布进行分析是非常必要的.该文以12电极方形电容传感器为基础,对电容层析成像技术应用于循环流化床研究做了简单阐述,着重研究了用有限元法对方形电容传感器敏感场分布的计算机仿真,讨论了负敏感场的处理方法.     

软测量技术在多相流检测中的应用

本文综述了软测量技术在多相流检测中的应用.在简要阐述软测量技术基本原理和特点的基础上,介绍了基于机理分析、人工智能、过程层析成像和动态信号分析等多相流检测领域中的软测量方法,并分析了目前该技术应用中存在的问题.     

两相流电容层析成像中图像重建的研究

电容层析成像(Electrical Capacitance Tomography,ECT)技术是目前最具发展前景的多相流参数检测方法,它可重建两相流在其流经管道横截面上的分布图像.对12电极的电容传感器进行了数学描述,建立了电容敏感场分布的数学模型及其有限元模型,分析了敏感场的算法,利用有限元法实现了敏感场的仿真,并且研究了敏感场分布的规律.在此基础上利用灰度图像重建的方法重现出不同流体分布状态下油管横截面的灰度图像,为两相流在线检测仪器的设计提供了理论依据.     

电阻层析成像系统的硬件设计

电阻层析成像是利用多相介质具有不同的电导率,通过传感器测得数据,运用适当的算法重建电导率分布的一种过程层析成像技术。针对现有的成像技术存在的成本高、响应速度慢、不能测量导体的问题,设计了一种新型高精度电阻层析成像系统。硬件部分主要设计了DDS信号产生电路、电流源产生电路、微弱电压信号接收与调理电路、RS485通讯以及STM32F407和CPLD最小系统电路。实际实验搭建了固液两相流测量装置,结果表明该系统成像精度高、实时性好。     

一种电容层析成像系统结构的优化方法

介绍了电容层析成像的基本原理,针对现有电容层析成像系统存在的问题,提出了一种总线型、模块化的微弱电容测量电路新结构,对电容层析成像系统的结构进行了优化,以使该技术更适应工业现场应用。     

Study on electrodynamic sensor of multi-modality system for multiphase flow measurement

Accurate measurement of multiphase flows, including gas/solids, gas/liquid, and liquid/liquid flows, is still challenging. In principle, electrical capacitance tomography (ECT) can be used to measure the concentration of solids in a gas/solids flow and the liquid (e.g., oil) fraction in a gas/liquid flow, if the liquid is non-conductive. Electrical resistance tomography (ERT) can be used to measure a gas/liquid flow, if the liquid is conductive. It has been attempted to use a dual-modality ECT/ERT system to measure both the concentration profile and the velocity profile by pixel-based cross correlation. However, this approach is not realistic because of the dynamic characteristics and the complexity of multiphase flows and the difficulties in determining the velocities by cross correlation. In this paper, the issues with dual modality ECT/ERT and the difficulties with pixel-based cross correlation will be discussed. A new adaptive multi-modality (ECT, ERT and electro-dynamic) sensor, which can be used to measure a gas/solids or gas/liquid flow, will be described. Especially, some details of the electrodynamic sensor of multi-modality system such as sensing electrodes optimum design, electrostatic charge amplifier, and signal processing will be discussed. Initial experimental results will be given.     

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。     

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.     

Adaptive filtering techniques for velocity estimation of tomographic electric capacitance signals of two-phase gas/oil flows

The development of adaptive real-time flow velocity estimation algorithms for two-phase flows can contribute to monitoring the pipelines of various complex processes, such as energy, chemical, petroleum and nuclear industries. Among the different non-invasive tomography techniques, electrical capacitance tomography (ECT) is gaining increasing attention for its potential use in real-time imaging and characterization of multiphase flow systems. The nature of ECT signals for two-phase flows can significantly degrade the velocity estimation process with cross-correlation approaches. We address the unique challenges of such signals and propose a preprocessing technique to improve the performance and robustness of the velocity estimation algorithm. Two adaptive filters are used to estimate the velocity of a two-phase type flow. A least mean square (LMS) and a fast block LMS (FBLMS) are used to model the time delay between the two signals captured by the twin sensor (ECT). Performance of the proposed technique is assessed by applying it to ECT data obtained from an experimental flow rig. The computed estimates are then compared with the calculated velocity from tracking motion of bubbles captured by a high speed camera monitoring the two phase flow in the pipe. Results show that the proposed technique provides consistent results across various flow patterns, and is advantageous compared to cross-correlation based techniques, specially for chaotic flow conditions. Furthermore, the proposed estimation algorithms can be applied to other electric based tomographic techniques.     

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LS-SVM的油气两相流空隙率测量方法的研究

利用12电极电容层析成像系统电容传感器获取的66个电容测量值,基于最小二乘支持向量机（LS-SVM）算法,提出了两相流空隙率在线测量的新方法。该方法用LS-SVM来建立空隙率测量模型。在实际测量时,首先归一化ECT获取的电容测量值,然后将归一化电容值输入已经建立的空隙率模型即可计算出空隙率。实验结果表明：该方法是有效的,避免了复杂耗时的图像重建过程,测量误差在6%以内,测量时间小于0.08 s。     

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。     

电容层析成像两相流流型可视化系统

本文基于电容层析成像技术研制了一套两相流型可视化系统,建立了一种新型的具有高速采集和高分辨率测量的电容数据采集系统,采用加权反投影算法进行图像重建以保证流型可视化的实时性和有效性,整套流型可视化系统经气液两相流现场应用测试和气固流化床实验室动态实验证明是有效的,成像速度达20幅／秒以上,满足工业应用要求。     

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。     

电容层析成像系统中径向电极的研究

由于电容层析成像系统中电容测量非常困难,常采用在相邻电极间嵌入径向电极的方法来改善电容敏感场的分布.文中对8电极电容层析成像系统进行了具体的仿真实验和理论分析,并对径向电极及其嵌入尺寸对电容传感器的电容值及敏感场分布的影响进行了研究,根据各项参数对电容传感器性能的影响以及传感器结构参数的优化目标函数,确定了径向电极的最优嵌入尺寸.实验结果表明:采用径向电极最优嵌入尺寸的电容层析成像系统,能够缩小测量电容值的动态变化范围,降低数据采集电路的设计难度,同时还能够提高电容响应的灵敏度,改善电容传感器敏感场的分布,提高电容层析成像系统的性能.