电容层析成像技术在线测量气固流化床空隙率的研究

基于电容层析成像技术，提出了一种在线测量气固流化床空隙率的新方法。建立了相应的12电极电容层析成像气固流化床空隙率测量系统，可同时实现气固 流化床空隙率分布的在线显示和整体空隙率测量。选择加权反投影算法进行图像重建以保证空隙率分布显示的实时性和有效性。采用Tikhonov正则化原理和ART算法相结合的组合型新图像重建算法来实现整体空隙率的测量。Tikhonov正则化原理用于克服图像重建过程中的不适定问题，ART算法用于提高最终重建图像的质量。研究表明以上提出的空隙率测量新方法是有效的。空隙率分布在线测量的速度可达25幅／秒以上，整体空隙率测量的最大误差可小于5％。     

基于ECT和蚂蚁算法的油气两相流空隙率在线测量

基于电容层析成像系统（ECT）和蚂蚁算法，提出了一种油气两相流空隙率在线测量的新方法。该方法利用电容层析成像系统12电极电容传感器所获取的66个测量电容信息，首先根据电容层析成像系统所获取的流型辨识结果确定对应流型下的实际空隙率测量模型参数f和b，然后利用蚂蚁算法的信息素信息，找到当前测量状态下对空隙率起主要作用的组合电容集合和相应的权重系数，从而实现空隙率测量。与流型相对应的空隙率测量线性模型参数f和b基于先验数据通过最小二乘方法确定。油气两相流的实验结果表明，该方法对空隙率的在线测量是有效的，避免了复杂的图像重建计算，实时性能佳，测量时间小于0.08 s。在几种典型流型下，提出的空隙率测量方法与常用的快关阀方法相比最大测量偏差小于5.5%。     

一种用于在线测量的电容层析成像图像重建算法

提出了一种新的、快速的电容层析成像算法。在分析极小范数解的基础之上针对ECT逆问题的特点对其进行改进，并利用奇异值分解定理证明这种改进的数值稳定作用，从而从极小范数解的角度解决了ECT逆问题数值解的唯一性和稳定性问题；在此基础上从最优化的角度推导出进一步提高重建图像质量的公式；数值实验表明这种改进是有效的，其图像重建时间近似LBP，然而成像质量比LBP、Tikhonov和Landweber迭代法要好。     

卷积神经网络在PCS系统辅助诊断油井示功图的研究

近年来,随着中国石化油气生产运行指挥系统(PCS)的建设,越来越多的采油数据接入了系统,数据体量越来越大,由此为机器学习奠定了基础。本文利用卷积神经网络的经典模型Inception V3,通过建立各工况的示功图训练数据训练卷积神经网络的模型,在模型建立过程中不断修改训练参数和样本集数据,最终实现了卷积神经网络在PCS系统辅助诊断。     

一种新的电容层析成像电极组合激励测量模式

电容层析成像图像重建为非线性不适定反问题,其敏感场分布不均匀,可获得的测量数据有限,中心处物体的成像效果不佳。增加电极数目,可获得更多的电容测量数据,减小其不定性,同时改善敏感场分布。但同时导致电容测量值变小,测量精度下降。在保证电容测量精度的前提下,提出了24电极组合式电容层析成像传感器结构,研究了两种激励测量方案,并与传统12电极电容层析成像传感器进行了对比分析,包括电容测量值的大小及其动态测量范围、灵敏场分布的均匀性以及不同流型下的重建图像。仿真结果表明,与12电极电容层析成像传感器相比,采用24电极组合式电容层析成像传感器,其电容测量值大,可较好地保证测量精度,其灵敏度分布更加均匀,对中心处物体的成像质量明显提高。     

基于改进的LS-SVM测量油气两相流空隙率

利用ECT电容传感器提供的电容测量值,基于改进的最小二乘支持向量机(LS-SVM),提出了一种油气两相流空隙率测量的新方法.运用该方法测量空隙率时,以ECT电容传感器获取的66个独立电容值作为空隙率模型的输入,计算即可得空隙率.建模阶段,针对LS-SVM使用中存在的问题,首先运用训练数据筛选技巧,对LS-SVM进行稀疏化改进,接着引入实数编码的遗传算法(RC-GA)优化LS-SVM参数,然后运用改进后的LS-SVM和基于RC-GA的参数选取方法建立空隙率测量模型.所提出的空隙率测量方法省去了常用ECT方法测量空隙率时的复杂耗时的图像重建过程,提高了空隙率测量的实时性.实验结果表明,提出的LS-SVM改进和参数优化方法是有效的,提出的空隙率测量方法具有实时性佳的优点,测量精度满足实际应用要求.     

基于电容层析成像技术的流化床内固相体积分数分布特性研究

针对煤清洁转化过程流化床内复杂气固两相流动特性,采用电容层析成像(简称ECT)技术,考察了流化床内玻璃珠和树脂颗粒瞬时和时均的固相体积分数分布特性。结果表明,利用不同时刻的瞬时固相体积分数等值图,可实现对截面颗粒分布的量化检测;局部时均固相体积分数沿径向呈"中心低、边壁高"的对称分布,截面时均固相体积分数沿轴向呈"下浓上稀"分布,测量结果与经典理论和实验观察相一致,表明ECT方法可以定性与定量测定流化床内气固两相流动过程固相体积分数。     

基于电容层析成像和模糊模式识别的油气两相流流型辨识新方法的研究

基于电容层析成像和模糊模式识别技术别提出了一种油气两相流流型辨识的新方法。建立了12电极电容层析成像流型自动识别系统，该系统利用Tikhonov正则化原理并结合SIRT(Simultaneous Reconstruction Techniques)算法进行图像重建。Tikhonov正则化原理用于克服图像重建过程中的不适定问题，SIRT算法用于提高最终重建图像的质量。根据流型的随机和模糊特性，提出了一种根据管截面重建图像进行流型辨识的模糊流型判别方法。研究结果表明，提出的流型辨识新方法是有效的。对于层状流、核心流、环状流、均相流等流型，流型辨识的准确率高于95％，辨识一个流型所用的时间小于0.3秒。对于塞状流，流型辨识的准确率高于90％。     

融合电容层析成像先验信息的集合卡尔曼滤波统计估计

提出融合先验信息的电容层析成像(ECT)系统信息融合成像法——基于Bayesian重建算法的集合卡尔曼滤波融合算法(EnKF)。其优势在于能充分利用流体动力方面的先验信息,进一步修正原始迭代算法的重建图像质量。Bayesian重建算法在重建图像的迭代过程充分考虑噪声和图像的概率分布,完成初步成像过程。集合卡尔曼滤波算法利用先验信息实现对多相流流体流动的预测,继而改善成像质量。此外,为了便于估算误差的协方差,多相流的相分布作为EnKF融合方法的估计对象,用状态向量空间的灰度差分统计描述,明显改善了通过状态向量空间模型获得的统计估算结果。仿真和试验的结果都充分表明了在ECT系统中集合卡尔曼滤波融合方法的可行性。     

基于BP神经网络的天然气脱硫装置异常诊断模型

利用BP神经网络对天然气脱硫装置进行模拟研究。以某天然气净化厂实际运行数据及Hysys模拟数据作为网络训练数据集,利用Matlab建立脱硫单元异常情况诊断模型,用以评价装置运行异常情况下偏离正常工况的程度,并判断产生异常的原因。测试结果表明,训练后的神经网络模型对模拟数据预测准确度较好。诊断模型具有简捷性和准确性,可用于脱硫装置运行状况的分析和优化指导。     

Deep learning-based tomographic imaging of ECT for characterizing particle distribution in circulating fluidized bed

The gas and solids in a circulating fluidized bed (CFB) are heterogeneously dispersed and a multiscale flow regime may form both in time and space. Accurate measurement of the fluidizing process is significant for investigating the multiscale gas–solid flow characteristics and the design, optimization, and control of CFBs in various applications. This article develops a deep learning-based tomographic imaging of electrical capacitance tomography (ECT) to characterize the particle concentration distribution in a CFB. The deep tomographic imaging approach is realized through training a well-designed convolutional neural network (CNN) with the numerically built dataset. Simulation results demonstrate that the average values of the relative image errors reconstructed by CNN in the test set are 0.1110 and 0.1114 for the 60 and 100 mm pipes, respectively, which are better than the average values of 0.1819 and 0.2519 by the Landweber algorithm. With the verification of the trained model based on the prepared data can image the unseen typical flow patterns better than Landweber, it is further used to investigate the particle flow characteristics of a lab-scale CFB. Experimental results reveal that the developed deep tomographic imaging of ECT can successfully measure the fluidized particle distribution in both the 60 and 100 mm pipes, showing good prediction and generality of the designed CNN model. A flow regime transformation from “annular” flow to “core-annular” flow and pneumatic conveying is observed under the tested conditions. Besides, the flow regime would be highly affected by the fluidized gas flow rate and the initial bed height.     

基于改进的RBF网络和自适应小波图像增强的ECT两相流测量系统(英文)

Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance.To solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image enhancement.Firstly,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured.Then,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image feature-enhanced.Through multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be constructed.Finally,the image is reconstructed with adjusting wavelet coefficients.In this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm.Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].     

Reconstruction of electrical capacitance tomography images based on fast linearized alternating direction method of multipliers for two-phase flow system

Electrical capacitance tomography (ECT) has been applied to two-phase flow measurement in recent years. Image reconstruction algorithms play an important role in the successful applications of ECT. To solve the il-posed and nonlinear inverse problem of ECT image reconstruction, a new ECT image reconstruction method based on fast lin-earized alternating direction method of multipliers (FLADMM) is proposed in this paper. On the basis of theoretical analysis of compressed sensing (CS), the data acquisition of ECT is regarded as a linear measurement process of permittivity distribution signal of pipe section. A new measurement matrix is designed and L1 regularization method is used to convert ECT inverse problem to a convex relaxation problem which contains prior knowledge. A new fast alternating direction method of multipliers which contained linearized idea is employed to minimize the objective function. Simulation data and experimental results indicate that compared with other methods, the quality and speed of reconstructed images are markedly improved. Also, the dynamic experimental results in-dicate that the proposed algorithm can fulfil the real-time requirement of ECT systems in the application.     

ECT imaging of three-phase fluidized bed based on three-phase capacitance model

In this work, the electrical capacitance tomography (ECT) with neural network multi-criteria optimization image reconstruction technique (NN-MOIRT), early developed by the authors, is applied to imaging bubble column and three-phase fluidized bed systems in the real time manner. Air, norpar (paraffin) and glass-beads are used as the gas, liquid, and solid phases, respectively. A three-phase capacitance model coupled with a two-region model is proposed to attain the gas holdup and the solids fraction from the permittivity maps of the three-phase system. The two-region model assumes that the solids fraction in the emulsion phase in the no bubble region is the same as in the bubble region. The three-phase capacitance model combines series and parallel capacitance connections among gas, liquid and solid components to relate the three-phase permittivity to each phase holdup. A direct image calculation to obtain the gas holdup from the permittivity map of the three-phase system is also performed by determining the permittivity threshold for the gas bubbles. Comparisons of the gas holdup obtained by ECT with that obtained from liquid head measurement showed a good agreement, validating the applicability of the model and its associated image calculation.     

Verification of fluidized bed electrical capacitance tomography measurements with a fibre optic probe

Previous studies aimed at determining the spatial accuracy of electrical capacitance tomography (ECT) have employed phantoms placed within the ECT measurement space. No previous studies have compared ECT with a second independent measurement technique in an operating fluidized bed. In the present work, radial voidage profiles have been measured with ECT in the 0.14-m I.D. riser of a circulating fluidized bed (CFB) and in a bubbling fluidized bed with a 0.19-m I.D. The dynamic and time-averaged radial voidage profiles have been compared with measurements taken with a fibre optic probe in the same riser and in a slightly narrower (0.15-m I.D.) bubbling fluidized bed. In spite of the intrusiveness of the latter technique, the time-averaged radial profiles in the CFB riser fall within 10% of each other when the CFB is operated at high-flux conditions that lead to a very dense wall region. Iterative reconstruction of the ECT images is not needed in this case. Similar agreement is found between the two techniques in the bubbling fluidized bed, but off-line iterative image reconstruction is clearly necessary in this fluidization regime. These results suggest that ECT, which is often described as a tomographic imaging technique with low spatial resolution, can in fact provide semi-quantitative time-averaged images of the cross-section of fluidized beds of diameter comparable to or less than that used here.     

用于两相流流型显示和空隙率测量的电容层析成像技术

引　言两相流流型的实时显示和空隙率的在线测量对两相流系统的控制、运行乃至机理研究等均具有重要意义 ,一直是两相流领域中重要的研究方向 .现已提出多种实现流型显示和空隙率测量的方法 ,例如用于流型显示的核辐射线法和光学法 ,用于空隙率 (或浓度 )测量的机理估算法、核辐射线法、电学法、光学法、微波法、热学法和核磁共振法等 ,但是总的来讲还未能满足应用要求 ,实际应用的例子也较少[1] .电容层析成像 (ｅｌｅｃｔｒｉｃａｌｃａｐａｃｉｔａｎｃｅｔｏｍｏｇｒａｐｈｙ ,简记ＥＣＴ)技术由于可在不干扰流场的情况下获取反映两相流…     

Investigation of gas–solid bubbling fluidized beds using ECT with a modified Tikhonov regularization technique

Electrical capacitance tomography (ECT) provides a non‐intrusive means to visualize cross‐sectional material distribution of gas–solid bubbling fluidized beds. Successful application of ECT strongly depends on the image reconstruction algorithm used. For on‐line measurements of bubbling fluidized beds, employing an algorithm that can produce high‐quality images without extensive computation is necessary. Using the conventional Tikhonov regularization algorithm, image quality in the central area is basically satisfied but suffers from artifacts in the near‐wall region. To solve this problem, a similar division operation learned from linear back projection was introduced to modify the conventional Tikhonov algorithm. Both numerical simulations and experiments were performed to evaluate the modified technique. The results indicate that the artifacts can be effectively removed and the reconstructed image quality is similar to Landweber method with dozens of iterations. Furthermore, the modified Tikhonov technique shows high accuracy when obtaining important hydrodynamic parameters in gas–solid bubbling fluidized beds. © 2017 American Institute of Chemical Engineers AIChE J, 63: 29–41, 2018     

应用电容层析成像对多相流的动态可视化(英文)

Identifying the flow patterns is vital for understanding the complicated physical mechanisms in multiphase flows.For this purpose,electrical capacitance tomography(ECT) technique is considered as a promising visualization method for the flow pattern identification,in which image reconstruction algorithms play an important role.In this paper,a generalized dynamic reconstruction model,which integrates ECT measurement information and physical evolution information of the objects of interest,was presented.A generalized objective functional that simultaneously considers the spatial constraints,temporal constraints and dynamic evolution information of the objects of interest was proposed.Numerical simulations and experiments were implemented to evaluate the feasibility and efficiency of the proposed algorithm.For the cases considered in this paper,the proposed algorithm can well reconstruct the flow patterns,and the quality of the reconstructed images is improved,which indicates that the proposed algorithm is competent to reconstruct the flow patterns in the visualization of multiphase flows.