EXPERIMENTAL STUDY OF A CAPACITIVE TOMOGRAPHY SYSTEM FOR MULTIPHASE FLOW

This paper presents the experimental development of a capacitive tomography system applied to the study of multiphase flows. A capacitance sensor with eight electrodes and a capacitance measurement transducer were constructed. The two-phase flow void fraction was obtained through an electric-mechanical measurement system. The reconstruction of the image of several two-phase flows was obtained using the linear back projection method. Numerical simulation of the capacitance values between electrode pairs wereperformed, through the method of finite elements, in order to obtain the sensibility maps. This experimental procedure showed the influence of several parameters on the quality of the reconstructed images. The quality of the reconstructed images for air-water and water-oil flows, for different void fractions, demonstrated the validity of the tomography system developed

Selected Papers from the 9th World Congress on Industrial Process Tomography

Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies

Investigation of sine-wave inputs for an FDM EIT system

Includes bibliographical references.This thesis project report describes the research done by the author under the supervision of Prof. J. Tapson. The area of research is an investigation of sine-wave drive for a frequency division multiplexing (FDM) electrical impedance tomography (EIT) system. This thesis was commissioned by Prof. J. Tapson, on the 1 st of November 200 1. The goals were as follows: 1. Investigate the research done on this project by previous researchers. 2. Investigate the current applications in which capacitance, resistance and impedance tomography are used in research level and in industry. 3. Design and develop a working 8-electrode impedance tomography system. Also, make provisions for a possible upgrade of the 8-electrode system to a 16-electrode (16 capacitance and 16 resistance electrodes) system employing FDM and using sine-wave excitation. 4. VerifY and compare the performance of the 8-electode impedance tomography system to the previous research done by Teague [53], for static configurations of multi-phase air-gravel-seawater mixtures. 5. Evaluate the ability of the system to differentiate between air and gravel mass in static situations. 6. Draw conclusions regarding the performance, effectiveness and limitations of the system. 7. Make recommendations for future project developments. 8. Submit the thesis by the 28th of March 2003

Electrical Capacitance Volume Tomography: Design and Applications

This article reports recent advances and progress in the field of electrical capacitance volume tomography (ECVT). ECVT, developed from the two-dimensional electrical capacitance tomography (ECT), is a promising non-intrusive imaging technology that can provide real-time three-dimensional images of the sensing domain. Images are reconstructed from capacitance measurements acquired by electrodes placed on the outside boundary of the testing vessel. In this article, a review of progress on capacitance sensor design and applications to multi-phase flows is presented. The sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of three-dimensional capacitance sensors are illustrated. The article also highlights applications of ECVT sensors on vessels of various sizes from 1 to 60 inches with complex geometries. Case studies are used to show the capability and validity of ECVT. The studies provide qualitative and quantitative real-time three-dimensional information of the measuring domain under study. Advantages of ECVT render it a favorable tool to be utilized for industrial applications and fundamental multi-phase flow research

METODY PARAMETRYCZNE W ROZWIĄZYWANIU PROBLEMU ODWROTNEGO DLA MONITOROWANIA PRZEPŁYWÓW MATERIAŁÓW SYPKICH

The article presents the parametrisation-based methods of monitoring of the process of gravitational silo discharging with aid of capacitance tomography techniques. Proposed methods cover probabilistic Bayes’ modelling, including spatial and temporal analysis and Markov chain Monte Carlo methods as well as process parametrisation with artificial neural networks. In contrast to classical image reconstruction-based methods, parametric modelling allows to omit this stage as well as abandon the associated reconstruction errors. Parametric modelling enables the direct analysis of significant parameters of investigated process that in turn results in easier incorporation into the control feedback loop. Presented examples are given for the gravitational flow of bulk solids in silos.Niniejszy artykuł przedstawia parametryczne metody rozwiązywania problemu odwrotnego w tomografii pojemnościowej na przykładzie monitorowania procesu przepływu materiałów sypkich przy użyciu tomografii pojemnościowej. Wybrane metody obejmują modelowanie probabilistyczne Bayesa, w tym przestrzenne i czasowe oraz metody Monte Carlo łańcuchów Markowa, a także parametryzację procesu z użyciem sztucznych sieci neuronowych. W odróżnieniu od klasycznych metod opartych na algorytmach rekonstrukcji obrazu parametryzacja pozwala na pominięcie tego etapu, a co za tym idzie brak dodatkowych błędów związanych z rekonstrukcją. Parametryzacja pozwala na bezpośrednią analizę istotnych parametrów badanego procesu, przez co łatwiejsze jest użycie tych wyników w pętli sprzężenia zwrotnego sterowania. Przykłady rozpatrywane w tekście są opisane dla procesu grawitacyjnego opróżniania materiałów sypkich przechowywanych w silosach

Multiphase flow measurement and data analytic based on multi-modal sensors

Accurate multiphase flow measurement is crucial in the energy industry. Over the past decades, separation of the multiphase flow into single-phase flows has been a standard method for measuring multiphase flowrate. However, in-situ, non-invasive, and real-time imaging and measuring the key parameters of multiphase flows remain a long-standing challenge. To tackle the challenge, this thesis first explores the feasibility of performing time-difference and frequency-difference imaging of multiphase flows with complex-valued electrical capacitance tomography (CVECT). The multiple measurement vector (MMV) model-based CVECT imaging algorithm is proposed to reconstruct conductivity and permittivity distribution simultaneously, and the alternating direction method of multipliers (ADMM) is applied to solve the multi-frequency image reconstruction problem. The proposed multiphase flow imaging approach is verified and benchmarked with widely adopted tomographic image reconstruction algorithms. Another focus of this thesis is multiphase flowrate estimation based on low-cost, multi-modal sensors. Machine learning (ML) has recently emerged as a powerful tool to deal with time series sensing data from multi-modal sensors. This thesis investigates three prevailing machine learning methods, i.e., deep neural network (DNN), support vector machine (SVM), and convolutional neural network (CNN), to estimate the flowrate of oil/gas/water three-phase flows based on the Venturi tube. The improvement of CNN with the combination of long-short term memory machine (LSTM) is made and a temporal convolution network (TCN) model is introduced to analyse the collected time series sensing data from the Venturi tube installed in a pilot-scale multiphase flow facility. Furthermore, a multi-modal approach for multiphase flowrate measurement is developed by combining the Venturi tube and a dual-plane ECT sensor. An improved TCN model is built to predict the multiphase flowrate with various data pre-processing methods. The results provide guidance on data pre-processing methods for multiphase flowrate measurement and suggest that the proposed combination of low-cost flow sensing techniques and machine learning can effectively translate the time series sensing data to achieve satisfactory flowrate measurement under various flow conditions