Fast and Precise Soft-Field Electromagnetic Tomography Systems for Multiphase Flow Imaging

• Published in: Energies (Basel) Vol. 11; no. 5; p. 1199
• Main Authors: Mallach, Malte, Gevers, Martin, Gebhardt, Patrik, Musch, Thomas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2018
• Subjects: Data acquisition; Data processing; Electric power distribution; Electrical impedance; electrical impedance tomography; Electrical properties; Electromagnetic properties; finite element method; Frames per second; Gauss–Newton method; Image acquisition; Image processing; Information processing; Medical imaging; microwave tomography; Multiphase flow; multiphase flow imaging; Normal distribution; NOSER algorithm; Optimization; Signal processing; soft-field electromagnetic tomography; Spatial discrimination; Spatial resolution; Tomography
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11051199

In the process industry, measurement systems are required for process development and optimization, as well as for monitoring and control. The processes often involve multiphase mixtures or flows that can be analyzed using tomography systems, which visualize the spatial material distribution within a certain measurement domain, e.g., a process pipe. In recent years, we studied the applicability of soft-field electromagnetic tomography methods for multiphase flow imaging, focusing on concepts for high-speed data acquisition and image reconstruction. Different non-intrusive electrical impedance and microwave tomography systems were developed at our institute, which are sensitive to the local contrasts of the electrical properties of the materials. These systems offer a very high measurement and image reconstruction rate of up to 1000 frames per second in conjunction with a dynamic range of up to 120 dB. This paper provides an overview of the underlying concepts and recent improvements in terms of sensor design, data acquisition and signal processing. We introduce a generalized description for modeling the electromagnetic behavior of the different sensors based on the finite element method (FEM) and for the reconstruction of the electrical property distribution using the Gauss–Newton method and Newton’s one-step error reconstructor (NOSER) algorithm. Finally, we exemplify the applicability of the systems for different measurement scenarios. They are suitable for the analysis of rapidly-changing inhomogeneous scenarios, where a relatively low spatial resolution is sufficient.