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Special Issue "Tomographic Sensors for Industrial Process Control"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editor

Prof. Dr. Uwe Hampel
E-Mail Website
Guest Editor
Helmholtz-Zentrum Dresden-Rossendorf, Head Experimental Thermal Fluid Dynamics, Bautzner Landstraße 400, 01328 Dresden, Germany
Technische Universitaet Dresden, Chair of Imaging Techniques in Energy and Process Engineering, 01062 Dresden, Germany
Interests: thermal fluid dynamics; multiphase flow measurement; process tomography

Special Issue Information

Dear Colleagues,

Control systems in the process industry have the task of ensuring a stable processing of material streams within a well-defined sequence of operations. Such operations are, for instance, reaction, separation, crystallization, solidification, mixing, and drying. For that purpose, the contemporary process industry typically employs control systems with local sensors for, e.g., temperature, pressure, flow, and filling level. With the ongoing progress in sensor development, there is now a growing interest in using sensors with higher complexity in industrial control systems. One such category is process tomography sensors.

Process tomography is an established class of imaging techniques, used to obtain 2D or 3D information of the distribution and flow of materials in pipes and vessels. Compared to its counterparts in medical diagnostics and non-destructive testing, process tomography typically targets high scanning speed rather than high spatial resolution. In recent years, a number of different process tomography modalities have evolved. These are, for example, electrical tomography, magnetic tomography, ultrasound tomography, microwave tomography, and optical tomography. However, some of the classical tomography modalities, such as X-ray tomography, emission tomography, and magnetic resonance imaging, have also been made fast enough to study industrial processes.

With respect to industrial process control, tomographic imaging had so far been of lesser consideration as real time reconstruction and feature extraction was difficult to achieve. However, recent developments in powerful and smart massive parallel computing architectures have changed the game. Process tomography can now be turned into a powerful sensor element for tomography-based process control.

The Special Issue invites researchers from different fields to publish their latest scientific and technical achievements in the field of process control using process tomography techniques. The focus of this Special Issue is on a holistic demonstration of this technology for typical industrial processes, preferably in the fields of chemical, environmental, and energy engineering. Demonstration may be in real industrial systems or in a laboratory environment. Presented technical solutions should include tomography sensors, data processing, and control strategies with at least a proof of principle. Contributions may furthermore deal with new concepts for hardware and software, e.g., real-time tomographic sensing and data processing as well as novel theoretical control concepts for the use of tomography sensors in control loops.

Prof. Dr. Uwe Hampel
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process tomography sensors
  • industrial process control
  • control systems in process engineering
  • real-time image and data processing
  • feature extraction
  • parallel computing

Published Papers (6 papers)

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Research

Article
An Electromagnetic Time-Reversal Imaging Algorithm for Moisture Detection in Polymer Foam in an Industrial Microwave Drying System
Sensors 2021, 21(21), 7409; https://doi.org/10.3390/s21217409 - 08 Nov 2021
Viewed by 576
Abstract
Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A [...] Read more.
Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A decomposition of the time-reversal operator (DORT) algorithm with a proper Green’s function of multilayered media is utilized to localize the moisture location. The derived Green’s function can be applied to the media with low or high contrast layers. It is shown that the time-reversal imaging (TRI) with the proposed Green’s function can be applied to the multilayered media with a moderately rough surface. Moreover, a single frequency TRI is proposed to decrease the measurement time. Numerical results for different moisture scenarios are presented to demonstrate the efficacy of the proposed method. The developed method is then tested on the experimental data for different moisture scenarios from our developed MWT experimental prototype. Image reconstruction results show promising capabilities of the TRI algorithm in estimating the moisture location in the polymer foam. Full article
(This article belongs to the Special Issue Tomographic Sensors for Industrial Process Control)
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Article
System Identification of Conveyor Belt Microwave Drying Process of Polymer Foams Using Electrical Capacitance Tomography
Sensors 2021, 21(21), 7170; https://doi.org/10.3390/s21217170 - 28 Oct 2021
Viewed by 470
Abstract
The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power [...] Read more.
The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power levels of the microwave sources. A moisture controller can be designed to achieve this goal; however, a process model is required to design model-based controllers. Since complex physics governs the microwave drying process, system identification tools are employed in this paper to exploit the process input and output information and find a simplified yet accurate model of the process. The moisture content of the foam that is the process output is measured using a designed electrical capacitance tomography (ECT) sensor. The ECT sensor estimates the 2D permittivity distribution of moving foams, which correlates with the foam moisture. Experiments are conducted to collect the ECT measurements while giving different inputs to the microwave sources. A state-space model is estimated using one of the collected datasets and is validated using the other datasets. The comparison between the model response and the actual measurements shows that the model is accurate enough to design a controller for the microwave drying process. Full article
(This article belongs to the Special Issue Tomographic Sensors for Industrial Process Control)
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Article
An Ultrasound Tomography Method for Monitoring CO2 Capture Process Involving Stirring and CaCO3 Precipitation
Sensors 2021, 21(21), 6995; https://doi.org/10.3390/s21216995 - 21 Oct 2021
Cited by 2 | Viewed by 484
Abstract
In this work, an ultrasound computed tomography (USCT) system was employed to investigate the fast-kinetic reactive crystallization process of calcium carbonate. USCT measurements and reconstruction provided key insights into the bulk particle distribution inside the stirred tank reactor and could be used to [...] Read more.
In this work, an ultrasound computed tomography (USCT) system was employed to investigate the fast-kinetic reactive crystallization process of calcium carbonate. USCT measurements and reconstruction provided key insights into the bulk particle distribution inside the stirred tank reactor and could be used to estimate the settling rate and settling time of the particles. To establish the utility of the USCT system for dynamical crystallization processes, first, the experimental imaging tasks were carried out with the stirred solid beads, as well as the feeding and stirring of the CaCO3 crystals. The feeding region, the mixing process, and the particles settling time could be detected from USCT data. Reactive crystallization experiments for CO2 capture were then conducted. Moreover, there was further potential for quantitative characterization of the suspension density in this process. USCT-based reconstructions were investigated for several experimental scenarios and operating conditions. This study demonstrates a real-time monitoring and fault detection application of USCT for reactive crystallization processes. As a robust noninvasive and nonintrusive tool, real-time signal analysis and reconstruction can be beneficial in the development of monitoring and control systems with real-world applications for crystallization processes. A diverse range of experimental studies shown here demonstrate the versatility of the USCT system in process application, hoping to unlock the commercial and industrial utility of the USCT devices. Full article
(This article belongs to the Special Issue Tomographic Sensors for Industrial Process Control)
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Article
Real-Time Fault Detection and Diagnosis of CaCO3 Reactive Crystallization Process by Electrical Resistance Tomography Measurements
Sensors 2021, 21(21), 6958; https://doi.org/10.3390/s21216958 - 20 Oct 2021
Cited by 1 | Viewed by 450
Abstract
In the present research work, an electrical resistance tomography (ERT) system is utilized as a means for real-time fault detection and diagnosis (FDD) during a reactive crystallization process. The calcium carbonate crystallization is part of the carbon capture and utilization scheme where process [...] Read more.
In the present research work, an electrical resistance tomography (ERT) system is utilized as a means for real-time fault detection and diagnosis (FDD) during a reactive crystallization process. The calcium carbonate crystallization is part of the carbon capture and utilization scheme where process monitoring and malfunction diagnostics strategies are presented. The graphical logic representation of the fault tree analysis methodology is used to develop the system failure states. The measurement consistency due to the use of a single electrode from a set of ERT electrodes for malfunction identification is experimentally and quantitatively investigated based on the sensor sensitivity and standard deviation criteria. Electrical current measurements are employed to develop a LabVIEW-based process automation program by using the process-specific knowledge and historical process data. Averaged electrical current is correlated to the mechanical failure of the stirrer through standard deviation evaluation, and slopes of the measured data are used to monitor the pump and concentrations status. The performance of the implemented methodology for detecting the induced faults and abnormalities is tested at different operating conditions, and a basic signal-based alarming technique is developed. Full article
(This article belongs to the Special Issue Tomographic Sensors for Industrial Process Control)
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Article
Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System
Sensors 2021, 21(20), 6919; https://doi.org/10.3390/s21206919 - 19 Oct 2021
Cited by 1 | Viewed by 506
Abstract
The article presents an application of microwave tomography (MWT) in an industrial drying system to develop tomographic-based process control. The imaging modality is applied to estimate moisture distribution in a polymer foam undergoing drying process. Our Leading challenges are fast data acquisition from [...] Read more.
The article presents an application of microwave tomography (MWT) in an industrial drying system to develop tomographic-based process control. The imaging modality is applied to estimate moisture distribution in a polymer foam undergoing drying process. Our Leading challenges are fast data acquisition from the MWT sensors and real-time image reconstruction of the process. Thus, a limited number of sensors are chosen for the MWT and are placed only on top of the polymer foam to enable fast data acquisition. For real-time estimation, we present a neural network-based reconstruction scheme to estimate moisture distribution in a polymer foam. Training data for the neural network is generated using a physics-based electromagnetic scattering model and a parametric model for moisture sample generation. Numerical data for different moisture scenarios are considered to validate and test the performance of the network. Further, the trained network performance is evaluated with data from our developed prototype of the MWT sensor array. The experimental results show that the network has good accuracy and generalization capabilities. Full article
(This article belongs to the Special Issue Tomographic Sensors for Industrial Process Control)
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Article
Supporting Visualization Analysis in Industrial Process Tomography by Using Augmented Reality—A Case Study of an Industrial Microwave Drying System
Sensors 2021, 21(19), 6515; https://doi.org/10.3390/s21196515 - 29 Sep 2021
Cited by 1 | Viewed by 632
Abstract
Industrial process tomography (IPT) based process control is an advisable approach in industrial heating processes for improving system efficiency and quality. When using it, appropriate dataflow pipelines and visualizations are key for domain users to implement precise data acquisition and analysis. In this [...] Read more.
Industrial process tomography (IPT) based process control is an advisable approach in industrial heating processes for improving system efficiency and quality. When using it, appropriate dataflow pipelines and visualizations are key for domain users to implement precise data acquisition and analysis. In this article, we propose a complete data processing and visualizing workflow regarding a specific case—microwave tomography (MWT) controlled industrial microwave drying system. Furthermore, we present the up-to-date augmented reality (AR) technique to support the corresponding data visualization and on-site analysis. As a pioneering study of using AR to benefit IPT systems, the proposed AR module provides straightforward and comprehensible visualizations pertaining to the process data to the related users. Inside the dataflow of the case, a time reversal imaging algorithm, a post-imaging segmentation, and a volumetric visualization module are included. For the time reversal algorithm, we exhaustively introduce each step for MWT image reconstruction and then present the simulated results. For the post-imaging segmentation, an automatic tomographic segmentation algorithm is utilized to reveal the significant information contained in the reconstructed images. For volumetric visualization, the 3D generated information is displayed. Finally, the proposed AR system is integrated with the on-going process data, including reconstructed, segmented, and volumetric images, which are used for facilitating interactive on-site data analysis for domain users. The central part of the AR system is implemented by a mobile app that is currently supported on iOS/Android platforms. Full article
(This article belongs to the Special Issue Tomographic Sensors for Industrial Process Control)
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