E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

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

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

Deadline for manuscript submissions: 31 May 2019

Special Issue Editors

Guest Editor
Prof. Dr. Manuchehr Soleimani

Department of Electronics & Electrical Engineering, University of Bath, BA2 7AY, UK
Website | E-Mail
Interests: tomographic imaging
Guest Editor
Dr. Thomas Wondrak

Helmholtz-Zentrum Dresden-Rossendorf, Institut für Fluiddynamik, Abt. Magnetohydrodynamik, Dresden 01328, Germany
Website | E-Mail
Interests: metal flow imaging
Guest Editor
Prof. Dr. Chao Tan

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Website | E-Mail
Interests: multi-phase flow

Special Issue Information

Dear Colleagues,

Industrial Process Tomography (IPT) is a set of multi-dimensional sensor technologies that aim to provide unparalleled internal information of industrial processes used in many business sectors. The World Congress on Industrial Process Tomography (WCIPT) is the flagship conference of the International Society for Industrial Process Tomography (ISIPT. www.isipt.org), which is held every two years. After successful previous events: UK (1999), Germany (2001), Canada (2003), Japan (2005), Norway (2007), China (2010), Poland (2013), and Brazil (2016), this year, WCIPT-2018 will take place in Bath, UK, under the chairmanship of Prof. Manuchehr Soleimani.

This congress focuses on the state-of-the-art of industrial process tomography and its applications in various fields:

  1. New Generation Systems for Wider Support of Industrial Applications:
    1. Multi-modal and multi-spectral methods addressing complex process distributions.
    2. Multi-dimensional systems that radically extend length and/or temporal scales. 
    3. Smart tomographic systems that provide direct application data, or process control.
    4. Human-machine interaction in IPT systems.
    5. Machine learning from IPT data.
  2. New Developments in Foundational System Elements for Enhanced Process Interaction:
    1. Excitation and response sensing methods and topologies for all modes: e.g. acoustic, electrical, hard radiation, magnetic resonance, and positron-emission.
    2. Data acquisition architectures to enhance system performance for focussed IPT products.
    3. Integrated system design and packaging for special application needs such as intrinsic safety.
    4. Raw data processing such as direct inversion and high-speed reconstruction methods.
    5. Interpretation data processing yielding industry relevant information.
  3. Pioneering Industrial Case Studies
    1. Holistic study of industrial application for pilot investigation.
    2. Holistic study of industrial application for online control.

Prof. Dr. Manuchehr Soleimani
Dr. Thomas Wondrak
Prof. Dr. Chao Tan
Guest Editors

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 1800 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

  • Industrial process tomography
  • Super-sensing
  • Soft field tomography
  • Hard field tomography

Published Papers (2 papers)

View options order results:
result details:
Displaying articles 1-2
Export citation of selected articles as:

Research

Open AccessArticle Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography
Sensors 2019, 19(7), 1521; https://doi.org/10.3390/s19071521
Received: 28 January 2019 / Revised: 15 March 2019 / Accepted: 25 March 2019 / Published: 28 March 2019
PDF Full-text (12824 KB) | HTML Full-text | XML Full-text
Abstract
The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and [...] Read more.
The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and reconstruction of data using electrical tomography. The novelty was the use of original machine learning algorithms. Their characteristic feature is the use of many separately trained subsystems, each of which generates a single pixel of the output image. Artificial Neural Network (ANN), LARS and Elastic net methods were used to solve the inverse problem. These algorithms have been modified by a corresponding increase in equations (multiply) for electrical impedance tomography using the finite element method grid. The Gauss-Newton method was used as a reference to machine learning methods. The algorithms were trained using learning data obtained through computer simulation based on real models. The results of the experiments showed that in the considered cases the best quality of reconstructions was achieved by ANN. At the same time, ANN was the slowest in terms of both the training process and the speed of image generation. Other machine learning methods were comparable with the deterministic Gauss-Newton method and with each other. Full article
Figures

Figure 1

Open AccessArticle Multiple Wire-Mesh Sensors Applied to the Characterization of Two-Phase Flow inside a Cyclonic Flow Distribution System
Sensors 2019, 19(1), 193; https://doi.org/10.3390/s19010193
Received: 25 November 2018 / Revised: 20 December 2018 / Accepted: 21 December 2018 / Published: 7 January 2019
PDF Full-text (7934 KB) | HTML Full-text | XML Full-text
Abstract
Wire-mesh sensors are used to determine the phase fraction of gas–liquid two-phase flow in many industrial applications. In this paper, we report the use of the sensor to study the flow behavior inside an offshore oil and gas industry device for subsea phase [...] Read more.
Wire-mesh sensors are used to determine the phase fraction of gas–liquid two-phase flow in many industrial applications. In this paper, we report the use of the sensor to study the flow behavior inside an offshore oil and gas industry device for subsea phase separation. The study focused on the behavior of gas–liquid slug flow inside a flow distribution device with four outlets, which is part of the subsea phase separator system. The void fraction profile and the flow symmetry across the outlets were investigated using tomographic wire-mesh sensors and a camera. Results showed an ascendant liquid film in the cyclonic chamber with the gas phase at the center of the pipe generating a symmetrical flow. Dispersed bubbles coalesced into a gas vortex due to the centrifugal force inside the cyclonic chamber. The behavior favored the separation of smaller bubbles from the liquid bulk, which was an important parameter for gas-liquid separator sizing. The void fraction analysis of the outlets showed an even flow distribution with less than 10% difference, which was a satisfactorily result that may contribute to a reduction on the subsea gas–liquid separators size. From the outcomes of this study, detailed information regarding this type of flow distribution system was extracted. Thereby, wire-mesh sensors were successfully applied to investigate a new type of equipment for the offshore oil and gas industry. Full article
Figures

Figure 1

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top