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Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography – a hybrid approach
 
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Online publication date: 2023-01-27
 
 
Publication date: 2023-01-27
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(1):11
 
HIGHLIGHTS
  • Combination of two types of electrical tomography (capacitance and impedance).
  • Verification of the advantage of hybrid tomography over homogeneous methods.
  • Application of the LSTM network to solve the inverse problem in electrical tomography.
  • The original approach to tomographic measurements as a data sequence for the LSTM network.
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ABSTRACT
The article presents a new concept for monitoring industrial tank reactors. The presented concept allows for faster and more reliable monitoring of industrial processes, which increases their reliability and reduces operating costs. The innovative method is based on electrical tomography. At the same time, it is non-invasive and enables the imaging of phase changes inside tanks filled with liquid. In particular, the hybrid tomograph can detect gas bubbles and crystals formed during industrial processes. The main novelty of the described solution is the simultaneous use of two types of electrical tomography: impedance and capacitance. Another novelty is the use of the LSTM network to solve the tomographic inverse problem. It was made possible by taking the measurement vector as a data sequence. Research has shown that the proposed hybrid solution and the LSTM algorithm work better than separate systems based on impedance or capacitance tomography.
 
CITATIONS (9):
1.
SENSOR PLATFORM OF INDUSTRIAL TOMOGRAPHY FOR DIAGNOSTICS AND CONTROL OF TECHNOLOGICAL PROCESSES
Krzysztof Król, Tomasz Rymarczyk, Konrad Niderla, Edward Kozłowski
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
 
2.
Applying Logistic Regression with Elastic Net and PCA to Determine the Objects Location in EIT
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2023 International Interdisciplinary PhD Workshop (IIPhDW)
 
3.
Influence of loss function on training the LSTM network in wall moisture tomography
Tomasz Rymarczyk, Monika Kulisz, Grzegorz Kłosowski, Maria Mognaschi
International Journal of Applied Electromagnetics and Mechanics
 
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Ultrasound Brain Tomography: Comparison of Deep Learning and Deterministic Methods
Manuchehr Soleimani, Tomasz Rymarczyk, Grzegorz Kłosowski
IEEE Transactions on Instrumentation and Measurement
 
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Detection and Determination of User Position Using Radio Tomography with Optimal Energy Consumption of Measuring Devices in Smart Buildings
Michał Styła, Edward Kozłowski, Paweł Tchórzewski, Dominik Gnaś, Przemysław Adamkiewicz, Jan Laskowski, Sylwia Skrzypek-Ahmed, Arkadiusz Małek, Dariusz Kasperek
Energies
 
6.
Complex system for analysis and monitoring of technological processes based on tomography
Cezary Figura, Krzysztof Król, Grzegorz Bartnik, Mariusz Kowalczuk, Ewa Golec, Piotr Czarnecki
Journal of Modern Science
 
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Feedback system for reactor process analysis
Paweł Kaleta, Krzysztof Król, Marek Opielak, Michał Jarmuł, Ryszard Nowak, Sebastian Zupok
Journal of Modern Science
 
8.
Poster: The Concept of an Ultrasensitive Industrial Ultrasound Scanner Using Hilbert and Wavelet Transforms in a Machine Learning Model
Grzegorz Kłosowski, Tomasz Rymarczyk, Manuchehr Soleimani, Konrad Niderla
Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems
 
9.
Poster: The Use of Machine Learning in Electrical Impedance Tomography—A Variable Frequency Approach
Monika Kulisz, Tomasz Rymarczyk, Grzegorz Kłosowski, Konrad Niderla, Michał Oleszek
Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems
 
eISSN:2956-3860
ISSN:1507-2711
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