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Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors

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Warianty tytułu
PL
Nowatorskie metody neuronowej rekonstrukcji obrazów tomograficznych w eksploatacji zbiornikowych reaktorów przemysłowych
Języki publikacji
EN PL
Abstrakty
EN
The article presents an innovative concept of improving the monitoring and optimization of industrial processes. The developed method is based on a system of many separately trained neural networks, in which each network generates a single point of the output image. Thanks to the elastic net method, the implemented algorithm reduces the correlated and irrelevant variables from the input measurement vector, making it more resistant to the phenomenon of data noises. The advantage of the described solution over known non-invasive methods is to obtain a higher resolution of images dynamically appearing inside the reactor of artifacts (crystals or gas bubbles), which essentially contributes to the early detection of hazards and problems associated with the operation of industrial systems, and thus increases the efficiency of chemical process control.
PL
W artykule przedstawiono nowatorską koncepcję usprawnienia monitoringu i optymalizacji procesów przemysłowych. Opracowana metoda bazuje na systemie osobno wytrenowanych wielu sieci neuronowych, w którym każda sieć generuje pojedynczy punkt obrazu wyjściowego. Dzięki zastosowaniu metody elastic net zaimplementowany algorytm redukuje z wejściowego wektora pomiarowego zmienne skorelowane i nieistotne, czyniąc go bardziej odpornym na zjawisko zaszumienia danych. Przewagą opisywanego rozwiązania nad znanymi metodami nieinwazyjnymi jest uzyskanie wyższej rozdzielczości obrazów dynamicznie pojawiających się wewnątrz reaktora artefaktów (kryształów lub pęcherzy gazowych), co zasadniczo przyczynia się do wczesnego wykrycia zagrożeń i problemów związanych z eksploatacją systemów przemysłowych, a tym samym zwiększa efektywność sterowania procesami chemicznymi.
Rocznik
Strony
261--267
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
  • University of Economics and Innovation ul. Projektowa 4, 20-209 Lublin, Poland Research and Development Center, Netrix S.A. ul. Związkowa 26, 20-148 Lublin, Poland
  • Lublin University of Technology Department of Organization of Enterprise ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
Bibliografia
  • 1. Babout L, Grudzień K, Wiącek J, Niedostatkiewicz M, Karpiński B, Szkodo M. Selection of Material for X-Ray Tomography Analysis and DEM Simulations: Comparison between Granular Materials of Biological and Non-Biological Origins. Granular Matter 2018; 20 (3): 38, https://doi.org/10.1007/s10035-018-0809-y.
  • 2. Banasiak R, Wajman R, Sankowski D, Soleimani M. Three-Dimensional Nonlinear Inversion of Electrical Capacitance Tomography Data Using a Complete Sensor Model. Progress In Electromagnetics Research (PIER) 2010; 100: 219-234, https://doi.org/10.2528/ PIER09111201.
  • 3. Dusek J, Hladky D, Mikulka J. Electrical Impedance Tomography Methods and Algorithms Processed with a GPU. Progress In Electromagnetics Research Symposium - Spring (PIERS) 2017; 1710–14, https://doi.org/10.1109/PIERS.2017.8262025.
  • 4. Garbaa H, Jackowska-Strumiłło L, Grudzień K, Romanowski A. Application of Electrical Capacitance Tomography and Artificial Neural Networks to Rapid Estimation of Cylindrical Shape Parameters of Industrial Flow Structure. Archives of Electrical Engineering 2016; 65 (4): 657–69, https://doi.org/10.1515/aee-2016-0046.
  • 5. Grudzien K, Chaniecki Z, Romanowski A, Sankowski D, Nowakowski J, Niedostatkiewicz M. Application of Twin-Plane ECT Sensor for Identification of the Internal Imperfections inside Concrete Beams. IEEE International Instrumentation and Measurement Technology Conference Proceedings 2016; May, 1–6, https://doi.org/10.1109/I2MTC.2016.7520512.
  • 6. Kłosowski G, Gola A, Świć A. Application of Fuzzy Logic Controller for Machine Load Balancing in Discrete Manufacturing System. In International Conference on Intelligent Data Engineering and Automated Learning 2015; 256–63, https://doi.org/10.1007/978-3-319-24834-9_31.
  • 7. Kłosowski G, Rymarczyk T, Gola A. Increasing the Reliability of Flood Embankments with Neural Imaging Method. Applied Sciences 2018; 8 (9): 1457, https://doi.org/10.3390/app8091457.
  • 8. Kłosowski G, Rymarczyk T. Using neural networks and deep learning algorithms in electrical impedance tomography. Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska 2017; 7 (3): 99–102, https://doi.org/10.5604/01.3001.0010.5226.
  • 9. Korzeniewska E, Gałązka-Czarnecka I, Czarnecki A, Piekarska A, Krawczyk A. Influence of PEF on Antocyjans in Wine. Przegląd Elektrotechniczny 2018; 1 (1): 59–62, https://doi.org/10.15199/48.2018.01.15.
  • 10. Korzeniewska E, Walczak M, Rymaszewski J. Elements of Elastic Electronics Created on Textile Substrate, Proceedings of the 24th International Conference Mixed Design of Integrated Circuits and Systems - MIXDES 2017; 2017, 447-45, https://doi.org/10.23919/ MIXDES.2017.8005250.
  • 11. Kosicka E, Kozłowski E, Mazurkiewicz D. Intelligent Systems of Forecasting the Failure of Machinery Park and Supporting Fulfilment of Orders of Spare Parts. In: Burduk A., Mazurkiewicz D. (eds) Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. ISPEM 2017. Advances in Intelligent Systems and Computing, vol 637. Springer, Cham, 2018, https://doi.org/10.1007/978-3-319-64465-3_6.
  • 12. Kozłowski E., Mazurkiewicz D., Kowalska B., Kowalski, D. Binary Linear Programming as a Decision-Making Aid for Water Intake Operators. In: Burduk A., Mazurkiewicz D. (eds) Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. ISPEM 2017. Advances in Intelligent Systems and Computing, vol 637. Springer, Cham, 2018, https://doi.org/10.1007/978-3-319-64465-3_20.
  • 13. Kryszyn J, Smolik W T, Radzik B, Olszewski T, Szabatin R. Switchless Charge-Discharge Circuit for Electrical Capacitance Tomography. Measurement Science and Technology 2014; 25 (11): 115009, https://doi.org/10.1088/0957-0233/25/11/115009.
  • 14. Kryszyn J, Waldemar S. Toolbox for 3d Modelling and Image Reconstruction in Electrical Capacitance Tomography. Informatics Control Measurement in Economy and Environment Protection 2017; 7 (1).
  • 15. Lopato P, Tomasz C, Sikora R, Gratkowski S, Ziolkowski M. Full Wave Numerical Modelling of Terahertz Systems for Nondestructive Evaluation of Dielectric Structures. COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 2013; 32 (3): 736–49, https://doi.org/10.1108/03321641311305719.
  • 16. Majchrowicz M, Kapusta P, Jackowska-Strumiłło L, Sankowski D. Acceleration of image reconstruction process in the electrical capacitance tomography 3d in heterogeneous, multi-GPU system. Informatics Control Measurement in Economy and Environment Protection 2017; 7 (1): 37–41, https://doi.org/10.5604/01.3001.0010.4579.
  • 17. Mikulka J. Accelerated Reconstruction of T2 Maps in Magnetic Resonance Imaging. Measurement Science Review 2015; 4: 210–18, https://doi.org/10.1515/msr-2015-0029.
  • 18. Park S, Na J, Kim M, Lee J M. Multi-Objective Bayesian Optimization of Chemical Reactor Design Using Computational Fluid Dynamics. Computers & Chemical Engineering 2018; 119 : 25–37, https://doi.org/10.1016/j.compchemeng.2018.08.005.
  • 19. Psuj G. Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements. Sensors 18 (2): 292, https://doi.org/10.3390/s18010292.
  • 20. Romanowski A. Big Data-Driven Contextual Processing Methods for Electrical Capacitance Tomography. IEEE Transactions on Industrial Informatics 2018; 1–1, https://doi.org/10.1109/TII.2018.2855200.
  • 21. Rymarczyk T, Adamkiewicz P, Polakowski K, Sikora J. Effective Ultrasound and Radio Tomography Imaging Algorithm for Two-Dimensional Problems. Przegląd Elektrotechniczny 2018; 94 (6): 62–69.
  • 22. Rymarczyk T, Kłosowski G, Kozłowski E. A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings. Sensors 2018; 18 (7): 2285.
  • 23. Rymarczyk T, Kłosowski G. Application of Neural Reconstruction of Tomographic Images in the Problem of Reliability of Flood Protection Facilities. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20 (3): 425–34, https://doi.org/10.17531/ein.2018.3.11.
  • 24. Rymarczyk T, Sikora J. Applying Industrial Tomography to Control and Optimization Flow Systems. Open Physics 2018; 16 (1): 332–45, https://doi.org/10.1515/phys-2018-0046.
  • 25. Sobaszek Ł, Gola A, Świć A. Predictive Scheduling as a Part of Intelligent Job Scheduling System: in, 358–67. Springer, Cham 2018, https://doi.org/10.1007/978-3-319-64465-3_35.
  • 26. Soleimani M, Mitchell C N, Banasiak R, Wajman R, Adler A. Four-dimensional electrical capacitance tomography imaging using experimental data. Progress In Electromagnetics Research 2009; 90: 171–86, https://doi.org/10.2528/PIER09010202.
  • 27. Tian G, Yang B, Dong M, Zhu R, Yin F, Zhao X, Wang Y, Xiao W, Wang Q, Zhang W. The Effect of Temperature on the Microbial Communities of Peak Biogas Production in Batch Biogas Reactors. Renewable Energy 2018; 123: 15–25,https://doi.org/10.1016/j.renene.2018.01.119.
  • 28. Voutilainen A, Lehikoinen A, Vauhkonen M, Kaipio J P. Three-Dimensional Nonstationary Electrical Impedance Tomography with a Single Electrode Layer. Measurement Science and Technology 2010; 21 (3): 035107, https://doi.org/10.1088/0957-0233/21/3/035107.
  • 29. Wang Mi. Industrial Tomography: Systems and Applications. Edited by Elsevier Ltd. Woodhead Publishing 2015.
  • 30. Ziolkowski M, Gratkowski S, Zywica A R. Analytical and Numerical Models of the Magnetoacoustic Tomography with Magnetic Induction. COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 2018; 37 (2): 538–48, https://doi.org/10.1108/COMPEL-12-2016-0530.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2f1f9fd9-bc29-4842-9d6f-3713ddce2e3b
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