SIMULATION OF GRAVITATIONAL SOLIDS FLOW PROCESS AND ITS PARAMETERS ESTIMATION BY THE USE OF ELECTRICAL CAPACITANCE TOMOGRAPHY AND ARTIFICIAL NEURAL NETWORKS

Hela Garbaa

hgarbaa@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science (Poland)

Lidia Jackowska-Strumiłło


Lodz University of Technology, Institute of Applied Computer Science (Poland)

Krzysztof Grudzień


Lodz University of Technology, Institute of Applied Computer Science (Poland)

Andrzej Romanowski


Lodz University of Technology, Institute of Applied Computer Science (Poland)

Abstract

The paper presents a new approach to monitoring changes of characteristic parameters of gravitational solids flow. Electrical Capacitance Tomography (ECT) is applied for non-invasive process monitoring. Artificial Neural Networks (ANN) are used to estimate important flow parameters knowing the measured capacitances. The proposed approach solves the ECT inverse problem in a direct manner and provides a rapid parameterization of the funnel flow. The simulation of the silo discharging process is performed relying on real flow behaviour obtained from the authors’ previous work. The simulated data are used to new approach testing and verification. The obtained results proved that proposed ANN-based method will allow for on-line gravitational solids flow monitoring.


Keywords:

Electrical Capacitance Tomography, process simulation, Artificial Neural Networks, funnel flow parameters estimation

Fiderek P., Wajman R., Kucharski J.: The Fuzzy System for Recognition and Control of the two Phase Gas- Liquid Flows. IAPGOS, 4/2015, 7¬–¬11.
  Google Scholar

Garbaa H., Jackowska-Strumiłło L., Grudzień K., Romanowski A.: Neural network approach to ECT inverse problem solving for estimation of gravitational solids flow. Proc. of the 2014 Federated Conference on Computer Science and Information Systems (FedCSIS 2014), AAIA’14, Vol. 2, Sep. 7–10, 2014, Warsaw, Poland, 19–26 [DOI:10.15439/2014F368].
  Google Scholar

Grudzień K., Romanowski A., Aykroyd R.G., Williams R.A., Mosorov V.: Parametric Modelling Algorithms in Electrical Capacitance Tomography for Multiphase Flow Monitoring, IEEE, MEMSTECH'2006, May 2006, Lviv-Polyana, Ukraine, 24–27 [DOI: 10.1109/MEMSTECH.2006.288675].
  Google Scholar

Haykin S.: Neural Networks: a comprehensive foundation – 2nd ed. Prentice Hall, 1999.
  Google Scholar

Isaksen Ø.: A review of reconstruction techniques for capacitance tomography. Meas. Sci. Technol. 7/1996, 325–33.
  Google Scholar

Jackowska-Strumillo L., Sokolowski J., Żochowski A., Henrot A: On Numerical Solution of Shape Inverse Problems. Computational Optimization and Applications 23/2002, 231–255.
  Google Scholar

Lei J., Liu S.: Dynamic Inversion Approach for Electrical Capacitance Tomography. IEEE Transactions On Instrumentation And Measurement 11/2013, 3035–3049.
  Google Scholar

Lei J., Liu S., Wang X., Liu Q.: An Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Robust Principle Component Analysis. Sensors 13/2013, 2076–2092.
  Google Scholar

Lionheart W.R.B.: Review: Developments in EIT reconstruction algorithms: pitfalls, challenges and recent development. Physiol. Meas. 25/2004, 125–142.
  Google Scholar

Ratajewicz-Mikolajczak E., Sikora J.: Neural networks method for identification of the objects behind the screen, IEEE Trans Med Imaging 6/2002, 613–619.
  Google Scholar

Rautenbach C., Mudde R.F., Yang X., Melaaen M.C., Halvorsen B.M.: A comparative study between electrical capacitance tomography and time-resolved X-ray tomography. Flow Measurement and Instrumentation 30/2013, 34–44.
  Google Scholar


  Google Scholar

Romanowski A., Grudzień K., Williams R.A.: Analysis and Interpretation of Hopper Behaviour Using ECT. Part. Part. Syst. Charact. 3-4/2006, 297–305.
  Google Scholar

Smolik W., Radomski D.: The matlab’s toolbox for iterative image reconstruction in electrical capacitance tomography. 5th Int. Symp. on Process tomography (Poland), 98–103.
  Google Scholar

Stasiak M., Sikora J., Filipowicz S.F., Nita K.: Principal component analysis and artificial neural network approach to electrical impedance tomography problems approximated by multi-region boundary element method. Engineering Analyses with Boundary Elements 31/2007, 713–720.
  Google Scholar

Warsito W., Fan L.S.: Development of 3-Dimensional Electrical Capacitance Tomography Based on Neural Network Multi-criterion Optimization Image Reconstruction. Proc. of 3rd World Congress on Industrial Process Tomography (Banff) 2003, 942–947.
  Google Scholar

Download


Published
2016-05-10

Cited by

Garbaa, H. ., Jackowska-Strumiłło, L., Grudzień, K. ., & Romanowski, A. (2016). SIMULATION OF GRAVITATIONAL SOLIDS FLOW PROCESS AND ITS PARAMETERS ESTIMATION BY THE USE OF ELECTRICAL CAPACITANCE TOMOGRAPHY AND ARTIFICIAL NEURAL NETWORKS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 6(2), 34–37. https://doi.org/10.5604/20830157.1201314

Authors

Hela Garbaa 
hgarbaa@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Lidia Jackowska-Strumiłło 

Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Krzysztof Grudzień 

Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Andrzej Romanowski 

Lodz University of Technology, Institute of Applied Computer Science Poland

Statistics

Abstract views: 204
PDF downloads: 60


Most read articles by the same author(s)