Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure

• Autorzy: Garbaa H. , Jackowska-Strumiłło L. , Grudzień K. , Romanowski A.

Identyfikatory

DOI

10.1515/aee-2016-0046

• Języki publikacji: EN

Abstrakty

EN

A new approach to solve the inverse problem in electrical capacitance tomography is presented. The proposed method is based on an artificial neural network to estimate three different parameters of a circular object present inside a pipeline, i.e. radius and 2D position coordinates. This information allows the estimation of the distribution of material inside a pipe and determination of the characteristic parameters of a range of flows, which are characterised by a circular objects emerging within a cross section such as funnel flow in a silo gravitational discharging process. The main advantages of the proposed approach are explicitly: the desired characteristic flow parameters are estimated directly from the measured capacitances and rapidity, which in turn is crucial for online flow monitoring. In a classic approach in order to obtain these parameters in the first step the image is reconstructed and then the parameters are estimated with the use of image processing methods. The obtained results showed significant reduction of computations time in comparison to the iterative LBP or Levenberg-Marquard algorithms.

Słowa kluczowe

EN

artificial neural networks; electrical capacitance tomography; flow parameters estimation; inverse problem

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2016
• Tom: Vol. 65, nr 4
• Strony: 657--669
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wz.

Twórcy

autor

Garbaa H.

• Lodz University of Technology, Institute of Applied Computer Science Stefanowskiego, 18/22, 90-924, Łódź, Poland
• REsearch Groups in Intelligent Machines (REGIM-Lab) University of Sfax, National School of Engineers (ENIS) BP 1173, Sfax 3038, Tunisia

autor

Jackowska-Strumiłło L.

• Lodz University of Technology, Institute of Applied Computer Science Stefanowskiego, 18/22, 90-924, Łódź, Poland

autor

Grudzień K.

• Lodz University of Technology, Institute of Applied Computer Science Stefanowskiego, 18/22, 90-924, Łódź, Poland

autor

Romanowski A.

• Lodz University of Technology, Institute of Applied Computer Science Stefanowskiego, 18/22, 90-924, Łódź, Poland

Bibliografia

• [1] Grudzien K., Romanowski A., Chaniecki Z. et al., Description of the silo flow and bulk solid pulsation detection using ECT, Flow Measurement and Instrumentation, vol. 21, pp. 198-206 (2010).
• [2] Zhang W., Wang C., Yang W., Wang C-H., Application of electrical capacitance tomography in particulate process measurement – A review, Advanced Powder Technology, vol. 25, pp. 174-188 (2014).
• [3] Jackowska-Strumillo L., Sokolowski J., Żochowski A., Henrot A., On Numerical Solution of Shape Inverse Problems. Computational Optimization and Applications, vol. 23, no. 2, pp. 231-255 (2002).
• [4] Isaksen Ø., A review of reconstruction techniques for capacitance tomography. Meas. Sci. Technol., vol. 7, pp. 325-333 (1996).
• [5] Gao R.X., Tang X., Gordon G., Kazmer D.O., Online product quality monitoring through in-process measurement, CIRP Annals - Manufacturing Technology, vol. 63, no. 1, pp. 493-496 (2014).
• [6] Tapp H.S., Peyton A.J., Kemsley E.K., Wilson R.H, Chemical engineering applications of electrical process tomography, Sensors and Actuators B: Chemical, vol. 92, no. 1-2, pp. 17-24 (2003).
• [7] Wahab Y.A., Rahim R.A., Rahiman M.H.F. et al., Non-invasive process tomography in chemical mixtures, A review, Sensors and Actuators B: Chemical, vol. 2, pp. 602-617 (2015).
• [8] Lei J., Liu S., Wang X., Liu Q., An Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Robust Principle Component Analysis, Sensors 13, pp. 2076-2092 (2013).
• [9] Lionheart W.R.B., Developments in EIT reconstruction algorithms: pitfalls, challenges and recent development, Review, Physiol. Meas, vol. 25, pp. 125-142 (2004).
• [10] Marashdeh Q., Warsito W., Fan L-S., Teixeira F.L., Nonlinear Forward Problem Solution for Electrical Capacitance Tomography Using Feed-Forward Neural Network, IEEE Sensors Journal, vol. 6, no. 2, pp. 441-449 (2006).
• [11] Kapusta P., Majchrowicz M., Sankowski D., Banasiak R., Application of GPU parallel computing for acceleration of Finite Element Method based 3D reconstruction algorithms in Electrical Capacitance Tomography, Image Processing & Communications, vol. 17, no. 4, pp. 343-350 (2012).
• [12] Mohamad-Saleh J., Hoyle B.S., Podd F.J.W., Spink D.M., Direct process estimation from tomographic data using artificial neural systems, Journal of Electronic Imaging, vol. 10, no. 3, pp. 646-652 (2001).
• [13] Romanowski A., Grudzień K., Williams R.A., Analysis and Interpretation of Hopper Behaviour Using ECT, Part Part. Syst. Charact, vol. 23, no. 3-4, pp. 297-305 (2006).
• [14] Wajman R., Fiderek P., Fidos H. et al., Metrological evaluation of a 3D electrical capacitance tomography measurement system for two-phase flow fraction determination, Meas. Sci. Technol. vol. 24, no. 065302 (2013).
• [15] 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, vol. 31, pp. 713-720 (2007).
• [16] Fiderek P., Wajman R., Kucharski J., The Fuzzy System for Recognition and Control of the two Phase Gas- Liquid Flows, Informatics Control Measurement in Economy and Environment Protection (IAPGOS), no. 4, pp. 7-11 (2015).
• [17] Ratajewicz-Mikolajczak E., Sikora J., Neural networks method for identification of the objects behind the screen, IEEE Trans Med Imaging, vol. 21, no. 6, pp. 613-9 (2002).
• [18] Garbaa H., Jackowska-Strumiłło L., Grudzień K., Romanowski A., Neural network approach to ECT inverse problem solving for estimation of gravitational solids flow, In Proc. of the 2014 Federated Conf. on Computer Science and Inf. Systems, AAIA’14, vol. 2, Warsaw, Poland, pp. 19-26 (2014).
• [19] Wang H., Yang W., Proctor I. et al., Online monitoring and flow regime identification of fluidised bed drying and granulation processes, IST 2009 International Workshop on Imaging Systems and Techniques, Shenzhen, China (2009).
• [20] Wang C., He X., Comparing Reconstruction Algorithms for Different Size of Object in Electrical Tomography, 11th IEEE International Conference on Electronic Measurement & Instruments, CEMI’2013, pp. 760-764 (2013).
• [21] Liukkonen M., Hiltunen Y., Laakso I., Advanced Monitoring and Diagnosis of Industrial Processes, 8th EUROSIM Congress on Modelling and Simulation (2013).
• [22] Haykin S., Neural Networks: a comprehensive foundation – 2nd ed., Prentice Hall (1999).
• [23] 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), pp. 942-947 (2003).
• [24] 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, Lviv-Polyana, UKRAINE, pp. 24-27 (2006).
• [25] Smolik W.T., Accelerated Levenberg Marquardt Method With an Optimal Step Length in Electrical Capacitance Tomography, IEEE Int. Conf. on Imaging Systems and Techniques, pp. 204-209 (2010)
• [26] ECTSim3D MATLAB’s Toolbox, http://ectsim.ire.pw.edu.pl, accessed on April 2014.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).