Parametric modelling algorithms in electrical capacitance tomography for multiphase flow monitoring

K. Grudzień, A. Romanowski, R. G. Aykroyd, Richard A Williams, V. Mosorov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Bayesian statistics is a powerful physical phenomena modelling tool. However it usually demands highly iterative algorithms, hence it is was not widely used so far. Recently, rapid development of computing capabilities enables use of such methods. Computing methodology here presented features Markov chain Monte Carlo (MCMC) methods applied to Bayesian modelling. The essential aspect is enabling direct characteristic parameters estimation, hence omitting the phase of image reconstruction widely produced whenever process tomography is applied. This property has an important feature of making feasible implementation of automatic industrial process control systems based on Electrical Capacitance Tomography (ECT).

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Perspective Technologies and Methods in MEMS Design
Pages100-106
Number of pages7
DOIs
Publication statusPublished - 2007
Event2nd International Conference on Perspective Technologies and Methods in MEMS Design - Lviv, Ukraine
Duration: 24 May 200627 May 2006

Conference

Conference2nd International Conference on Perspective Technologies and Methods in MEMS Design
Abbreviated titleMEMSTECH 2006
CountryUkraine
CityLviv
Period24/05/0627/05/06

Keywords

  • Advanced statistical algorithms
  • Electrical capacitance tomography
  • Granular flow
  • Inverse problem

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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  • Cite this

    Grudzień, K., Romanowski, A., Aykroyd, R. G., Williams, R. A., & Mosorov, V. (2007). Parametric modelling algorithms in electrical capacitance tomography for multiphase flow monitoring. In Proceedings of the 2nd International Conference on Perspective Technologies and Methods in MEMS Design (pp. 100-106). [4068439] https://doi.org/10.1109/MEMSTECH.2006.288675