Parametric modelling algorithms in electrical capacitance tomography for multiphase flow monitoring

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

doi:10.1109/MEMSTECH.2006.288675

Parametric modelling algorithms in electrical capacitance tomography for multiphase flow monitoring

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

^*Corresponding author for this work

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 2nd International Conference on Perspective Technologies and Methods in MEMS Design
Pages	100-106
Number of pages	7
DOIs	https://doi.org/10.1109/MEMSTECH.2006.288675
Publication status	Published - 2007
Event	2nd International Conference on Perspective Technologies and Methods in MEMS Design - Lviv, Ukraine Duration: 24 May 2006 → 27 May 2006

Conference

Conference	2nd International Conference on Perspective Technologies and Methods in MEMS Design
Abbreviated title	MEMSTECH 2006
Country/Territory	Ukraine
City	Lviv
Period	24/05/06 → 27/05/06

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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10.1109/MEMSTECH.2006.288675

Cite this

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title = "Parametric modelling algorithms in electrical capacitance tomography for multiphase flow monitoring",

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).",

keywords = "Advanced statistical algorithms, Electrical capacitance tomography, Granular flow, Inverse problem",

author = "K. Grudzie{\'n} and A. Romanowski and Aykroyd, {R. G.} and Williams, {Richard A} and V. Mosorov",

year = "2007",

doi = "10.1109/MEMSTECH.2006.288675",

language = "English",

isbn = "966553517X",

pages = "100--106",

booktitle = "Proceedings of the 2nd International Conference on Perspective Technologies and Methods in MEMS Design",

note = "2nd International Conference on Perspective Technologies and Methods in MEMS Design, MEMSTECH 2006 ; Conference date: 24-05-2006 Through 27-05-2006",

}

Grudzień, K, Romanowski, A, Aykroyd, RG, Williams, RA & 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., 4068439, pp. 100-106, 2nd International Conference on Perspective Technologies and Methods in MEMS Design, Lviv, Ukraine, 24/05/06. https://doi.org/10.1109/MEMSTECH.2006.288675

Parametric modelling algorithms in electrical capacitance tomography for multiphase flow monitoring. / Grudzień, K.; Romanowski, A.; Aykroyd, R. G. et al.
Proceedings of the 2nd International Conference on Perspective Technologies and Methods in MEMS Design. 2007. p. 100-106 4068439.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Parametric modelling algorithms in electrical capacitance tomography for multiphase flow monitoring

AU - Grudzień, K.

AU - Romanowski, A.

AU - Aykroyd, R. G.

AU - Williams, Richard A

AU - Mosorov, V.

PY - 2007

Y1 - 2007

N2 - 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).

AB - 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).

KW - Advanced statistical algorithms

KW - Electrical capacitance tomography

KW - Granular flow

KW - Inverse problem

UR - http://www.scopus.com/inward/record.url?scp=44049092086&partnerID=8YFLogxK

U2 - 10.1109/MEMSTECH.2006.288675

DO - 10.1109/MEMSTECH.2006.288675

M3 - Conference contribution

AN - SCOPUS:44049092086

SN - 966553517X

SN - 9789665535171

SP - 100

EP - 106

BT - Proceedings of the 2nd International Conference on Perspective Technologies and Methods in MEMS Design

T2 - 2nd International Conference on Perspective Technologies and Methods in MEMS Design

Y2 - 24 May 2006 through 27 May 2006

ER -

Parametric modelling algorithms in electrical capacitance tomography for multiphase flow monitoring

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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