Modeling, Identification and Control at Telemark University College

Lie, Bernt; Ruscio, David Di; Ergon, Rolf; Glemmestad, Bjørn; Halstensen, Maths; Haugen, Finn; Mylvaganam, Saba; Skeie, Nils-Olav; Winkler, Dietmar

doi:10.4173/mic.2009.3.4

MIC

2009

DOI: 10.4173/mic.2009.3.4

|View full text |Cite

Modeling, Identification and Control at Telemark University College

Bernt Lie¹,

David Di Ruscio²,

Rolf Ergon³

et al.

Abstract: Master studies in process automation started in 1989 at what soon became Telemark University College, and the 20 year anniversary marks the start of our own PhD degree in Process, Energy and Automation Engineering. The paper gives an overview of research activities related to control engineering at

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2011

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 63 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Neural networks in electrical capacitance tomography (ECT)-based interface detection

Ren¹,

Pradeep²,

Mylvaganam³

2011

Meas. Sci. Technol.

View full text Add to dashboard Cite

Tomographic systems have been tested in separators for estimating the distribution of the three phases (including emulsion and foam) via interface detection in various laboratories. Experiments and simulations show that electrical capacitance tomography (ECT) is capable of detecting the interface accurately in spite of the high conductivity of the water present. Due to the high conductivity of the water/brine in the pipe separator, the sensitivity of the capacitance measurements is to a certain extent immune to variations in material properties. In a series of preliminary tests, capacitance tomography was used to estimate the interface in a pipe separator containing oil and water/brine. Results obtained from laboratory scale models are presented and discussed with some information on the uncertainties involved. Artificial neural networks exhibit enhanced ability to mask variations in unwanted/unimportant parameters in the separation process, thus reducing the complexities involved in the solution of the essentially underdetermined system of equations evolving out of different models developed for the system. Due to ample data being available from tomographic systems, a data driven soft sensor (virtual sensor) approach is also discussed with some considerations on processing times to address the potential of the ECT in real time measurement and control applications.

show abstract

Neural networks in electrical capacitance tomography (ECT)-based interface detection

Ren¹,

Pradeep²,

Mylvaganam³

2011

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Modeling, Identification and Control at Telemark University College

Cited by 1 publication

References 63 publications

Neural networks in electrical capacitance tomography (ECT)-based interface detection

Neural networks in electrical capacitance tomography (ECT)-based interface detection

Contact Info

Product

Resources

About