NDLI: Interface detection in pipe separators Using ECT: Performance with reduced number of sensing electrodes

Content Provider	IEEE Xplore Digital Library
Author	Pradeep, C. Yan Ru Mylvaganam, S.
Copyright Year	2011
Description	Author affiliation: Faculty of technology, Department of Electrical Engineering, Information technology and cybernetics, Telemark University College, Porsgrunn, Norway (Pradeep, C.; Yan Ru; Mylvaganam, S.)
Abstract	Pipe separators are currently being assessed as substitutes for conventional separators in the oil and gas industry for the separation of gas, oil and water. In the process of separation, the interface levels between the different media are important measurands to be monitored to optimize the separation process. Electrical Capacitance Tomography (ECT) without too much focus on tomograms can be used to detect the interfaces in a separator with enough accuracy for control purposes. With the easing of the CPU time needed for image processing, the possibility of getting enough information from reduced number of electrodes has also to be looked into, in view of reducing the processing time. The performance of the ECT system with reduced number of electrodes is studied in this paper using inferential methods based on artificial neural networks (ANN). Performance of a 12 electrode ECT system is assessed by studying its performance with only 6 and 4 electrodes. The detection/estimation of interfaces is done effectively and in much shorter time compared to the processing of data with tomograms using a 12 electrode system. The inferential method can handle non-linearity and results from it can be easily integrated into other control algorithms addressing the actuators used in separators.
Starting Page	221
Ending Page	226
File Size	1129604
Page Count	6
File Format	PDF
ISBN	9781424480630
e-ISBN	9781424480647
DOI	10.1109/SAS.2011.5739795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-02-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes ANN Particle separators Artificial neural networks ECT soft sensors Capacitance pipe separators Capacitive sensors Capacitance measurement interface detection inferential methods
Content Type	Text
Resource Type	Article

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