Share Email Print

Journal of Electronic Imaging

Direct process estimation from tomographic data using artificial neural systems
Author(s): Junita Mohamad-Saleh; Brian S. Hoyle; Frank J. W. Podd; D. Mark Spink
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a wellresearched sensing technique for this task, due to its low-cost, nonintrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas–oil, gas–water, and gas–oil–water flows from ECT measurements. A two-dimensional finite-element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.

Paper Details

Date Published: 1 July 2001
PDF: 7 pages
J. Electron. Imag. 10(3) doi: 10.1117/1.1379570
Published in: Journal of Electronic Imaging Volume 10, Issue 3
Show Author Affiliations
Junita Mohamad-Saleh, Univ. of Leeds (United Kingdom)
Brian S. Hoyle, Univ. of Leeds (United Kingdom)
Frank J. W. Podd, Univ. of Leeds (United Kingdom)
D. Mark Spink, Univ. of Leeds (United Kingdom)

© SPIE. Terms of Use
Back to Top