1 July 2001 Direct process estimation from tomographic data using artificial neural systems
Author Affiliations +
J. of Electronic Imaging, 10(3), (2001). doi:10.1117/1.1379570
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.
Junita Mohamad-Saleh, Brian S. Hoyle, Frank J. W. Podd, D. Mark Spink, "Direct process estimation from tomographic data using artificial neural systems," Journal of Electronic Imaging 10(3), (1 July 2001). http://dx.doi.org/10.1117/1.1379570



Direct methods


Error analysis


Neural networks

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