Direct process estimation from tomographic data using artificial neural systems

Junita Mohamad-Saleh; Brian S. Hoyle; Frank J. W. Podd; D. Mark Spink

doi:10.1117/1.1379570

1 July 2001 Direct process estimation from tomographic data using artificial neural systems

Junita Mohamad-Saleh, Brian S. Hoyle, Frank J. W. Podd, D. Mark Spink

Journal of Electronic Imaging, Vol. 10, Issue 3, (July 2001). https://doi.org/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.

©(2001) Society of Photo-Optical Instrumentation Engineers (SPIE)

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

Published: 1 July 2001

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