Neural Network Based Montioring and Control of Fluidized Bed.

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The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. ... continued below

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114 p.; Other: FDE: PDF; PL:

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Bodruzzaman, M. & Essawy, M.A. April 1, 1996.

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Description

The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to the experimental system. In this report, the hardware implementation issues of the control algorithms are also discussed.

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114 p.; Other: FDE: PDF; PL:

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OSTI as DE98051814

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  • Other Information: PBD: Apr 1996

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  • Other: DE98051814
  • Report No.: DOE/MT/94015--T8
  • Grant Number: FG22-94MT94015
  • DOI: 10.2172/643552 | External Link
  • Office of Scientific & Technical Information Report Number: 643552
  • Archival Resource Key: ark:/67531/metadc692808

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  • April 1, 1996

Added to The UNT Digital Library

  • Aug. 14, 2015, 8:43 a.m.

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  • Nov. 10, 2015, 8:14 p.m.

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Bodruzzaman, M. & Essawy, M.A. Neural Network Based Montioring and Control of Fluidized Bed., report, April 1, 1996; United States. (digital.library.unt.edu/ark:/67531/metadc692808/: accessed October 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.