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Online Steady State and Transient State Identification of Various Processes

Ghaderzadeh, Kanan | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 41938 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Bozorgmehry Bozarjomehry, Ramin
  7. Abstract:
  8. A statistically-based method has been developed for automated identification of steady state. The method is computationally inexpensive when compared to conventional techniques. The R-statistic is dimensionless and independent of the measurement level. Because it is a ratio of estimated variances, it is also independent of the process variance. Simulations show that for recommended critical values are also effectively independent of the magnitude and distribution of the noise.
    A fuzzy-logic-based methodology for on-line steady state and transient state identification is introduced. Although steady state identification is complicated by the presence of noise, fuzzy logic can help capture important features of on-line signals and classify incoming signals into transient state and steady state trend categories. The results indicate that the algorithm is capable of detecting transients accurately, identifying trends reliably, and not misinterpreting a steady state signal as a transient one.
    Autocorrelation usually increases both the average and variability of the probability density function of R. Either short-term or long-term autocorrelation can change the pdf(R) and can result in false “not at steady state” readings. Our solution to differentiate between long-term and short-term autocorrelation is to make the sampling intervals long enough such that the influence of short-term autocorrelation on the sampled data is negligible. Practically, the autocorrelations of representative steady state data could be calculated and the sampling interval selected to be long enough such that the autocorrelation between successive sampling data is zero within confidence limits
  9. Keywords:
  10. Statistical Methods ; Fuzzy Logic ; Genetic Algorithm ; Autocorrelation ; Steady State Detection ; Transient State Detection

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