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Identification and Abnormal Condition Detection of a Cement Rotary Kiln

Sadeghian, Masoud | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 39829 (58)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Fatehi, Alireza
  7. Abstract:
  8. One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this thesis, we employed a nonlinear system identification method for identification, prediction and abnormal condition detection of a cement rotary kiln. After selecting proper inputs and outputs, an input-output model is identified for the plant. Because of the fact that the model of a kiln is very complicated, determining all of the parameters of the model including input channels delay and their dynamic order is not possible during the identification as it is too time consuming. Hence, we used a model-free method for estimating input channel delay and determining the dynamics of a nonlinear system. These methods are based on Lipschitz theorem. By means of that, the identification task gets easier and the results are more accurate. We presented nonlinear predictor and simulator models for a real cement rotary kiln by using nonlinear identification technique on the Locally Linear Neuro-Fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise fifteen minute horizon prediction for a cement rotary kiln is presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained three distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with fifteen minutes prediction horizon. The other two models are for the two faulty situations in the kiln with seven minutes prediction horizon. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used in this study.
  9. Keywords:
  10. Predictive Model ; Nonlinear System Identification ; Cement Rotary Kiln ; Locally Linear Neuro-Fuzzy Model ; simulator Models ; Delay Estimation Method

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