کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
689932 | 889656 | 2009 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Abnormal condition detection in a cement rotary kiln with system identification methods
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
تکنولوژی و شیمی فرآیندی
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چکیده انگلیسی
In this paper, we use system identification methods for abnormal condition detection in a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant's normal conditions. A novel approach is used in order to estimate the delays of the input channels of the kiln before identification part. This method eases the identification since with determining the input channels delays, the dimension of search space in the identification part reduces. Afterward, to identify the kiln's model, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOcally LInear MOdel Tree (LOLIMOT) algorithm which is an incremental tree-structure algorithm. Finally, with the model for normal condition of the kiln, the incident of abnormalities in output are detected based on the length of duration and magnitude of difference between the real output and model's output. We distinguished three abnormal conditions in the kiln, two of which are known as common abnormal conditions and another one which was not characteristically known for cement experts either.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Process Control - Volume 19, Issue 9, October 2009, Pages 1538-1545
Journal: Journal of Process Control - Volume 19, Issue 9, October 2009, Pages 1538-1545
نویسندگان
Iman Makaremi, Alireza Fatehi, Babak Nadjar Araabi, Morteza Azizi, Ahmad Cheloeian,