Knowledge-based prediction in process control systems under limited measurement data | Zendy

Natalia Bakhtadze | Zendy; Ekaterina Sakrutina | Zendy; B. V. Pavlov | Zendy; Vladimir Lototsky | Zendy; Oleg Zaikin | Zendy

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Knowledge-based prediction in process control systems under limited measurement data

Author(s) -

Natalia Bakhtadze,

Ekaterina Sakrutina,

B. V. Pavlov,

Vladimir Lototsky,

Oleg Zaikin

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.08.044

Subject(s) - computer science , process (computing) , data mining , identification (biology) , quality (philosophy) , product (mathematics) , wavelet , associative property , process control , machine learning , artificial intelligence , philosophy , botany , geometry , mathematics , epistemology , pure mathematics , biology , operating system

The paper offers identification algorithms enabling process parameters estimation under limited output samples and time-varying inputs. The algorithms are based on the associative search of analogs and wavelet-analysis of signals. The effectiveness of the techniques proposed is demonstrated with the example of product quality prediction in oil refining industry.

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