Knowledge-Based Control and Optimization of Blast Furnace Gas System in Steel Industry | Zendy

Haixia Wang | Zendy; Chunyang Sheng | Zendy; Xiao Lu | Zendy

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Knowledge-Based Control and Optimization of Blast Furnace Gas System in Steel Industry

Author(s) -

Haixia Wang,

Chunyang Sheng,

Xiao Lu

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2763630

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Aiming at the control and optimization problem of blast furnace gas (BFG) systems in the steel industry, a knowledge-based optimal control algorithm combining fuzzy rules extraction with neural networks (NNs) ensemble-based prediction is proposed. On one hand, a fuzzy model is designed to extract the expert control knowledge from the historical data of the industrial process after community detection, and then, a great deal of scheduling knowledge is employed to compose a fuzzy rule base, which can be used for fuzzy inference of control scheme with a new input. On the other hand, data-driven NNs ensemble is built to model the BFG system for prediction. Meanwhile, the prediction results can provide the inputs when using fuzzy rule base for control and optimization. Finally, a BFG system of one steel enterprise is studied in this paper for experiments, which verifies the effectiveness and practicability of the proposed method.

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