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A Sliding‐window Smooth Support Vector Regression Model for Nonlinear Blast Furnace System
Author(s) -
Jian Ling,
Gao Chuanhou,
Xia Zunquan
Publication year - 2011
Publication title -
steel research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.603
H-Index - 49
eISSN - 1869-344X
pISSN - 1611-3683
DOI - 10.1002/srin.201000082
Subject(s) - blast furnace , context (archaeology) , sliding window protocol , silicon , thermal , thermal state , automation , computer science , predictive modelling , nonlinear system , engineering , materials science , mechanical engineering , window (computing) , metallurgy , machine learning , paleontology , physics , meteorology , biology , operating system , quantum mechanics
Blast furnace is one of the most complex industrial reactors and remains some unsolved puzzles, such as blast furnace automation, prediction of the inner thermal state, etc. In this work, a sliding‐window smooth support vector regression model is presented to address the issue of predicting the blast furnace inner thermal state, represented by the silicon content in blast furnace hot metal in the context. Different from the traditional numerical prediction models of silicon, the constructed SW‐SSVR model is devoted to predicting the changing trend of silicon and exhibits good performance with high percentage of successful trend prediction, competitive computational speed and timely online service. Additionally, some sharp fluctuation trend in the silicon test data can also be followed well by the SW‐SSVR model, which is always difficult for traditional data–driven based silicon prediction models. All of these indicate that the SW‐SSVR model is a good candidate to predict the change of blast furnace inner thermal state, and may provide a guide for operators to take proper action on operating blast furnace in advance.