期刊
ENGINEERING OPTIMIZATION
卷 52, 期 9, 页码 1612-1631出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/0305215X.2019.1665037
关键词
Temperature prediction; kernel-based learning algorithm; SVR; SDP; reheating furnace
资金
- National Key Research and Development Program of China [2016YFB0901900]
- Major Program of the National Natural Science Foundation of China [71790614]
- Fund for the National Natural Science Foundation of China [61573086]
- Fund for Innovative Research Groups of the National Natural Science Foundation of China [71621061]
- 111 Project [B16009]
- Major International Joint Research Project [71520107004]
Temperature prediction in a slab heating furnace is an important problem for steel production processes. However, the problem is complicated owing to process complexity and industrial noise. These factors make it difficult to obtain precise prediction results by a mechanism model in practical production. In this article, an integrated modelling approach is proposed through combining mechanism and data-driven models. This method constructs a generalized-kernel support vector regression (SVR) on new search space to improve the predictive performance of a mechanism model, in which the kernel matrix is a combination of multiple single kernels. The learning problem can be solved globally by a semi-definite programming (SDP) problem. Numerical experiments using actual data from an iron and steel enterprise in China are performed to illustrate the effectiveness of the proposed integrated method.
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