Abstract：Due to the high temperature in the middle of blast furnace， the central position sensor of the cross temperature measuring is very easy to be damaged， and the replacement period is always long， resulting in the gas flow distribution not being observed in time. To this end， two kinds of data:based intelligent modeling methods of multi-output support vector regression machine (M-SVR) and random vector functional-link networks (RVFLNs) were used to establish the temperature estimation model of cross temperature measuring center of blast furnace. Finally， the temperature estimation model based on industrial data was verified and compared. The results show that both M-SVR model and RVFLNs model have good temperature estimation effect when the sample size is small. However， when the sample size is large enough， the generalization performance and estimation accuracy of M-SVR model is better than those of the RVFLNs model.
Key words： blast furnace ironmaking cross temperature measuring temperature estimation multi-output support vector regression (M-SVR) random vector functional-link networks (RVFLNs)
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