The endpoint parameters are very important to the process of EAF steelmaking, but their online measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines (MSVM). In this model, the input space was divided by subtractive clustering and a submodel based on LSSVM was built in each subspace. To decrease the correlation among the submodels and to improve the accuracy and robustness of the model, the submodels were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.
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