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A novel modeling approach to predict the end-point phosphorus content in electric-arc furnace steel-making plant is proposed. The approach includes two procedures. Firstly, it detects the abnormal sample data point caused by disorder operating mode in the original training set with support vector domain description method and erases these abnormal samples; then, it reconstructs a new training set with these clean sample data. Secondly, the predictive model is obtained by using least square support vector machines and the new training set. Through the comparative experiments between the proposed approach in this paper and the direct modeling approach using least square support vector machines with the original training set, the results show that the proposed approach has superiority in the end-point phosphorus content predictive task in steel-making process.