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Hot metal desulphurization process is a complex and nonlinear process, final sulfur content in hot metal is difficult to measurement on-line. In order to predict the final sulfur content accurately, a model based on support vector machine (SVM) was proposed. The model cleared the outlier from modeling data through robust regression method and cleared the inconsistent data through rough set theory, and the quality of modeling data was improved. The model improved accuracy of the SVM model through immune evolutionary algorithm (IEA). Simulation results show that the model is high accuracy, and it can provide effective guidance for desulphurization production

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