Abstract
An empirical dynamic model of burn-through point (BTP) in sintering process was developed. The K-means clustering was used to feed distribution according to the cold bed permeability, which was estimated by the superficial gas velocity in the cold stage. For each clustering, a novel genetic programming (NGP) was proposed to construct the empirical model of the waste gas temperature and the bed pressure drop in the sintering stage. The least square method (LSM) and M-estimator were adopted in NGP to improve the ability to compute and resist disturbance. Simulation results show the superiority of the proposed method.
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Foundation Item: Item Sponsored by National Natural Science Foundation of China (60736021, 21076179); National High-Technologies Research and Development Program of China (863 Program) (2006AA04Z184, 2007AA041406); Key Technologies Research and Development Program of Zhejiang Province of China (2006C11066, 2006C31051); Natural Science Foundation of Zhejiang Province of China (Y4080339)
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Shang, Xq., Lu, Jg., Sun, Yx. et al. Data-driven prediction of sintering burn-through point based on novel genetic programming. J. Iron Steel Res. Int. 17, 1–5 (2010). https://doi.org/10.1016/S1006-706X(10)60188-4
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DOI: https://doi.org/10.1016/S1006-706X(10)60188-4