Data-Driven Prediction of Sintering Burn-Through Point Based on Novel Genetic Programming

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@Article{Shang20101,

• author = "Xiu-qin Shang and Jian-gang Lu and You-xian Sun and Jun Liu and Yu-qian Ying",
• title = "Data-Driven Prediction of Sintering Burn-Through Point Based on Novel Genetic Programming",
• journal = "Journal of Iron and Steel Research, International",
• volume = "17",
• number = "12",
• pages = "1--5",
• year = "2010",
• ISSN = "1006-706X",
• DOI = "doi:10.1016/S1006-706X(10)60188-4",
• URL = "http://www.sciencedirect.com/science/article/B82XP-51VB3C8-1/2/1de00900cc3d2e7a67b60aa929329773",
• keywords = "genetic algorithms, genetic programming, burn-through point, K-means clustering",
• 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.",

}

Genetic Programming entries for Xiu-qin Shang Jiangang Lu Youxian Sun Jun Liu Yu-qian Ying

Citations