Data-Driven Prediction of Sintering Burn-Through Point Based on Novel Genetic Programming
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 programm...
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| Veröffentlicht in: | Journal of iron and steel research, international international, 2010-12, Vol.17 (12), p.1-5 |
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| container_title | Journal of iron and steel research, international |
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| creator | SHANG, Xiu-qin LU, Jian-gang SUN, You-xian LIU, Jun YING, Yu-qian |
| description | 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. |
| doi_str_mv | 10.1016/S1006-706X(10)60188-4 |
| format | Article |
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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. 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Iron Steel Res. Int</addtitle><addtitle>Journal of Iron and Steel Research</addtitle><description>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. 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Iron Steel Res. Int</stitle><addtitle>Journal of Iron and Steel Research</addtitle><date>2010-12-01</date><risdate>2010</risdate><volume>17</volume><issue>12</issue><spage>1</spage><epage>5</epage><pages>1-5</pages><issn>1006-706X</issn><eissn>2210-3988</eissn><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. 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| source | SpringerNature Journals; Access via ScienceDirect (Elsevier); Alma/SFX Local Collection |
| subjects | Applied and Technical Physics burn-through point Clustering Empirical analysis Engineering genetic programming Genetics Iron and steel industry K-means clustering Machines Manufacturing Materials Engineering Materials Science Mathematical models Metallic Materials Physical Chemistry Pressure drop Processes Programming Sintering 均值聚类 数据驱动 最小二乘法 烧结过程 遗传程序设计 |
| title | Data-Driven Prediction of Sintering Burn-Through Point Based on Novel Genetic Programming |
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