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Prediction of Coke Quality with Multi-layer Neural Network
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Jian-wen Lu1, Pei-shan Lu2, Xin Jiang3, Li-jun Fu3, Xiao-zong Jia2
Rare Earth Steel | 2020, 46(1) : 14 - 17
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Rare Earth Steel | 2020, 46(1): 14-17
• Production Practice and Management •
Prediction of Coke Quality with Multi-layer Neural Network
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Jian-wen Lu1, Pei-shan Lu2, Xin Jiang3, Li-jun Fu3, Xiao-zong Jia2
Affiliations
  • 1.Information Service Center of Baotou Iron & Steel (Group) Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
  • 2.Coke Chemical Engineering Branch Co. of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
  • 3.Technical Center of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
Published: 2020-02-25 doi: 10.13647/j.cnki.btgkj.2020.01.004
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In this paper, it is proposed a method for constructing a prediction model of coke production quality with the neural network technique using the process and production data accumulated during the coke productions of Baotou Steel.

coke quality index  /  coke quality model  /  neural network
芦建文, 卢培山, 江鑫, 付利俊, 贾晓宗. 采用多层神经网络预测焦炭质量. 包钢科技, 2020 , 46 (1) : 14 -17 . DOI: 10.13647/j.cnki.btgkj.2020.01.004
Jian-wen Lu, Pei-shan Lu, Xin Jiang, Li-jun Fu, Xiao-zong Jia. Prediction of Coke Quality with Multi-layer Neural Network[J]. Science & Technology of Baotou Steel, 2020 , 46 (1) : 14 -17 . DOI: 10.13647/j.cnki.btgkj.2020.01.004
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Year 2020 volume 46 Issue 1
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Article Info
doi: 10.13647/j.cnki.btgkj.2020.01.004
  • Receive Date：2019-08-22
  • Online Date：2026-10-20
  • Published：2020-02-25
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  • Received：2019-08-22
Affiliations
    1.Information Service Center of Baotou Iron & Steel (Group) Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
    2.Coke Chemical Engineering Branch Co. of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
    3.Technical Center of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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