规则与数据驱动的层流冷却过程带钢卷取温度模型

片锦香; 柴天佑; 李界家

doi:10.3724/SP.J.1004.2012.01861

规则与数据驱动的层流冷却过程带钢卷取温度模型

doi: 10.3724/SP.J.1004.2012.01861

1.
沈阳建筑大学信息与控制工程学院沈阳 110168;
2.
东北大学自动化研究中心沈阳 110189;
3.
流程工业综合自动化国家重点实验室沈阳 110189

详细信息

通讯作者:
片锦香

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- 被引次数: 0
出版历程
- 收稿日期: 2011-09-26
- 修回日期: 2012-06-08
- 刊出日期: 2012-11-20

Rule and Data Driven Strip Coiling Temperature Model in Laminar Cooling Process

1.
School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168;
2.
Research Center of Automation, Northeastern University, Shenyang 110189;
3.
State Key Laboratory of Integrated Automation for Process Industry, Shenyang 110189

摘要

摘要: 针对现有层流冷却过程带钢温度模型缺乏换热系数、带钢定位、带钢卷取温度计算的有效方法这一问题,提出了由冷却单元阀门开闭状态模型、带钢冷却单元定位模型、不同换热方式下的带钢温度模型组成的带钢卷取温度动态模型,将案例推理、规则推理、神经网络等相结合,提出了规则与数据驱动的模型参数智能辨识方法.采用某钢厂实际生产运行数据对所提出的带钢卷取温度动态模型进行了实验研究,实验结果表明本文提出的方法能够有效提高带钢卷取温度模型的精度.
- 层流冷却 /
- 参数辨识 /
- 规则驱动 /
- 数据驱动 /
- 卷取温度
Abstract: The existing cooling process models lack the methods to compute the heat transfer parameter and the position that strip reaches and cannot be used to compute the strip coiling temperature directly. So a strip coiling temperature model is proposed, which consists of the status of cooling unit valves calculating model, the strip segment tracking model, and the top surface temperature model under different heat transfer conditions. What is more, a rule and data driven hybrid intelligent identification algorithm is developed combining the case-based reasoning, rule-reasoning with the neural network. The tests using real industrial data of a steel plant have been conducted and indicated that the proposed strip coiling temperature model has made a great contribution to the prediction precision of the strip coiling temperature during the laminar cooling process.
- Laminar cooling /
- parameter identification /
- rule-driven /
- data-driven /
- coiling temperature

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参考文献(1)

[1]

Peng L G, Liu E Y, Zhang D H, Liu X H, Xu F. Development and application of advanced coiling temperature control system in hot strip mill. Advanced Materials Research, 2012, 421: 140-146[2] Zheng Y, Li S Y. Plant-wide temperature drop monitoring in run-out table strip cooling process. In: Proceedings of the 2011 International Symposium on Advanced Control of Industrial Processes. Hangzhou, China: IEEE, 2011. 287-292[3] Chai Tian-You, Wang Xiao-Bo. Application of RBF neural networks in control system of the slab accelerating cooling process. Acta Automatica Sinica, 2000, 26(2): 219-225(柴天佑, 王笑波. RBF神经网络在加速冷却控制系统中的应用. 自动化学报, 2000, 26(2): 219-225)[4] Peng L, Li Q, Zhou Z. Cooling hot rolling steel strip using combined tactics. Journal of University of Science and Technology Beijing, 2008, 15(3): 362-365[5] Dong Z K, Wang X, Wang X B, Li S Y, Zheng Y H. Application of weighted multiple models adaptive controller in the plate cooling process. Acta Automatica Sinica, 2010, 36(8): 1144-1150(董志坤, 王昕, 王笑波, 李少远, 郑益慧. 多模型加权自适应控制在中厚板层流冷却系统中的应用. 自动化学报, 2010, 36(8): 1144-1150)[6] Zheng Y, Li S Y, Wang X B. An approach to model building for accelerated cooling process using instance-based learning. Expert Systems with Applications, 2010, 37(7): 5364-5371[7] Guo R M. Modeling and simulation of run-out table cooling control using feedforward-feedback and element tracking system. IEEE Transactions on Industry Applications, 1997, 33(2): 304-311[8] Serajzadeh S. Modelling of temperature history and phase transformations during cooling of steel. Journal of Materials Processing Technology, 2004, 146(3): 311-317[9] Chai T Y, Tan M H, Chen X Y, Li H X. Intelligent optimization control for laminar cooling. In: Proceedings of the 15th IFAC World Congress. Barcelona, Spain: Elsevier Science Ltd., 2002. 181-186[10] Pian J X, Chai T Y, Wang H, Su C Y. Hybrid intelligent forecasting method of the laminar cooling process for hot strip. In: Proceedings of the 2007 American Control Conference. New York, USA: IEEE, 2007. 4866-4871[11] Xing G S, Ding J L, Chai T Y, Afshar P, Wang H. Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process. Engineering Applications of Artificial Intelligence, 2012, 25(2): 418-429[12] Wang Yong-Fu, Wang Dian-Hui, Chai Tian-You. Data mining and systems theory based fuzzy modeling and control compensation for friction. Acta Automatica Sinica, 2010, 36(3): 412-420(王永富, 王殿辉, 柴天佑. 基于数据挖掘与系统理论建立摩擦模糊模型与控制补偿. 自动化学报, 2010, 36(3): 412-420)[13] Ding F, Liu P X, Liu G J. Multiinnovation least-squares identification for system modeling. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40(3): 767-778[14] Carvalho E P, Martínez J, Martínez J M, Pisnitchenko F. On optimization strategies for parameter estimation in models governed by partial differential equations. Mathematics and Computers in Simulation, to be published[15] Pal S K, De P K, Basak J. Unsupervised feature evaluation: a neuro-fuzzy approach. IEEE Transactions on Neural Networks, 2000, 11(2): 366-376

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