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电熔镁砂产品单吨能耗混合预报模型

吴志伟 柴天佑 吴永建

吴志伟, 柴天佑, 吴永建. 电熔镁砂产品单吨能耗混合预报模型. 自动化学报, 2013, 39(12): 2002-2011. doi: 10.3724/SP.J.1004.2013.02002
引用本文: 吴志伟, 柴天佑, 吴永建. 电熔镁砂产品单吨能耗混合预报模型. 自动化学报, 2013, 39(12): 2002-2011. doi: 10.3724/SP.J.1004.2013.02002
WU Zhi-Wei, CHAI Tian-You, WU Yong-Jian. A Hybrid Prediction Model of Energy Consumption Per Ton for Fused Magnesia. ACTA AUTOMATICA SINICA, 2013, 39(12): 2002-2011. doi: 10.3724/SP.J.1004.2013.02002
Citation: WU Zhi-Wei, CHAI Tian-You, WU Yong-Jian. A Hybrid Prediction Model of Energy Consumption Per Ton for Fused Magnesia. ACTA AUTOMATICA SINICA, 2013, 39(12): 2002-2011. doi: 10.3724/SP.J.1004.2013.02002

电熔镁砂产品单吨能耗混合预报模型

doi: 10.3724/SP.J.1004.2013.02002
基金项目: 

国家自然科学基金(61020106003,61004009),国家重点基础研究发展计划 (973项目)(2009CB320601)资助

详细信息
    作者简介:

    柴天佑 中国工程院院士,东北大学教授. 1985 年于东北大学获得博士学位.主要研究方向为自适应控制,智能解耦控制,流程工业综合自动化理论、方法与技术. E-mail:tychai@mail.neu.edu.cn

A Hybrid Prediction Model of Energy Consumption Per Ton for Fused Magnesia

Funds: 

Supported by National Natural Science Foundation of China (61020106003, 61004009) and National Basic Research Program of China (973Program) (2009CB320601)

  • 摘要: 产品的单吨能耗是反映电熔镁砂熔炼过程产品产量和能耗的综合生产指标. 通过分析炉内电热转换关系,利用能量守恒原理建立了产品单吨能耗模型. 针对模型的未知非线性和参数时变等综合复杂性提出了由基于机理分析的单吨能耗主模型和 基于神经网络的补偿模型组成的产品单吨能耗混合预报模型. 其中神经网络补偿模型用于补偿模型的未知非线性和参数不确定性对于预报模型准确性的影响. 采用某电熔镁砂熔炼过程实测数据验证了所建立的混合预报模型是有效的.
  • [1] Zhang X, Xue D F, Xu D L, Feng X Q, Wang J Y. Growth of large MgO single crystals by an arc-fusion method. Journal of Crystal Growth, 2005, 280(1-2): 234-238
    [2] Zhang X, Xue D F, Wang J Y, Feng X Q. Improved growth technology of large MgO single crystals. Journal of Crystal Growth, 2006, 292(2): 505-509
    [3] Guo Mao-Xian. Industry Furnace. Beijing: Metallurgical Industry Press, 2002 (郭茂先. 工业电炉. 北京: 冶金工业出版社, 2002)
    [4] Tong Yong-Juan, Zhang Wei-Jun, Li Peng, Wang Shuai, Zhao Wen-Jing, Yu Hong-Chao. Thermal process analysis and energy conservation of fused magnesia production. Energy for Metallurgical Industry, 2011, 30(3): 28-30(仝永娟, 张卫军, 李鹏, 王帅, 赵文婧, 于宏超. 电熔镁砂生产的热工过程分析与节能. 冶金能源, 2011, 30(3): 28-30)
    [5] Qi Guo-Chao, Zhang Wei-Jun, Tong Yong-Juan, Li Jun, Cui Jun-Feng. Geometry size optimizing of fused magnesia arc furnace. Energy for Metallurgical Industry, 2010, 29(4): 34-36, 47(齐国超, 张卫军, 仝永娟, 李军, 崔俊峰. 电熔镁电弧炉炉体优化设计. 冶金能源, 2010, 29(4): 34-36, 47)
    [6] Wang Yan, Mao Zhi-Zhong, Li Yan, Tian Hui-Xin, Shi Ai-Ping. Study on modeling and coupling simulation of power supply system for AC electric arc furnace. Journal of System Simulation, 2010, 22(4): 841-844(王琰, 毛志忠, 李妍, 田慧欣, 石爱平. 交流电弧炉供电系统建模及耦合关系仿真研究. 系统仿真学报, 2010, 22(4): 841-844)
    [7] Wang Y, Mao Z Z, Li Y, Tian H X, Feng L F. Modeling and parameter identification of an electric arc for the arc furnace. In: Proceedings of the 2008 IEEE International Conference on Automation and Logistics. Qingdao, China: IEEE, 2008. 740-743
    [8] Zheng T X, Makram E B. An adaptive arc furnace model. IEEE Transactions on Power Delivery, 2000, 15(3): 931-939
    [9] Makram E B, Zheng T, Girgis A A. Effect of different arc furnace models on voltage distortion. In: Proceedings of the 8th International Conference on Harmonics and Quality of Power. Athens: IEEE, 1998, 2: 1079-1085
    [10] Tseng K J, Wang Y M, Vilathgamuwa D M. An experimentally verified hybrid Cassie-Mayr electric arc model for power electronics simulations. IEEE Transactions on Power Electronics, 1997, 12(3): 429-436
    [11] Song Q. On the weight convergence of Elman networks. IEEE Transactions on Neural Networks, 2010, 21(3): 463-480
    [12] Liou C Y, Huang J C, Yang W C. Modeling word perception using the Elman network. Neurocomputing, 2008, 71(16-18): 3150-3157
    [13] Guo C Y, Song Q, Cai W J. A neural network assisted caSCAde control system for air handling unit. IEEE Transactions on Industrial Electronics, 2007, 54(1): 620-628
    [14] Shi Xiao-Hu. Some Theoretical Studies of Elman Neural Networks and Evolutionary Algorithms and Their Applications[Ph.D. dissertation], Jilin University, China, 2006(时小虎. Elman神经网络与进化算法的若干理论研究及应用[博士学位论文], 吉林大学, 中国, 2006)
    [15] Shi Xiao-Hu, Liang Yan-Chun, Xu Xu. An improved Elman model and recurrent back-propagation control neural networks. Journal of Software, 2003, 14(6): 1110-1119(时小虎, 梁艳春, 徐旭. 改进的Elman模型与递归反传控制神经网络. 软件学报, 2003, 14(6): 1110-1119)
    [16] Li Jian-Chuan,  Qin Guo-Jun,  Wen Xi-Sen,  Hu Niao-Qing. Over-fitting in neural network learning algorithms and its solving strategies. Journal of Vibration Measurement and Diagnosis, 2002, 22(4): 260-264(李俭船, 秦国军, 温熙森, 胡茑庆. 神经网络学习算法的过拟合问题及解决办法. 振动、测试与诊断, 2002, 22(4): 260-264)
    [17] Shi Fu, Wang Peng, Sun Zhen-Bin. Control and Operating of Submerged Arc Furnace. Beijing: Metallurgical Industry Press, 2010 (石富, 王鹏, 孙振斌. 矿热炉控制与操作. 北京: 冶金工业出版社, 2010)
    [18] Wu Z W, Chai T Y, Fu J, Sun J. Hybrid intelligent optimal control of fused magnesium furnaces. In: Proceedings of the 49th IEEE Conference on Decision and Control. Atlanta, GA: IEEE, 2010. 3313-3318
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出版历程
  • 收稿日期:  2012-05-14
  • 修回日期:  2012-10-10
  • 刊出日期:  2013-12-20

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