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

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

1.
东北大学流程工业国家重点实验室沈阳 110819;
2.
东北大学自动化研究中心沈阳 110819

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

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- 被引次数: 0
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

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

1.
State Key Laboratory of Integrated Automation for Process Industries, Northeastern University, Shenyang 110819;
2.
Research Center of Automation, Northeastern University, Shenyang 110819

Funds:

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

摘要

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.
- 电熔镁砂 /
- 单吨能耗 /
- 预报模型 /
- 能量守恒 /
- 神经网络
Abstract: Energy consumption per ton is a main index of product quality and energy consumption in the smelting process of fused magnesia. By analyzing the electric heating conversion principle, an energy consumption per ton model of product is proposed by using the law of conservation of energy. For the complex characteristics such as unknown nonlinear and time-varying parameters of the energy consumption per ton model, a hybrid prediction model of energy consumption per ton which consists of a main model based on mechanism analysis and a compensation mode based on neural network is also proposed. The compensation model compensates the modeling errors caused by unknown nonlinearity and uncertainty of some parameters for the mathematical model. Industrial applications show the usefulness and effectiveness of the proposed hybrid prediction model for energy consumption per ton.
- Fused magnesia /
- energy consumption per ton /
- prediction model /
- conservation of energy /
- neural network

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参考文献(18)

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