基于影子趋势对比的矿热炉炉况在线辨识及趋势预测

李沛; 阳春华; 贺建军; 桂卫华

doi:10.16383/j.aas.c190827

基于影子趋势对比的矿热炉炉况在线辨识及趋势预测

doi: 10.16383/j.aas.c190827

1.
中南大学自动化学院长沙 410083

基金项目: 国家自然科学基金委基础科学中心项目(61988101), 国家自然科学基金国际(地区)合作与交流项目(61860206014)

详细信息

作者简介:
李沛：中南大学自动化学院博士研究生. 主要研究方向为复杂工业过程建模与控制.E-mail: csulipei@163.com

阳春华：中南大学自动化学院教授. 国家杰出青年基金获得者. 2002年获得中南大学博士学位. 主要研究方向为复杂工业过程建模与优化控制, 智能自动化控制系统. 本文通信作者.E-mail: ychh@csu.edu.cn

贺建军：中南大学自动化学院教授. 2004年获得中南大学博士学位. 主要研究方向为复杂工业过程建模与优化控制. E-mail: jjhe@csu.edu.cn

桂卫华：中国工程院院士, 中南大学自动化学院教授. 1981年获得中南矿冶学院硕士学位. 主要研究方向为复杂工业过程建模与优化控制, 工业大系统控制理论与应用.E-mail: gwh@csu.edu.cn

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出版历程
- 收稿日期: 2019-12-05
- 修回日期: 2020-02-16
- 网络出版日期: 2021-06-10
- 刊出日期: 2021-06-10

Smelting Condition Identification and Prediction for Submerged Arc Furnace Based on Shadow-trend-comparison

1.
School of Automation, Central South University, Changsha 410083

Funds: Supported by the the National Natural Science Foundation of China through the Basic Science Center Program (61988101), the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (61860206014)

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Author Bio:
LI Pei　Ph.D. candidate at the School of Automation, Central South University. His research interest covers modeling and control of complex industrial process

YANG Chun-Hua　Professor at the School of Automation, Central South University. She is also a winner of National Science Fund for Distinguished Young Scholars. She received her Ph. D. degree from Central South University in 2002. Her research interest covers modeling and optimal control of complex industrial process, intelligent automation control system. Corresponding author of this paper

HE Jian-Jun　Professor at the School of Automation, Central South University. He received his Ph. D. degree from Central South University in 2004. His research interest covers modeling and optimal control of complex industrial process

GUI Wei-Hua　Academician of the Chinese Academy of Engineering, and professor at the School of Automation, Central South University. He received his master degree from Central South Institute of Mining and Metallurgy in 1981. His research interest covers modeling and optimal control of complex industrial process and industrial large system control theory and application

摘要

摘要: 矿热炉埋弧冶炼炉况影响因素复杂且偶发迁移和跃变, 炉况发展趋势难以把握, 给冶炼过程控制带来挑战. 对此, 本文在深入分析埋弧冶炼机理的基础上, 建立了可表征反应区内电弧热与电阻热交互耦合关系的反应区操作电阻模型, 实现炉况发展趋势的在线跟踪. 当炉况发生迁移或跃变时, 利用前序炉况下所得模型生成影子趋势信息, 并综合考虑冶炼工艺及电弧电阻与料层电阻的动态特性差异, 辨析炉况变化的成因, 形成规则化的待辨识参数在线选取方法, 解决了炉况变化前后采样点少, 传统辨识方法无法适用的问题. 工业现场验证表明, 所提出方法可在复杂条件下对冶炼炉况进行准确跟踪, 并给出可靠的炉况发展趋势预测, 为冶炼过程的精细化生产奠定了基础.
- 埋弧冶炼 /
- 操作电阻 /
- 炉况预测 /
- 影子趋势
Abstract: Submerged arc smelting is an intermittent discharge process with complex influencing factors and the smelting condition would fluctuate in some cases, which makes the control of the smelting process extremely difficult. This paper proposed an operation resistance model that can well characterize the interactive relationship between arc resistance and burden resistance within reacting zones, which can help to track the trend of smelting condition. When smelting condition travels out of the predetermined trajectory, the model can also provide with shadow-trend of the smelting condition by predicting system dynamics under current control outputs, using parameters identified in pre-ordered smelting process. A selection rule of parameters to be re-identified is then proposed based on comparison between shadow-trend and measured data, guided by smelting mechanism. Thus it is practical to predict the trend of smelting condition with few measured data after changing point. Industrial data verification shows, that the proposed method can track and predict the trend of smelting condition well, which could definitely help to achieve the precise control of smelting process.
- Submerged arc smelting /
- operation resistance /
- smelting condition prediction /
- shadow-trend

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图 1 矿热炉简要构造

Fig. 1 Main structure of submerged arc furnace

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图 2 矿热炉内电弧及料层电阻分布示意

Fig. 2 Distribution of arc resistance and burden resistance

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图 3 矿热炉反应区结构

Fig. 3 Structure of melting pool

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图 4 炉料层电阻参数示意图

Fig. 4 Parameters related to burden resistance

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图 5 料层电阻率与炉盖温度关系

Fig. 5 Relationship of burden resistivity and temperature

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图 6 电阻率−热耦合操作电阻模型建模结果

Fig. 6 Modeling results of operation resistance model based on resistivity-thermal coupling

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图 7 电阻率−热耦合操作电阻模型建模结果(细节)

Fig. 7 Modeling results of operation resistance model based on resistivity-thermal coupling (in detail)

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图 8 电阻率−热耦合操作电阻模型与极限学习机效果对比

Fig. 8 Comparison between proposed model and extreme learning machine

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图 9 电阻率−热耦合操作电阻模型在炉况变化情况下的离线预测结果1

Fig. 9 Off-line prediction results of proposed model under changing smelting condition: Case 1

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图 10 电阻率−热耦合操作电阻模型在炉况变化情况下的离线预测结果1 (细节)

Fig. 10 Off-line prediction results of proposed model under changing smelting condition: Case 1 (in detail)

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图 11 电极升降操作对熔池的随机影响

Fig. 11 Random effect of electrode positioning on melting pool

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图 12 待辨识参数选取的规则二叉树

Fig. 12 Rules for selecting parameters that to be identified

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图 13 基于影子趋势对比的炉况在线辨识及预测结果1

Fig. 13 Results of on-line smelting condition identification and prediction based on shadow-trend-comparison: Case 1

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图 14 基于影子趋势对比的炉况在线辨识及预测结果1 (细节)

Fig. 14 Results of on-line smelting condition identification and prediction based on shadow-trend-comparison: Case 1 (in detail)

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图 15 案例1所适用的参数选取规则

Fig. 15 Rules for parameters selection that used in Case 1

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图 16 电阻率−热耦合操作电阻模型在炉况变化情况下的离线预测结果2

Fig. 16 Off-line prediction results of proposed model under changing smelting condition: Case 2

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图 17 案例2所适用的参数选取规则

Fig. 17 Rules for parameters selection that used in Case 2

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图 18 基于影子趋势对比的炉况在线辨识及预测结果2 (细节)

Fig. 18 Results of on-line smelting condition identification and prediction based on shadow-trend-comparison: Case 2 (in detail)

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图 19 基于影子趋势对比的炉况在线辨识及预测结果2

Fig. 19 Results of on-line smelting condition identification and prediction based on shadow-trend-comparison: Case 2

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表 1 B相反应区4月13日第一炉炉况变化前后模型参数情况

Table 1 Parameters of phase B in the first smelting cycle on 13 April

k₅ a₀ mc

变化前 1.88×10³ 2.31×10³ 2.86×10⁶
变化后 1.75×10³ 4.98×10³ 3.24×10⁶
幅值 −7.29 % 115.60 % 13.41 %

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